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Editors: Maria Florina Balcan, Kilian Q. Weinberger
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No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing
; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1-10
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
Nihar Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin Wainwright; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:11-20
A Deep Learning Approach to Unsupervised Ensemble Learning
Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:30-39
Revisiting Semi-Supervised Learning with Graph Embeddings
Zhilin Yang, William Cohen, Ruslan Salakhudinov; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:40-48
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Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Chelsea Finn, Sergey Levine, Pieter Abbeel; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:49-58
Diversity-Promoting Bayesian Learning of Latent Variable Models
Pengtao Xie, Jun Zhu, Eric Xing; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:59-68
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
Kirthevasan Kandasamy, Yaoliang Yu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:69-78
Hawkes Processes with Stochastic Excitations
Young Lee, Kar Wai Lim, Cheng Soon Ong; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:79-88
Data-driven Rank Breaking for Efficient Rank Aggregation
Ashish Khetan, Sewoong Oh; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:89-98
Dropout distillation
Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:99-107
Metadata-conscious anonymous messaging
Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:108-116
The Teaching Dimension of Linear Learners
Ji Liu, Xiaojin Zhu, Hrag Ohannessian; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:117-126
Truthful Univariate Estimators
Ioannis Caragiannis, Ariel Procaccia, Nisarg Shah; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:127-135
Why Regularized Auto-Encoders learn Sparse Representation?
Devansh Arpit, Yingbo Zhou, Hung Ngo, Venu Govindaraju; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:136-144
k-variates++: more pluses in the k-means++
Richard Nock, Raphael Canyasse, Roksana Boreli, Frank Nielsen; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:145-154
Multi-Player Bandits – a Musical Chairs Approach
Jonathan Rosenski, Ohad Shamir, Liran Szlak; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:155-163
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, Jie Chen, Jingdong Chen, Zhijie Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Ke Ding, Niandong Du, Erich Elsen, Jesse Engel, Weiwei Fang, Linxi Fan, Christopher Fougner, Liang Gao, Caixia Gong, Awni Hannun, Tony Han, Lappi Johannes, Bing Jiang, Cai Ju, Billy Jun, Patrick LeGresley, Libby Lin, Junjie Liu, Yang Liu, Weigao Li, Xiangang Li, Dongpeng Ma, Sharan Narang, Andrew Ng, Sherjil Ozair, Yiping Peng, Ryan Prenger, Sheng Qian, Zongfeng Quan, Jonathan Raiman, Vinay Rao, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Kavya Srinet, Anuroop Sriram, Haiyuan Tang, Liliang Tang, Chong Wang, Jidong Wang, Kaifu Wang, Yi Wang, Zhijian Wang, Zhiqian Wang, Shuang Wu, Likai Wei, Bo Xiao, Wen Xie, Yan Xie, Dani Yogatama, Bin Yuan, Jun Zhan, Zhenyao Zhu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:173-182
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On the Consistency of Feature Selection With Lasso for Non-linear Targets
Yue Zhang, Weihong Guo, Soumya Ray; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:183-191
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Minimum Regret Search for Single- and Multi-Task Optimization
Jan Hendrik Metzen; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:192-200
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:201-210
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The Variational Nystrom method for large-scale spectral problems
Max Vladymyrov, Miguel Carreira-Perpinan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:211-220
Multi-Bias Non-linear Activation in Deep Neural Networks
Hongyang Li, Wanli Ouyang, Xiaogang Wang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:221-229
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Asymmetric Multi-task Learning Based on Task Relatedness and Loss
Giwoong Lee, Eunho Yang, Sung Hwang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:230-238
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Accurate Robust and Efficient Error Estimation for Decision Trees
Lixin Fan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:239-247
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Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
Ohad Shamir; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:248-256
Convergence of Stochastic Gradient Descent for PCA
Ohad Shamir; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:257-265
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data
Andrew Lan, Tom Goldstein, Richard Baraniuk, Christoph Studer; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:266-275
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A Kernelized Stein Discrepancy for Goodness-of-fit Tests
Qiang Liu, Jason Lee, Michael Jordan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:276-284
Variable Elimination in the Fourier Domain
Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla, Bart Selman; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:285-294
Low-Rank Matrix Approximation with Stability
Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen Chu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:295-303
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Linking losses for density ratio and class-probability estimation
Aditya Menon, Cheng Soon Ong; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:304-313
Stochastic Variance Reduction for Nonconvex Optimization
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:314-323
Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, David Blei; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:324-333
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams
Roy Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, Benjamin Marlin; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:334-343
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Binary embeddings with structured hashed projections
Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:344-353
A Variational Analysis of Stochastic Gradient Algorithms
Stephan Mandt, Matthew Hoffman, David Blei; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:354-363
Adaptive Sampling for SGD by Exploiting Side Information
Siddharth Gopal; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:364-372
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Learning from Multiway Data: Simple and Efficient Tensor Regression
Rose Yu, Yan Liu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:373-381
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A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models
Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:382-391
Online Stochastic Linear Optimization under One-bit Feedback
Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-hua Zhou; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:392-401
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
Rodolphe Jenatton, Jim Huang, Cedric Archambeau; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:402-411
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Actively Learning Hemimetrics with Applications to Eliciting User Preferences
Adish Singla, Sebastian Tschiatschek, Andreas Krause; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:412-420
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Learning Simple Algorithms from Examples
Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:421-429
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Learning Physical Intuition of Block Towers by Example
Adam Lerer, Sam Gross, Rob Fergus; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:430-438
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Structure Learning of Partitioned Markov Networks
Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:439-448
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:449-457
Beyond CCA: Moment Matching for Multi-View Models
Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:458-467
Fast methods for estimating the Numerical rank of large matrices
Shashanka Ubaru, Yousef Saad; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:468-477
Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie, Ross Girshick, Ali Farhadi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:478-487
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Efficient Private Empirical Risk Minimization for High-dimensional Learning
Shiva Prasad Kasiviswanathan, Hongxia Jin; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:488-497
Parameter Estimation for Generalized Thurstone Choice Models
Milan Vojnovic, Seyoung Yun; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:498-506
Large-Margin Softmax Loss for Convolutional Neural Networks
Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:507-516
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A Random Matrix Approach to Echo-State Neural Networks
Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:517-525
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Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings
Rie Johnson, Tong Zhang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:526-534
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Optimality of Belief Propagation for Crowdsourced Classification
Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:535-544
Stability of Controllers for Gaussian Process Forward Models
Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:545-554
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Learning privately from multiparty data
Jihun Hamm, Yingjun Cao, Mikhail Belkin; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:555-563
Network Morphism
Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:564-572
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A Kronecker-factored approximate Fisher matrix for convolution layers
Roger Grosse, James Martens; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:573-582
Experimental Design on a Budget for Sparse Linear Models and Applications
Sathya Narayanan Ravi, Vamsi Ithapu, Sterling Johnson, Vikas Singh; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:583-592
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Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, Simon Lacoste-Julien; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:593-602
Exact Exponent in Optimal Rates for Crowdsourcing
Chao Gao, Yu Lu, Dengyong Zhou; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:603-611
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
Yuting Zhang, Kibok Lee, Honglak Lee; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:612-621
Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit
Jie Shen, Ping Li, Huan Xu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:622-631
A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization
Frank Curtis; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:632-641
Stochastic Quasi-Newton Langevin Monte Carlo
Umut Simsekli, Roland Badeau, Taylan Cemgil, Gaël Richard; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:642-651
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:652-661
Fast Rate Analysis of Some Stochastic Optimization Algorithms
Chao Qu, Huan Xu, Chong Ong; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:662-670
Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing
Ke Li, Jitendra Malik; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:671-679
Smooth Imitation Learning for Online Sequence Prediction
Hoang Le, Andrew Kang, Yisong Yue, Peter Carr; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:680-688
Community Recovery in Graphs with Locality
Yuxin Chen, Govinda Kamath, Changho Suh, David Tse; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:689-698
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Variance Reduction for Faster Non-Convex Optimization
Zeyuan Allen-Zhu, Elad Hazan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:699-707
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Loss factorization, weakly supervised learning and label noise robustness
Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:708-717
Analysis of Deep Neural Networks with Extended Data Jacobian Matrix
Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff Bilmes, Matthai Plilipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, Ozlem Aslan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:718-726
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Doubly Decomposing Nonparametric Tensor Regression
Masaaki Imaizumi, Kohei Hayashi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:727-736
Hyperparameter optimization with approximate gradient
Fabian Pedregosa; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:737-746
SDCA without Duality, Regularization, and Individual Convexity
Shai Shalev-Shwartz; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:747-754
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Heteroscedastic Sequences: Beyond Gaussianity
Oren Anava, Shie Mannor; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:755-763
A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:764-773
On the Quality of the Initial Basin in Overspecified Neural Networks
Itay Safran, Ohad Shamir; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:774-782
Primal-Dual Rates and Certificates
Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:783-792
Minimizing the Maximal Loss: How and Why
Shai Shalev-Shwartz, Yonatan Wexler; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:793-801
The Information-Theoretic Requirements of Subspace Clustering with Missing Data
Daniel Pimentel-Alarcon, Robert Nowak; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:802-810
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Online Learning with Feedback Graphs Without the Graphs
Alon Cohen, Tamir Hazan, Tomer Koren; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:811-819
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PAC learning of Probabilistic Automaton based on the Method of Moments
Hadrien Glaude, Olivier Pietquin; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:820-829
Estimating Structured Vector Autoregressive Models
Igor Melnyk, Arindam Banerjee; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:830-839
Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends
Christopher Tosh; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:840-849
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:850-858
A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:859-868
Correlation Clustering and Biclustering with Locally Bounded Errors
Gregory Puleo, Olgica Milenkovic; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:869-877
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PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem
Yahel David, Nahum Shimkin; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:878-887
A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation
Mohamed Elhoseiny, Tarek El-Gaaly, Amr Bakry, Ahmed Elgammal; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:888-897
BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces
Shane Carr, Roman Garnett, Cynthia Lo; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:898-907
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On the Iteration Complexity of Oblivious First-Order Optimization Algorithms
Yossi Arjevani, Ohad Shamir; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:908-916
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning
Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis Haupt; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:917-925
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation
David Wipf; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:926-935
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Fast k-means with accurate bounds
James Newling, Francois Fleuret; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:936-944
Boolean Matrix Factorization and Noisy Completion via Message Passing
Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:945-954
Convolutional Rectifier Networks as Generalized Tensor Decompositions
Nadav Cohen, Amnon Shashua; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:955-963
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:964-973
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Anytime Exploration for Multi-armed Bandits using Confidence Information
Kwang-Sung Jun, Robert Nowak; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:974-982
Structured Prediction Energy Networks
David Belanger, Andrew McCallum; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:983-992
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang, Jason D. Lee, Michael I. Jordan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:993-1001
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Compressive Spectral Clustering
Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1002-1011
Low-rank tensor completion: a Riemannian manifold preconditioning approach
Hiroyuki Kasai, Bamdev Mishra; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1012-1021
Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow
Huishuai Zhang, Yuejie Chi, Yingbin Liang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1022-1031
Estimating Maximum Expected Value through Gaussian Approximation
Carlo D’Eramo, Marcello Restelli, Alessandro Nuara; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1032-1040
Representational Similarity Learning with Application to Brain Networks
Urvashi Oswal, Christopher Cox, Matthew Lambon-Ralph, Timothy Rogers, Robert Nowak; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1041-1049
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal, Zoubin Ghahramani; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1050-1059
Generative Adversarial Text to Image Synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1060-1069
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data
Sandhya Prabhakaran, Elham Azizi, Ambrose Carr, Dana Pe’er; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1070-1079
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Zeyuan Allen-Zhu, Yang Yuan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1080-1089
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Sparse Parameter Recovery from Aggregated Data
Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1090-1099
Deep Structured Energy Based Models for Anomaly Detection
Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1100-1109
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Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling
Zeyuan Allen-Zhu, Zheng Qu, Peter Richtarik, Yang Yuan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1110-1119
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Unitary Evolution Recurrent Neural Networks
Martin Arjovsky, Amar Shah, Yoshua Bengio; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1120-1128
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Markov Latent Feature Models
Aonan Zhang, John Paisley; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1129-1137
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The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks
Yingfei Wang, Chu Wang, Warren Powell; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1138-1147
A Simple and Provable Algorithm for Sparse Diagonal CCA
Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1148-1157
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods
Huikang Liu, Weijie Wu, Anthony Man-Cho So; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1158-1167
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
Devansh Arpit, Yingbo Zhou, Bhargava Kota, Venu Govindaraju; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1168-1176
Learning to Generate with Memory
Chongxuan Li, Jun Zhu, Bo Zhang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1177-1186
Learning End-to-end Video Classification with Rank-Pooling
Basura Fernando, Stephen Gould; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1187-1196
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Learning to Filter with Predictive State Inference Machines
Wen Sun, Arun Venkatraman, Byron Boots, J.Andrew Bagnell; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1197-1205
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling
Mostafa Rahmani, Geroge Atia; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1206-1214
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DCM Bandits: Learning to Rank with Multiple Clicks
Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, Zheng Wen; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1215-1224
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, Yoram Singer; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1225-1234
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Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
Junpei Komiyama, Junya Honda, Hiroshi Nakagawa; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1235-1244
Contextual Combinatorial Cascading Bandits
Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1245-1253
Conservative Bandits
Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvari; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1254-1262
Variance-Reduced and Projection-Free Stochastic Optimization
Elad Hazan, Haipeng Luo; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1263-1271
Factored Temporal Sigmoid Belief Networks for Sequence Learning
Jiaming Song, Zhe Gan, Lawrence Carin; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1272-1281
False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking
QianQian Xu, Jiechao Xiong, Xiaochun Cao, Yuan Yao; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1282-1291
Strongly-Typed Recurrent Neural Networks
David Balduzzi, Muhammad Ghifary; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1292-1300
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Distributed Clustering of Linear Bandits in Peer to Peer Networks
Nathan Korda, Balazs Szorenyi, Shuai Li; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1301-1309
Collapsed Variational Inference for Sum-Product Networks
Han Zhao, Tameem Adel, Geoff Gordon, Brandon Amos; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1310-1318
On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search
Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1319-1328
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Benchmarking Deep Reinforcement Learning for Continuous Control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1329-1338
K-Means Clustering with Distributed Dimensions
Hu Ding, Yu Liu, Lingxiao Huang, Jian Li; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1339-1348
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Dmitry Ulyanov, Vadim Lebedev, Andrea, Victor Lempitsky; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1349-1357
Fast Constrained Submodular Maximization: Personalized Data Summarization
Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1358-1367
On the Statistical Limits of Convex Relaxations
Zhaoran Wang, Quanquan Gu, Han Liu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1368-1377
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1378-1387
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Igor Colin, Aurelien Bellet, Joseph Salmon, Stéphan Clémençon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1388-1396
Solving Ridge Regression using Sketched Preconditioned SVRG
Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1397-1405
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
Prashanth L.A., Cheng Jie, Michael Fu, Steve Marcus, Csaba Szepesvari; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1406-1415
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Estimating Accuracy from Unlabeled Data: A Bayesian Approach
Emmanouil Antonios Platanios, Avinava Dubey, Tom Mitchell; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1416-1425
Non-negative Matrix Factorization under Heavy Noise
Chiranjib Bhattacharya, Navin Goyal, Ravindran Kannan, Jagdeep Pani; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1426-1434
Extreme F-measure Maximization using Sparse Probability Estimates
Kalina Jasinska, Krzysztof Dembczynski, Robert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hullermeier; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1435-1444
Auxiliary Deep Generative Models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1445-1453
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Importance Sampling Tree for Large-scale Empirical Expectation
Olivier Canevet, Cijo Jose, Francois Fleuret; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1454-1462
Starting Small - Learning with Adaptive Sample Sizes
Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1463-1471
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Thang Bui, Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Yingzhen Li, Richard Turner; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1472-1481
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1482-1491
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Predictive Entropy Search for Multi-objective Bayesian Optimization
Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Amar Shah, Ryan Adams; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1492-1501
Black-Box Alpha Divergence Minimization
Jose Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1511-1520
One-Shot Generalization in Deep Generative Models
Danilo Rezende, Shakir, Ivo Danihelka, Karol Gregor, Daan Wierstra; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1521-1529
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Optimal Classification with Multivariate Losses
Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit Dhillon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1530-1538
A ranking approach to global optimization
Cedric Malherbe, Emile Contal, Nicolas Vayatis; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1539-1547
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric Xing; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1548-1557
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1558-1566
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Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
Christopher De Sa, Chris Re, Kunle Olukotun; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1567-1576
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1577-1586
Anytime optimal algorithms in stochastic multi-armed bandits
Rémy Degenne, Vianney Perchet; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1587-1595
Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design
William Hoiles, Mihaela Schaar; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1596-1604
On collapsed representation of hierarchical Completely Random Measures
Gaurav Pandey, Ambedkar Dukkipati; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1605-1613
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
Andre Martins, Ramon Astudillo; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1614-1623
Black-box Optimization with a Politician
Sebastien Bubeck, Yin Tat Lee; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1624-1631
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Gaussian process nonparametric tensor estimator and its minimax optimality
Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1632-1641
No-Regret Algorithms for Heavy-Tailed Linear Bandits
Andres Munoz Medina, Scott Yang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1642-1650
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Extended and Unscented Kitchen Sinks
Edwin Bonilla, Daniel Steinberg, Alistair Reid; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1651-1659
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization
Zhiqiang Xu, Peilin Zhao, Jianneng Cao, Xiaoli Li; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1660-1669
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1670-1679
ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission
Jinsung Yoon, Ahmed Alaa, Scott Hu, Mihaela Schaar; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1680-1689
An optimal algorithm for the Thresholding Bandit Problem
Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1690-1698
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching
Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1699-1707
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Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Christos Louizos, Max Welling; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1708-1716
Learning Granger Causality for Hawkes Processes
Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1717-1726
Neural Variational Inference for Text Processing
Yishu Miao, Lei Yu, Phil Blunsom; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1727-1736
Dictionary Learning for Massive Matrix Factorization
Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1737-1746
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Pixel Recurrent Neural Networks
Aäron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1747-1756
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Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well
Özgür Şimşek, Simón Algorta, Amit Kothiyal; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1757-1765
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Gaussian quadrature for matrix inverse forms with applications
Chengtao Li, Suvrit Sra, Stefanie Jegelka; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1766-1775
Train and Test Tightness of LP Relaxations in Structured Prediction
Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1776-1785
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares
Raman Arora, Poorya Mianjy, Teodor Marinov; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1786-1794
Hierarchical Compound Poisson Factorization
Mehmet Basbug, Barbara Engelhardt; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1795-1803
Opponent Modeling in Deep Reinforcement Learning
He He, Jordan Boyd-Graber, Kevin Kwok, Hal Daumé III; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1804-1813
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No penalty no tears: Least squares in high-dimensional linear models
Xiangyu Wang, David Dunson, Chenlei Leng; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1814-1822
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Zheng Qu, Peter Richtarik, Martin Takac, Olivier Fercoq; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1823-1832
On Graduated Optimization for Stochastic Non-Convex Problems
Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1833-1841
Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1842-1850
The knockoff filter for FDR control in group-sparse and multitask regression
Ran Dai, Rina Barber; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1851-1859
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Softened Approximate Policy Iteration for Markov Games
Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1860-1868
Stochastic Block BFGS: Squeezing More Curvature out of Data
Robert Gower, Donald Goldfarb, Peter Richtarik; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1869-1878
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Differential Geometric Regularization for Supervised Learning of Classifiers
Qinxun Bai, Steven Rosenberg, Zheng Wu, Stan Sclaroff; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1879-1888
Exploiting Cyclic Symmetry in Convolutional Neural Networks
Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1889-1898
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Graying the black box: Understanding DQNs
Tom Zahavy, Nir Ben-Zrihem, Shie Mannor; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1899-1908
The Sum-Product Theorem: A Foundation for Learning Tractable Models
Abram Friesen, Pedro Domingos; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1909-1918
Pareto Frontier Learning with Expensive Correlated Objectives
Amar Shah, Zoubin Ghahramani; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1919-1927
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1928-1937
A Simple and Strongly-Local Flow-Based Method for Cut Improvement
Nate Veldt, David Gleich, Michael Mahoney; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1938-1947
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1948-1957
Barron and Cover’s Theory in Supervised Learning and its Application to Lasso
Masanori Kawakita, Jun’ichi Takeuchi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1958-1966
Nonparametric Canonical Correlation Analysis
Tomer Michaeli, Weiran Wang, Karen Livescu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1967-1976
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits
Alexander Rakhlin, Karthik Sridharan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1977-1985
Associative Long Short-Term Memory
Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1986-1994
Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, Nando Freitas; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1995-2003
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Persistence weighted Gaussian kernel for topological data analysis
Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2004-2013
Learning Convolutional Neural Networks for Graphs
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2014-2023
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Persistent RNNs: Stashing Recurrent Weights On-Chip
Greg Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2024-2033
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Recurrent Orthogonal Networks and Long-Memory Tasks
Mikael Henaff, Arthur Szlam, Yann LeCun; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2034-2042
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The Arrow of Time in Multivariate Time Series
Stefan Bauer, Bernhard Schölkopf, Jonas Peters; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2043-2051
Mixture Proportion Estimation via Kernel Embeddings of Distributions
Harish Ramaswamy, Clayton Scott, Ambuj Tewari; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2052-2060
Fast DPP Sampling for Nystrom with Application to Kernel Methods
Chengtao Li, Stefanie Jegelka, Suvrit Sra; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2061-2070
Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, Guillaume Bouchard; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2071-2080
Interactive Bayesian Hierarchical Clustering
Sharad Vikram, Sanjoy Dasgupta; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2081-2090
A Convolutional Attention Network for Extreme Summarization of Source Code
Miltiadis Allamanis, Hao Peng, Charles Sutton; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2091-2100
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How to Fake Multiply by a Gaussian Matrix
Michael Kapralov, Vamsi Potluru, David Woodruff; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2101-2110
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing
Marco Gaboardi, Hyun Lim, Ryan Rogers, Salil Vadhan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2111-2120
Pliable Rejection Sampling
Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric Maillard; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2121-2129
Differentially Private Policy Evaluation
Borja Balle, Maziar Gomrokchi, Doina Precup; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2130-2138
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
Philip Thomas, Emma Brunskill; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2139-2148
Discrete Deep Feature Extraction: A Theory and New Architectures
Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Boelcskei; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2149-2158
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2159-2168
Training Deep Neural Networks via Direct Loss Minimization
Yang Song, Alexander Schwing, Richard, Raquel Urtasun; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2169-2177
Sequence to Sequence Training of CTC-RNNs with Partial Windowing
Kyuyeon Hwang, Wonyong Sung; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2178-2187
Variational Inference for Monte Carlo Objectives
Andriy Mnih, Danilo Rezende; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2188-2196
Hierarchical Decision Making In Electricity Grid Management
Gal Dalal, Elad Gilboa, Shie Mannor; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2197-2206
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Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization
Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause, Yaron Singer; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2207-2216
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2217-2225
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Isotonic Hawkes Processes
Yichen Wang, Bo Xie, Nan Du, Le Song; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2226-2234
Cross-Graph Learning of Multi-Relational Associations
Hanxiao Liu, Yiming Yang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2235-2243
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Markov-modulated Marked Poisson Processes for Check-in Data
Jiangwei Pan, Vinayak Rao, Pankaj Agarwal, Alan Gelfand; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2244-2253
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Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference
Tudor Achim, Ashish Sabharwal, Stefano Ermon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2254-2262
On the Power and Limits of Distance-Based Learning
Periklis Papakonstantinou, Jia Xu, Guang Yang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2263-2271
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery
Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit Dhillon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2272-2280
Generalized Direct Change Estimation in Ising Model Structure
Farideh Fazayeli, Arindam Banerjee; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2281-2290
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Robust Principal Component Analysis with Side Information
Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2291-2299
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation
Huan Gui, Jiawei Han, Quanquan Gu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2300-2309
Early and Reliable Event Detection Using Proximity Space Representation
Maxime Sangnier, Jerome Gauthier, Alain Rakotomamonjy; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2310-2319
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Stratified Sampling Meets Machine Learning
Edo Liberty, Kevin Lang, Konstantin Shmakov; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2320-2329
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Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model
Xinze Guan, Raviv Raich, Weng-Keen Wong; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2330-2339
Generalization Properties and Implicit Regularization for Multiple Passes SGM
Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2340-2348
Principal Component Projection Without Principal Component Analysis
Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2349-2357
Recovery guarantee of weighted low-rank approximation via alternating minimization
Yuanzhi Li, Yingyu Liang, Andrej Risteski; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2358-2367
Deconstructing the Ladder Network Architecture
Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2368-2376
Generalization and Exploration via Randomized Value Functions
Ian Osband, Benjamin Van Roy, Zheng Wen; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2377-2386
Evasion and Hardening of Tree Ensemble Classifiers
Alex Kantchelian, J. D. Tygar, Anthony Joseph; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2387-2396
Dynamic Memory Networks for Visual and Textual Question Answering
Caiming Xiong, Stephen Merity, Richard Socher; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2397-2406
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Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabas Poczos; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2407-2416
Learning Population-Level Diffusions with Generative RNNs
Tatsunori Hashimoto, David Gifford, Tommi Jaakkola; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2417-2426
Expressiveness of Rectifier Networks
Xingyuan Pan, Vivek Srikumar; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2427-2435
Discrete Distribution Estimation under Local Privacy
Peter Kairouz, Keith Bonawitz, Daniel Ramage; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2436-2444
Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies
David Inouye, Pradeep Ravikumar, Inderjit Dhillon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2445-2453
A Box-Constrained Approach for Hard Permutation Problems
Cong Han Lim, Steve Wright; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2454-2463
Geometric Mean Metric Learning
Pourya Zadeh, Reshad Hosseini, Suvrit Sra; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2464-2471
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Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity
Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina Eldar, Tong Zhang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2472-2481
Conditional Bernoulli Mixtures for Multi-label Classification
Cheng Li, Bingyu Wang, Virgil Pavlu, Javed Aslam; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2482-2491
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Yutian Chen, Zoubin Ghahramani; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2492-2501
Recycling Randomness with Structure for Sublinear time Kernel Expansions
Krzysztof Choromanski, Vikas Sindhwani; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2502-2510
Bidirectional Helmholtz Machines
Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2511-2519
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Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier
Jacob Abernethy, Elad Hazan; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2520-2528
Preconditioning Kernel Matrices
Kurt Cutajar, Michael Osborne, John Cunningham, Maurizio Filippone; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2529-2538
Greedy Column Subset Selection: New Bounds and Distributed Algorithms
Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2539-2548
Dynamic Capacity Networks
Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2549-2558
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Pricing a Low-regret Seller
Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2559-2567
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Estimation from Indirect Supervision with Linear Moments
Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2568-2577
Speeding up k-means by approximating Euclidean distances via block vectors
Thomas Bottesch, Thomas Bühler, Markus Kächele; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2578-2586
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Learning and Inference via Maximum Inner Product Search
Stephen Mussmann, Stefano Ermon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2587-2596
A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums
Anton Rodomanov, Dmitry Kropotov; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2597-2605
A Kernel Test of Goodness of Fit
Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2606-2615
Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2616-2625
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco, Praneeth Netrapalli, Aaron Sidford; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2626-2634
A Theory of Generative ConvNet
Jianwen Xie, Yang Lu, Song-Chun Zhu, Yingnian Wu; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2635-2644
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Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Quanming Yao, James Kwok; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2645-2654
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Computationally Efficient Nyström Approximation using Fast Transforms
Si Si, Cho-Jui Hsieh, Inderjit Dhillon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2655-2663
Gromov-Wasserstein Averaging of Kernel and Distance Matrices
Gabriel Peyré, Marco Cuturi, Justin Solomon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2664-2672
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Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics
Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2673-2681
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2682-2691
Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning
Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2692-2701
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Discriminative Embeddings of Latent Variable Models for Structured Data
Hanjun Dai, Bo Dai, Le Song; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2702-2711
Robust Random Cut Forest Based Anomaly Detection on Streams
Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2712-2721
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2722-2731
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Clustering High Dimensional Categorical Data via Topographical Features
Chao Chen, Novi Quadrianto; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2732-2740
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Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
Rong Ge, Chi Jin, Sham, Praneeth Netrapalli, Aaron Sidford; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2741-2750
Algorithms for Optimizing the Ratio of Submodular Functions
Wenruo Bai, Rishabh Iyer, Kai Wei, Jeff Bilmes; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2751-2759
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Model-Free Imitation Learning with Policy Optimization
Jonathan Ho, Jayesh Gupta, Stefano Ermon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2760-2769
ADIOS: Architectures Deep In Output Space
Moustapha Cisse, Maruan Al-Shedivat, Samy Bengio; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2770-2779
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Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2780-2789
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Control of Memory, Active Perception, and Action in Minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder, Honglak Lee; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2790-2799
The Label Complexity of Mixed-Initiative Classifier Training
Jina Suh, Xiaojin Zhu, Saleema Amershi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2800-2809
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
Aaron Schein, Mingyuan Zhou, David Blei, Hanna Wallach; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2810-2819
Tensor Decomposition via Joint Matrix Schur Decomposition
Nicolo Colombo, Nikos Vlassis; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2820-2828
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Continuous Deep Q-Learning with Model-based Acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2829-2838
Domain Adaptation with Conditional Transferable Components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2839-2848
Fixed Point Quantization of Deep Convolutional Networks
Darryl Lin, Sachin Talathi, Sreekanth Annapureddy; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2849-2858
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Provable Algorithms for Inference in Topic Models
Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2859-2867
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Epigraph projections for fast general convex programming
Po-Wei Wang, Matt Wytock, Zico Kolter; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2868-2877
Fast Algorithms for Segmented Regression
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2878-2886
Energetic Natural Gradient Descent
Philip Thomas, Bruno Castro Silva, Christoph Dann, Emma Brunskill; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2887-2895
Partition Functions from Rao-Blackwellized Tempered Sampling
David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2896-2905
Learning Mixtures of Plackett-Luce Models
Zhibing Zhao, Peter Piech, Lirong Xia; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2906-2914
Near Optimal Behavior via Approximate State Abstraction
David Abel, David Hershkowitz, Michael Littman; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2915-2923
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Power of Ordered Hypothesis Testing
Lihua Lei, William Fithian; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2924-2932
PHOG: Probabilistic Model for Code
Pavol Bielik, Veselin Raychev, Martin Vechev; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2933-2942
Shifting Regret, Mirror Descent, and Matrices
Andras Gyorgy, Csaba Szepesvari; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2943-2951
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Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Jelena Luketina, Mathias Berglund, Klaus Greff, Tapani Raiko; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2952-2960
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Model-Free Trajectory Optimization for Reinforcement Learning
Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2961-2970
Controlling the distance to a Kemeny consensus without computing it
Yunlong Jiao, Anna Korba, Eric Sibony; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2971-2980
Horizontally Scalable Submodular Maximization
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2981-2989
Group Equivariant Convolutional Networks
Taco Cohen, Max Welling; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2990-2999
Stochastic Discrete Clenshaw-Curtis Quadrature
Nico Piatkowski, Katharina Morik; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:3000-3009
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Correcting Forecasts with Multifactor Neural Attention
Matthew Riemer, Aditya Vempaty, Flavio Calmon, Fenno Heath, Richard Hull, Elham Khabiri; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:3010-3019
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Learning Representations for Counterfactual Inference
Fredrik Johansson, Uri Shalit, David Sontag; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:3020-3029
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series
Yunseong Hwang, Anh Tong, Jaesik Choi; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:3030-3039
Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige, Frank Wood; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:3040-3049
Slice Sampling on Hamiltonian Trajectories
Benjamin Bloem-Reddy, John Cunningham; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:3050-3058
Noisy Activation Functions
Caglar Gulcehre, Marcin Moczulski, Misha Denil, Yoshua Bengio; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:3059-3068
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PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit Dhillon; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:3069-3077
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