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Editors: Francis Bach, David Blei
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Stochastic Optimization with Importance Sampling for Regularized Loss Minimization
; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1-9
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Approval Voting and Incentives in Crowdsourcing
Nihar Shah, Dengyong Zhou, Yuval Peres; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:10-19
A low variance consistent test of relative dependency
Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:20-29
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An Aligned Subtree Kernel for Weighted Graphs
Lu Bai, Luca Rossi, Zhihong Zhang, Edwin Hancock; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:30-39
Spectral Clustering via the Power Method - Provably
Christos Boutsidis, Prabhanjan Kambadur, Alex Gittens; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:40-48
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Information Geometry and Minimum Description Length Networks
Ke Sun, Jun Wang, Alexandros Kalousis, Stephan Marchand-Maillet; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:49-58
Efficient Training of LDA on a GPU by Mean-for-Mode Estimation
Jean-Baptiste Tristan, Joseph Tassarotti, Guy Steele; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:59-68
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Adaptive Stochastic Alternating Direction Method of Multipliers
Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:69-77
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A Lower Bound for the Optimization of Finite Sums
Alekh Agarwal, Leon Bottou; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:78-86
Learning Word Representations with Hierarchical Sparse Coding
Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:87-96
Learning Transferable Features with Deep Adaptation Networks
Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:97-105
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Robust partially observable Markov decision process
Takayuki Osogami; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:106-115
On the Relationship between Sum-Product Networks and Bayesian Networks
Han Zhao, Mazen Melibari, Pascal Poupart; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:116-124
Learning from Corrupted Binary Labels via Class-Probability Estimation
Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, Bob Williamson; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:125-134
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection
Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:135-143
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A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate
Ohad Shamir; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:144-152
Attribute Efficient Linear Regression with Distribution-Dependent Sampling
Doron Kukliansky, Ohad Shamir; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:153-161
Learning Local Invariant Mahalanobis Distances
Ethan Fetaya, Shimon Ullman; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:162-168
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Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis
Zhuang Ma, Yichao Lu, Dean Foster; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:169-178
Abstraction Selection in Model-based Reinforcement Learning
Nan Jiang, Alex Kulesza, Satinder Singh; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:179-188
Surrogate Functions for Maximizing Precision at the Top
Purushottam Kar, Harikrishna Narasimhan, Prateek Jain; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:189-198
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes
Harikrishna Narasimhan, Purushottam Kar, Prateek Jain; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:199-208
Coresets for Nonparametric Estimation - the Case of DP-Means
Olivier Bachem, Mario Lucic, Andreas Krause; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:209-217
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits
Pratik Gajane, Tanguy Urvoy, Fabrice Clérot; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:218-227
Functional Subspace Clustering with Application to Time Series
Mohammad Taha Bahadori, David Kale, Yingying Fan, Yan Liu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:228-237
Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams
Rose Yu, Dehua Cheng, Yan Liu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:238-247
Atomic Spatial Processes
Sean Jewell, Neil Spencer, Alexandre Bouchard-Côté; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:248-256
Classification with Low Rank and Missing Data
Elad Hazan, Roi Livni, Yishay Mansour; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:257-266
Dynamic Sensing: Better Classification under Acquisition Constraints
Oran Richman, Shie Mannor; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:267-275
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A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis
Pinghua Gong, Jieping Ye; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:276-284
Telling cause from effect in deterministic linear dynamical systems
Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:285-294
High Dimensional Bayesian Optimisation and Bandits via Additive Models
Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:295-304
Theory of Dual-sparse Regularized Randomized Reduction
Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:305-314
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Generalization error bounds for learning to rank: Does the length of document lists matter?
Ambuj Tewari, Sougata Chaudhuri; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:315-323
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data
Toby Hocking, Guillem Rigaill, Guillaume Bourque; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:324-332
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Mind the duality gap: safer rules for the Lasso
Olivier Fercoq, Alexandre Gramfort, Joseph Salmon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:333-342
A General Analysis of the Convergence of ADMM
Robert Nishihara, Laurent Lessard, Ben Recht, Andrew Packard, Michael Jordan; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:343-352
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Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Yuchen Zhang, Xiao Lin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:353-361
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DiSCO: Distributed Optimization for Self-Concordant Empirical Loss
Yuchen Zhang, Xiao Lin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:362-370
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Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons
Yuxin Chen, Changho Suh; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:371-380
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs
Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:381-390
Structural Maxent Models
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Umar Syed; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:391-399
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning
Debarghya Ghoshdastidar, Ambedkar Dukkipati; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:400-409
The Benefits of Learning with Strongly Convex Approximate Inference
Ben London, Bert Huang, Lise Getoor; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:410-418
Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA
Bo Xin, David Wipf; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:419-427
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Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View
Takanori Maehara, Akihiro Yabe, Ken-ichi Kawarabayashi; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:428-437
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains
Katharina Blechschmidt, Joachim Giesen, Soeren Laue; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:438-447
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe, Christian Szegedy; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:448-456
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds
Yuchen Zhang, Martin Wainwright, Michael Jordan; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:457-465
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Landmarking Manifolds with Gaussian Processes
Dawen Liang, John Paisley; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:466-474
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Markov Mixed Membership Models
Aonan Zhang, John Paisley; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:475-483
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A Unified Framework for Outlier-Robust PCA-like Algorithms
Wenzhuo Yang, Huan Xu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:484-493
Streaming Sparse Principal Component Analysis
Wenzhuo Yang, Huan Xu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:494-503
A Divide and Conquer Framework for Distributed Graph Clustering
Wenzhuo Yang, Huan Xu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:504-513
How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances?
Senjian An, Farid Boussaid, Mohammed Bennamoun; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:514-523
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Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning
K. Lakshmanan, Ronald Ortner, Daniil Ryabko; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:524-532
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The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling
Michael Betancourt; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:533-540
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Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets
Dan Garber, Elad Hazan; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:541-549
Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models
Mrinal Das, Trapit Bansal, Chiranjib Bhattacharyya; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:550-559
Online Learning of Eigenvectors
Dan Garber, Elad Hazan, Tengyu Ma; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:560-568
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data
Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:569-578
Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup
Yufei Ding, Yue Zhao, Xipeng Shen, Madanlal Musuvathi, Todd Mytkowicz; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:579-587
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Ordinal Mixed Membership Models
Seppo Virtanen, Mark Girolami; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:588-596
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Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:597-606
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods
Seth Flaxman, Andrew Wilson, Daniel Neill, Hannes Nickisch, Alex Smola; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:607-616
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares
Garvesh Raskutti, Michael Mahoney; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:617-625
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On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence
Nathaniel Korda, Prashanth La; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:626-634
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Learning Parametric-Output HMMs with Two Aliased States
Roi Weiss, Boaz Nadler; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:635-644
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Yarin Gal, Yutian Chen, Zoubin Ghahramani; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:645-654
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
Yarin Gal, Richard Turner; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:655-664
Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top
Arun Rajkumar, Suprovat Ghoshal, Lek-Heng Lim, Shivani Agarwal; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:665-673
Stochastic Dual Coordinate Ascent with Adaptive Probabilities
Dominik Csiba, Zheng Qu, Peter Richtarik; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:674-683
Vector-Space Markov Random Fields via Exponential Families
Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:684-692
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Jonathan Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:693-701
Low Rank Approximation using Error Correcting Coding Matrices
Shashanka Ubaru, Arya Mazumdar, Yousef Saad; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:702-710
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Off-policy Model-based Learning under Unknown Factored Dynamics
Assaf Hallak, Francois Schnitzler, Timothy Mann, Shie Mannor; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:711-719
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification
Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xianqiu Li, Xilin Chen; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:720-729
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Asymmetric Transfer Learning with Deep Gaussian Processes
Melih Kandemir; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:730-738
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Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing
Rongda Zhu, Quanquan Gu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:739-747
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BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
Stephan Gouws, Yoshua Bengio, Greg Corrado; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:748-756
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Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization
Jiangwen Sun, Jin Lu, Tingyang Xu, Jinbo Bi; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:757-766
Cascading Bandits: Learning to Rank in the Cascade Model
Branislav Kveton, Csaba Szepesvari, Zheng Wen, Azin Ashkan; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:767-776
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models
James Foulds, Shachi Kumar, Lise Getoor; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:777-786
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions
Alina Ene, Huy Nguyen; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:787-795
Alpha-Beta Divergences Discover Micro and Macro Structures in Data
Karthik Narayan, Ali Punjani, Pieter Abbeel; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:796-804
Fictitious Self-Play in Extensive-Form Games
Johannes Heinrich, Marc Lanctot, David Silver; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:805-813
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
Adith Swaminathan, Thorsten Joachims; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:814-823
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The Hedge Algorithm on a Continuum
Walid Krichene, Maximilian Balandat, Claire Tomlin, Alexandre Bayen; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:824-832
A Linear Dynamical System Model for Text
David Belanger, Sham Kakade; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:833-842
Unsupervised Learning of Video Representations using LSTMs
Nitish Srivastava, Elman Mansimov, Ruslan Salakhudinov; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:843-852
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Message Passing for Collective Graphical Models
Tao Sun, Dan Sheldon, Akshat Kumar; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:853-861
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DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics
Yining Wang, Jun Zhu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:862-870
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades
Xinran He, Theodoros Rekatsinas, James Foulds, Lise Getoor, Yan Liu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:871-880
MADE: Masked Autoencoder for Distribution Estimation
Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:881-889
An Online Learning Algorithm for Bilinear Models
Yuanbin Wu, Shiliang Sun; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:890-898
Adaptive Belief Propagation
Georgios Papachristoudis, John Fisher; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:899-907
Large-scale log-determinant computation through stochastic Chebyshev expansions
Insu Han, Dmitry Malioutov, Jinwoo Shin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:908-917
Differentially Private Bayesian Optimization
Matt Kusner, Jacob Gardner, Roman Garnett, Kilian Weinberger; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:918-927
A Nearly-Linear Time Framework for Graph-Structured Sparsity
Chinmay Hegde, Piotr Indyk, Ludwig Schmidt; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:928-937
Support Matrix Machines
Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:938-947
Rademacher Observations, Private Data, and Boosting
Richard Nock, Giorgio Patrini, Arik Friedman; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:948-956
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From Word Embeddings To Document Distances
Matt Kusner, Yu Sun, Nicholas Kolkin, Kilian Weinberger; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:957-966
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Bayesian and Empirical Bayesian Forests
Taddy Matthew, Chun-Sheng Chen, Jun Yu, Mitch Wyle; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:967-976
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Inferring Graphs from Cascades: A Sparse Recovery Framework
Jean Pouget-Abadie, Thibaut Horel; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:977-986
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM
Ching-Pei Lee, Dan Roth; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:987-996
Safe Exploration for Optimization with Gaussian Processes
Yanan Sui, Alkis Gotovos, Joel Burdick, Andreas Krause; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:997-1005
The Ladder: A Reliable Leaderboard for Machine Learning Competitions
Avrim Blum, Moritz Hardt; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1006-1014
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Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
Maurizio Filippone, Raphael Engler; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1015-1024
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Finding Galaxies in the Shadows of Quasars with Gaussian Processes
Roman Garnett, Shirley Ho, Jeff Schneider; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1025-1033
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Following the Perturbed Leader for Online Structured Learning
Alon Cohen, Tamir Hazan; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1034-1042
Reified Context Models
Jacob Steinhardt, Percy Liang; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1043-1052
Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing
Yasin Abbasi-Yadkori, Peter Bartlett, Xi Chen, Alan Malek; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1053-1062
Learning Fast-Mixing Models for Structured Prediction
Jacob Steinhardt, Percy Liang; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1063-1072
A Probabilistic Model for Dirty Multi-task Feature Selection
Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1073-1082
On Deep Multi-View Representation Learning
Weiran Wang, Raman Arora, Karen Livescu, Jeff Bilmes; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1083-1092
Learning Program Embeddings to Propagate Feedback on Student Code
Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1093-1102
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Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems
Qiang Zhou, Qi Zhao; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1103-1112
Efficient Learning in Large-Scale Combinatorial Semi-Bandits
Zheng Wen, Branislav Kveton, Azin Ashkan; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1113-1122
Swept Approximate Message Passing for Sparse Estimation
Andre Manoel, Florent Krzakala, Eric Tramel, Lenka Zdeborovà; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1123-1132
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Simple regret for infinitely many armed bandits
Alexandra Carpentier, Michal Valko; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1133-1141
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Exponential Integration for Hamiltonian Monte Carlo
Wei-Lun Chao, Justin Solomon, Dominik Michels, Fei Sha; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1142-1151
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays
Junpei Komiyama, Junya Honda, Hiroshi Nakagawa; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1152-1161
Faster cover trees
Mike Izbicki, Christian Shelton; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1162-1170
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Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization
Tyler Johnson, Carlos Guestrin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1171-1179
Unsupervised Domain Adaptation by Backpropagation
Yaroslav Ganin, Victor Lempitsky; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1180-1189
Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer
Yan-Fu Liu, Cheng-Yu Hsu, Shan-Hung Wu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1190-1198
Manifold-valued Dirichlet Processes
Hyunwoo Kim, Jia Xu, Baba Vemuri, Vikas Singh; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1199-1208
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Multi-Task Learning for Subspace Segmentation
Yu Wang, David Wipf, Qing Ling, Wei Chen, Ian Wassell; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1209-1217
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik Kingma, Max Welling; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1218-1226
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Scalable Model Selection for Large-Scale Factorial Relational Models
Chunchen Liu, Lu Feng, Ryohei Fujimaki, Yusuke Muraoka; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1227-1235
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The Power of Randomization: Distributed Submodular Maximization on Massive Datasets
Rafael Barbosa, Alina Ene, Huy Nguyen, Justin Ward; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1236-1244
Dealing with small data: On the generalization of context trees
Ralf Eggeling, Mikko Koivisto, Ivo Grosse; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1245-1253
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
Xin Yuan, Ricardo Henao, Ephraim Tsalik, Raymond Langley, Lawrence Carin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1254-1263
A Bayesian nonparametric procedure for comparing algorithms
Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1264-1272
Convergence rate of Bayesian tensor estimator and its minimax optimality
Taiji Suzuki; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1273-1282
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments
Yifan Wu, Andras Gyorgy, Csaba Szepesvari; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1283-1291
Nested Sequential Monte Carlo Methods
Christian Naesseth, Fredrik Lindsten, Thomas Schon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1292-1301
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Sparse Variational Inference for Generalized GP Models
Rishit Sheth, Yuyang Wang, Roni Khardon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1302-1311
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Universal Value Function Approximators
Tom Schaul, Daniel Horgan, Karol Gregor, David Silver; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1312-1320
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games
Julien Perolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1321-1329
On Greedy Maximization of Entropy
Dravyansh Sharma, Ashish Kapoor, Amit Deshpande; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1330-1338
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Metadata Dependent Mondrian Processes
Yi Wang, Bin Li, Yang Wang, Fang Chen; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1339-1347
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Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM
Xiaojun Chang, Yi Yang, Eric Xing, Yaoliang Yu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1348-1357
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1358-1366
Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets
Woosang Lim, Minhwan Kim, Haesun Park, Kyomin Jung; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1367-1375
The Composition Theorem for Differential Privacy
Peter Kairouz, Sewoong Oh, Pramod Viswanath; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1376-1385
Convex Formulation for Learning from Positive and Unlabeled Data
Marthinus Du Plessis, Gang Niu, Masashi Sugiyama; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1386-1394
Threshold Influence Model for Allocating Advertising Budgets
Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1395-1404
Strongly Adaptive Online Learning
Amit Daniely, Alon Gonen, Shai Shalev-Shwartz; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1405-1411
CUR Algorithm for Partially Observed Matrices
Miao Xu, Rong Jin, Zhi-Hua Zhou; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1412-1421
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data
Yining Wang, Yu-Xiang Wang, Aarti Singh; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1422-1431
MRA-based Statistical Learning from Incomplete Rankings
Eric Sibony, Stéphan Clemençon, Jérémie Jakubowicz; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1432-1441
Risk and Regret of Hierarchical Bayesian Learners
Jonathan Huggins, Josh Tenenbaum; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1442-1451
Towards a Learning Theory of Cause-Effect Inference
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Iliya Tolstikhin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1452-1461
DRAW: A Recurrent Neural Network For Image Generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, Daan Wierstra; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1462-1471
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Multiview Triplet Embedding: Learning Attributes in Multiple Maps
Ehsan Amid, Antti Ukkonen; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1472-1480
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Distributed Gaussian Processes
Marc Deisenroth, Jun Wei Ng; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1481-1490
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Guaranteed Tensor Decomposition: A Moment Approach
Gongguo Tang, Parikshit Shah; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1491-1500
\ell_1,p-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods
Zirui Zhou, Qi Zhang, Anthony Man-Cho So; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1501-1510
Consistent estimation of dynamic and multi-layer block models
Qiuyi Han, Kevin Xu, Edoardo Airoldi; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1511-1520
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On the Rate of Convergence and Error Bounds for LSTD(λ)
Manel Tagorti, Bruno Scherrer; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1521-1529
Variational Inference with Normalizing Flows
Danilo Rezende, Shakir Mohamed; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1530-1538
Controversy in mechanistic modelling with Gaussian processes
Benn Macdonald, Catherine Higham, Dirk Husmeier; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1539-1547
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Convex Learning of Multiple Tasks and their Structure
Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1548-1557
K-hyperplane Hinge-Minimax Classifier
Margarita Osadchy, Tamir Hazan, Daniel Keren; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1558-1566
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Non-Stationary Approximate Modified Policy Iteration
Boris Lesner, Bruno Scherrer; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1567-1575
Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees
Mathieu Serrurier, Henri Prade; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1576-1584
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Geometric Conditions for Subspace-Sparse Recovery
Chong You, Rene Vidal; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1585-1593
An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process
Amar Shah, David Knowles, Zoubin Ghahramani; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1594-1603
Long Short-Term Memory Over Recursive Structures
Xiaodan Zhu, Parinaz Sobihani, Hongyu Guo; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1604-1612
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Weight Uncertainty in Neural Network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1613-1622
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Learning Submodular Losses with the Lovasz Hinge
Jiaqian Yu, Matthew Blaschko; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1623-1631
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Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection
Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander, Hoyt Koepke; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1632-1641
Hashing for Distributed Data
Cong Leng, Jiaxiang Wu, Jian Cheng, Xi Zhang, Hanqing Lu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1642-1650
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Large-scale Distributed Dependent Nonparametric Trees
Zhiting Hu, Ho Qirong, Avinava Dubey, Eric Xing; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1651-1659
Qualitative Multi-Armed Bandits: A Quantile-Based Approach
Balazs Szorenyi, Robert Busa-Fekete, Paul Weng, Eyke Hüllermeier; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1660-1668
Deep Edge-Aware Filters
Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, Jiaya Jia; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1669-1678
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A Convex Optimization Framework for Bi-Clustering
Shiau Hong Lim, Yudong Chen, Huan Xu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1679-1688
Is Feature Selection Secure against Training Data Poisoning?
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1689-1698
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Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Jose Miguel Hernandez-Lobato, Michael Gelbart, Matthew Hoffman, Ryan Adams, Zoubin Ghahramani; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1699-1707
A Theoretical Analysis of Metric Hypothesis Transfer Learning
Michaël Perrot, Amaury Habrard; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1708-1717
Generative Moment Matching Networks
Yujia Li, Kevin Swersky, Rich Zemel; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1718-1727
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Stay on path: PCA along graph paths
Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis, Han-Gyol Yi, Bharath Chandrasekaran; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1728-1736
Deep Learning with Limited Numerical Precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1737-1746
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Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices
Jie Wang, Jieping Ye; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1747-1756
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Harmonic Exponential Families on Manifolds
Taco Cohen, Max Welling; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1757-1765
Training Deep Convolutional Neural Networks to Play Go
Christopher Clark, Amos Storkey; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1766-1774
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Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)
Andrew Wilson, Hannes Nickisch; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1775-1784
Learning Deep Structured Models
Liang-Chieh Chen, Alexander Schwing, Alan Yuille, Raquel Urtasun; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1785-1794
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Community Detection Using Time-Dependent Personalized PageRank
Haim Avron, Lior Horesh; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1795-1803
Scalable Variational Inference in Log-supermodular Models
Josip Djolonga, Andreas Krause; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1804-1813
Variational Inference for Gaussian Process Modulated Poisson Processes
Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1814-1822
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Scalable Deep Poisson Factor Analysis for Topic Modeling
Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1823-1832
Hidden Markov Anomaly Detection
Nico Goernitz, Mikio Braun, Marius Kloft; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1833-1842
Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes
Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian Caffo; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1843-1851
Convex Calibrated Surrogates for Hierarchical Classification
Harish Ramaswamy, Ambuj Tewari, Shivani Agarwal; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1852-1860
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Jose Miguel Hernandez-Lobato, Ryan Adams; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1861-1869
Active Nearest Neighbors in Changing Environments
Christopher Berlind, Ruth Urner; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1870-1879
Bipartite Edge Prediction via Transductive Learning over Product Graphs
Hanxiao Liu, Yiming Yang; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1880-1888
Trust Region Policy Optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1889-1897
Discovering Temporal Causal Relations from Subsampled Data
Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1898-1906
Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons
Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit Dhillon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1907-1916
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
Philipp Geiger, Kun Zhang, Bernhard Schoelkopf, Mingming Gong, Dominik Janzing; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1917-1925
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On Symmetric and Asymmetric LSHs for Inner Product Search
Behnam Neyshabur, Nathan Srebro; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1926-1934
The Kendall and Mallows Kernels for Permutations
Yunlong Jiao, Jean-Philippe Vert; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1935-1944
Bayesian Multiple Target Localization
Purnima Rajan, Weidong Han, Raphael Sznitman, Peter Frazier, Bruno Jedynak; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1945-1953
Submodularity in Data Subset Selection and Active Learning
Kai Wei, Rishabh Iyer, Jeff Bilmes; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1954-1963
Variational Generative Stochastic Networks with Collaborative Shaping
Philip Bachman, Doina Precup; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1964-1972
Adding vs. Averaging in Distributed Primal-Dual Optimization
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik, Martin Takac; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1973-1982
Feature-Budgeted Random Forest
Feng Nan, Joseph Wang, Venkatesh Saligrama; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1983-1991
Entropic Graph-based Posterior Regularization
Maxwell Libbrecht, Michael Hoffman, Jeff Bilmes, William Noble; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1992-2001
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations
Tam Le, Marco Cuturi; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2002-2011
Low-Rank Matrix Recovery from Row-and-Column Affine Measurements
Or Zuk, Avishai Wagner; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2012-2020
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction
Sébastien Giguère, Amélie Rolland, Francois Laviolette, Mario Marchand; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2021-2029
A Multitask Point Process Predictive Model
Wenzhao Lian, Ricardo Henao, Vinayak Rao, Joseph Lucas, Lawrence Carin; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2030-2038
A Hybrid Approach for Probabilistic Inference using Random Projections
Michael Zhu, Stefano Ermon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2039-2047
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, Yoshua Bengio; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2048-2057
Learning to Search Better than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2058-2066
Gated Feedback Recurrent Neural Networks
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2067-2075
Context-based Unsupervised Data Fusion for Decision Making
Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela Schaar; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2076-2084
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Phrase-based Image Captioning
Remi Lebret, Pedro Pinheiro, Ronan Collobert; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2085-2094
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Celeste: Variational inference for a generative model of astronomical images
Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Mr Prabhat; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2095-2103
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Distributional Rank Aggregation, and an Axiomatic Analysis
Adarsh Prasad, Harsh Pareek, Pradeep Ravikumar; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2104-2112
Gradient-based Hyperparameter Optimization through Reversible Learning
Dougal Maclaurin, David Duvenaud, Ryan Adams; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2113-2122
Bimodal Modelling of Source Code and Natural Language
Miltos Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2123-2132
Cheap Bandits
Manjesh Hanawal, Venkatesh Saligrama, Michal Valko, Remi Munos; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2133-2142
Subsampling Methods for Persistent Homology
Frederic Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry Wasserman; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2143-2151
An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes, Philip Torr; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2152-2161
Binary Embedding: Fundamental Limits and Fast Algorithm
Xinyang Yi, Constantine Caramanis, Eric Price; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2162-2170
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, Ryan Adams; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2171-2180
How Hard is Inference for Structured Prediction?
Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2181-2190
Online Time Series Prediction with Missing Data
Oren Anava, Elad Hazan, Assaf Zeevi; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2191-2199
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach
Jason Pacheco, Erik Sudderth; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2200-2208
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A Fast Variational Approach for Learning Markov Random Field Language Models
Yacine Jernite, Alexander Rush, David Sontag; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2209-2217
Removing systematic errors for exoplanet search via latent causes
Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2218-2226
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Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Yves-Laurent Kom Samo, Stephen Roberts; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2227-2236
Correlation Clustering in Data Streams
KookJin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, Anthony Wirth; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2237-2246
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Learning Scale-Free Networks by Dynamic Node Specific Degree Prior
Qingming Tang, Siqi Sun, Jinbo Xu; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2247-2255
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, Surya Ganguli; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2256-2265
Modeling Order in Neural Word Embeddings at Scale
Andrew Trask, David Gilmore, Matthew Russell; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2266-2275
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Distributed Inference for Dirichlet Process Mixture Models
Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2276-2284
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Compressing Neural Networks with the Hashing Trick
Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, Yixin Chen; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2285-2294
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Intersecting Faces: Non-negative Matrix Factorization With New Guarantees
Rong Ge, James Zou; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2295-2303
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix
Roger Grosse, Ruslan Salakhudinov; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2304-2313
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A Deeper Look at Planning as Learning from Replay
Harm Vanseijen, Rich Sutton; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2314-2322
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Optimal and Adaptive Algorithms for Online Boosting
Alina Beygelzimer, Satyen Kale, Haipeng Luo; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2323-2331
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems
Christopher De Sa, Christopher Re, Kunle Olukotun; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2332-2341
An Empirical Exploration of Recurrent Network Architectures
Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2342-2350
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Complete Dictionary Recovery Using Nonconvex Optimization
Ju Sun, Qing Qu, John Wright; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2351-2360
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Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret
Haitham Bou Ammar, Rasul Tutunov, Eric Eaton; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2361-2369
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent
Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2370-2379
High Confidence Policy Improvement
Philip Thomas, Georgios Theocharous, Mohammad Ghavamzadeh; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2380-2388
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Fixed-point algorithms for learning determinantal point processes
Zelda Mariet, Suvrit Sra; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2389-2397
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Consistent Multiclass Algorithms for Complex Performance Measures
Harikrishna Narasimhan, Harish Ramaswamy, Aadirupa Saha, Shivani Agarwal; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2398-2407
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
James Martens, Roger Grosse; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2408-2417
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models
En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit Dhillon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2418-2426
Multi-instance multi-label learning in the presence of novel class instances
Anh Pham, Raviv Raich, Xiaoli Fern, Jesús Pérez Arriaga; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2427-2435
Entropy-Based Concentration Inequalities for Dependent Variables
Liva Ralaivola, Massih-Reza Amini; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2436-2444
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PU Learning for Matrix Completion
Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2445-2453
An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization
Necdet Aybat, Zi Wang, Garud Iyengar; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2454-2462
Sparse Subspace Clustering with Missing Entries
Congyuan Yang, Daniel Robinson, Rene Vidal; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2463-2472
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Moderated and Drifting Linear Dynamical Systems
Jinyan Guan, Kyle Simek, Ernesto Brau, Clayton Morrison, Emily Butler, Kobus Barnard; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2473-2482
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Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions
Taehoon Lee, Sungroh Yoon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2483-2492
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Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu-Xiang Wang, Stephen Fienberg, Alex Smola; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2493-2502
A trust-region method for stochastic variational inference with applications to streaming data
Lucas Theis, Matt Hoffman; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2503-2511
Inference in a Partially Observed Queuing Model with Applications in Ecology
Kevin Winner, Garrett Bernstein, Dan Sheldon; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2512-2520
Deterministic Independent Component Analysis
Ruitong Huang, Andras Gyorgy, Csaba Szepesvári; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2521-2530
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On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property
Maxime Gasse, Alexandre Aussem, Haytham Elghazel; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2531-2539
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Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
Roy Frostig, Rong Ge, Sham Kakade, Aaron Sidford; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2540-2548
A New Generalized Error Path Algorithm for Model Selection
Bin Gu, Charles Ling; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2549-2558
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