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Editors: Alina Beygelzimer, Daniel Hsu
Conference on Learning Theory 2019: Preface
; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1-2
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Inference under Information Constraints: Lower Bounds from Chi-Square Contraction
Jayadev Acharya, Clément L Canonne, Himanshu Tyagi; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3-17
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Learning in Non-convex Games with an Optimization Oracle
Naman Agarwal, Alon Gonen, Elad Hazan; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:18-29
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Learning to Prune: Speeding up Repeated Computations
Daniel Alabi, Adam Tauman Kalai, Katrina Liggett, Cameron Musco, Christos Tzamos, Ellen Vitercik; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:30-33
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Towards Testing Monotonicity of Distributions Over General Posets
Maryam Aliakbarpour, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:34-82
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Testing Mixtures of Discrete Distributions
Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:83-114
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Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT
Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:115-137
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Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes
Peter Auer, Pratik Gajane, Ronald Ortner; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:138-158
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Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information
Peter Auer, Yifang Chen, Pratik Gajane, Chung-Wei Lee, Haipeng Luo, Ronald Ortner, Chen-Yu Wei; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:159-163
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A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise
Francis Bach, Kfir Y Levy; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:164-194
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Learning Two Layer Rectified Neural Networks in Polynomial Time
Ainesh Bakshi, Rajesh Jayaram, David P Woodruff; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:195-268
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Private Center Points and Learning of Halfspaces
Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:269-282
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Lower bounds for testing graphical models: colorings and antiferromagnetic Ising models
Ivona Bezáková, Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:283-298
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Approximate Guarantees for Dictionary Learning
Aditya Bhaskara, Wai Ming Tai; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:299-317
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The Optimal Approximation Factor in Density Estimation
Olivier Bousquet, Daniel Kane, Shay Moran; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:318-341
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Sorted Top-k in Rounds
Mark Braverman, Jieming Mao, Yuval Peres; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:342-382
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Multi-armed Bandit Problems with Strategic Arms
Mark Braverman, Jieming Mao, Jon Schneider, S. Matthew Weinberg; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:383-416
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Universality of Computational Lower Bounds for Submatrix Detection
Matthew Brennan, Guy Bresler, Wasim Huleihel; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:417-468
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Optimal Average-Case Reductions to Sparse PCA: From Weak Assumptions to Strong Hardness
Matthew Brennan, Guy Bresler; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:469-470
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Learning rates for Gaussian mixtures under group action
Victor-Emmanuel Brunel; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:471-491
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Near-optimal method for highly smooth convex optimization
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:492-507
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Improved Path-length Regret Bounds for Bandits
Sébastien Bubeck, Yuanzhi Li, Haipeng Luo, Chen-Yu Wei; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:508-528
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Optimal Learning of Mallows Block Model
Robert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:529-532
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Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:533-557
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Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm
Tongyi Cao, Akshay Krishnamurthy; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:558-588
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A Rank-1 Sketch for Matrix Multiplicative Weights
Yair Carmon, John C Duchi, Sidford Aaron, Tian Kevin; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:589-623
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On the Computational Power of Online Gradient Descent
Vaggos Chatziafratis, Tim Roughgarden, Joshua R. Wang; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:624-662
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Active Regression via Linear-Sample Sparsification
Xue Chen, Eric Price; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:663-695
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A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal and Parameter-free
Yifang Chen, Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:696-726
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Faster Algorithms for High-Dimensional Robust Covariance Estimation
Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:727-757
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Testing Symmetric Markov Chains Without Hitting
Yeshwanth Cherapanamjeri, Peter L. Bartlett; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:758-785
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Fast Mean Estimation with Sub-Gaussian Rates
Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:786-806
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Vortices Instead of Equilibria in MinMax Optimization: Chaos and Butterfly Effects of Online Learning in Zero-Sum Games
Yun Kuen Cheung, Georgios Piliouras; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:807-834
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Pure entropic regularization for metrical task systems
Christian Coester, James R. Lee; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:835-848
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A near-optimal algorithm for approximating the John Ellipsoid
Michael B. Cohen, Ben Cousins, Yin Tat Lee, Xin Yang; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:849-873
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Artificial Constraints and Hints for Unbounded Online Learning
Ashok Cutkosky; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:874-894
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Combining Online Learning Guarantees
Ashok Cutkosky; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:895-913
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Learning from Weakly Dependent Data under Dobrushin’s Condition
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:914-928
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Space lower bounds for linear prediction in the streaming model
Yuval Dagan, Gil Kur, Ohad Shamir; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:929-954
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Computationally and Statistically Efficient Truncated Regression
Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:955-960
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Reconstructing Trees from Traces
Sami Davies, Miklos Z. Racz, Cyrus Rashtchian; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:961-978
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Is your function low dimensional?
Anindya De, Elchanan Mossel, Joe Neeman; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:979-993
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Computational Limitations in Robust Classification and Win-Win Results
Akshay Degwekar, Preetum Nakkiran, Vinod Vaikuntanathan; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:994-1028
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Fast determinantal point processes via distortion-free intermediate sampling
Michał Dereziński; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1029-1049
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Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Michał Dereziński, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1050-1069
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Communication and Memory Efficient Testing of Discrete Distributions
Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1070-1106
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Testing Identity of Multidimensional Histograms
Ilias Diakonikolas, Daniel M. Kane, John Peebles; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1107-1131
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Lower Bounds for Parallel and Randomized Convex Optimization
Jelena Diakonikolas, Cristóbal Guzmán; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1132-1157
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On the Performance of Thompson Sampling on Logistic Bandits
Shi Dong, Tengyu Ma, Benjamin Van Roy; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1158-1160
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Lower Bounds for Locally Private Estimation via Communication Complexity
John Duchi, Ryan Rogers; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1161-1191
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Sharp Analysis for Nonconvex SGD Escaping from Saddle Points
Cong Fang, Zhouchen Lin, Tong Zhang; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1192-1234
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Achieving the Bayes Error Rate in Stochastic Block Model by SDP, Robustly
Yingjie Fei, Yudong Chen; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1235-1269
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High probability generalization bounds for uniformly stable algorithms with nearly optimal rate
Vitaly Feldman, Jan Vondrak; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1270-1279
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Sum-of-squares meets square loss: Fast rates for agnostic tensor completion
Dylan J. Foster, Andrej Risteski; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1280-1318
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The Complexity of Making the Gradient Small in Stochastic Convex Optimization
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake Woodworth; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1319-1345
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Statistical Learning with a Nuisance Component
Dylan J. Foster, Vasilis Syrgkanis; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1346-1348
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On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA
Dan Garber; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1349-1373
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Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization
Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, César A. Uribe; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1374-1391
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Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives
Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1392-1393
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Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization
Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1394-1448
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Learning Ising Models with Independent Failures
Surbhi Goel, Daniel M. Kane, Adam R. Klivans; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1449-1469
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Learning Neural Networks with Two Nonlinear Layers in Polynomial Time
Surbhi Goel, Adam R. Klivans; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1470-1499
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When can unlabeled data improve the learning rate?
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Ruth Urner; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1500-1518
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Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature
Navin Goyal, Abhishek Shetty; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1519-1561
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Better Algorithms for Stochastic Bandits with Adversarial Corruptions
Anupam Gupta, Tomer Koren, Kunal Talwar; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1562-1578
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Tight analyses for non-smooth stochastic gradient descent
Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1579-1613
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Reasoning in Bayesian Opinion Exchange Networks Is PSPACE-Hard
Jan Hązła, Ali Jadbabaie, Elchanan Mossel, M. Amin Rahimian; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1614-1648
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How Hard is Robust Mean Estimation?
Samuel B. Hopkins, Jerry Li; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1649-1682
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A Robust Spectral Algorithm for Overcomplete Tensor Decomposition
Samuel B. Hopkins, Tselil Schramm, Jonathan Shi; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1683-1722
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Sample-Optimal Low-Rank Approximation of Distance Matrices
Pitor Indyk, Ali Vakilian, Tal Wagner, David P Woodruff; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1723-1751
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Making the Last Iterate of SGD Information Theoretically Optimal
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1752-1755
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Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation
Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1756-1771
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The implicit bias of gradient descent on nonseparable data
Ziwei Ji, Matus Telgarsky; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1772-1798
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An Optimal High-Order Tensor Method for Convex Optimization
Bo Jiang, Haoyue Wang, Shuzhong Zhang; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1799-1801
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Parameter-Free Online Convex Optimization with Sub-Exponential Noise
Kwang-Sung Jun, Francesco Orabona; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1802-1823
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Sample complexity of partition identification using multi-armed bandits
Sandeep Juneja, Subhashini Krishnasamy; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1824-1852
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Privately Learning High-Dimensional Distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan Ullman; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1853-1902
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On Communication Complexity of Classification Problems
Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1903-1943
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Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Belhal Karimi, Blazej Miasojedow, Eric Moulines, Hoi-To Wai; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1944-1974
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Discrepancy, Coresets, and Sketches in Machine Learning
Zohar Karnin, Edo Liberty; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1975-1993
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Bandit Principal Component Analysis
Wojciech Kotłowski, Gergely Neu; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1994-2024
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Contextual bandits with continuous actions: Smoothing, zooming, and adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2025-2027
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Distribution-Dependent Analysis of Gibbs-ERM Principle
Ilja Kuzborskij, Nicolò Cesa-Bianchi, Csaba Szepesvári; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2028-2054
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Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression
Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, Damek Davis; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2055-2110
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An Information-Theoretic Approach to Minimax Regret in Partial Monitoring
Tor Lattimore, Csaba Szepesvári; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2111-2139
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Solving Empirical Risk Minimization in the Current Matrix Multiplication Time
Yin Tat Lee, Zhao Song, Qiuyi Zhang; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2140-2157
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On Mean Estimation for General Norms with Statistical Queries
Jerry Li, Aleksandar Nikolov, Ilya Razenshteyn, Erik Waingarten; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2158-2172
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Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits
Yingkai Li, Yining Wang, Yuan Zhou; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2173-2174
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Sharp Theoretical Analysis for Nonparametric Testing under Random Projection
Meimei Liu, Zuofeng Shang, Guang Cheng; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2175-2209
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Combinatorial Algorithms for Optimal Design
Vivek Madan, Mohit Singh, Uthaipon Tantipongpipat, Weijun Xie; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2210-2258
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Nonconvex sampling with the Metropolis-adjusted Langevin algorithm
Oren Mangoubi, Nisheeth K Vishnoi; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2259-2293
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Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance
Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis Bach, Alessandro Rudi; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2294-2340
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Planting trees in graphs, and finding them back
Laurent Massoulié, Ludovic Stephan, Don Towsley; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2341-2371
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Uniform concentration and symmetrization for weak interactions
Andreas Maurer, Massimiliano Pontil; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2372-2387
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Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit
Song Mei, Theodor Misiakiewicz, Andrea Montanari; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2388-2464
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Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem
Nadav Merlis, Shie Mannor; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2465-2489
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Lipschitz Adaptivity with Multiple Learning Rates in Online Learning
Zakaria Mhammedi, Wouter M Koolen, Tim Van Erven; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2490-2511
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VC Classes are Adversarially Robustly Learnable, but Only Improperly
Omar Montasser, Steve Hanneke, Nathan Srebro; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2512-2530
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Affine Invariant Covariance Estimation for Heavy-Tailed Distributions
Dmitrii M. Ostrovskii, Alessandro Rudi; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2531-2550
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Stochastic Gradient Descent Learns State Equations with Nonlinear Activations
Samet Oymak; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2551-2579
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A Theory of Selective Prediction
Mingda Qiao, Gregory Valiant; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2580-2594
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Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon
Alexander Rakhlin, Xiyu Zhai; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2595-2623
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Classification with unknown class-conditional label noise on non-compact feature spaces
Henry Reeve, Kabán; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2624-2651
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The All-or-Nothing Phenomenon in Sparse Linear Regression
Galen Reeves, Jiaming Xu, Ilias Zadik; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2652-2663
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Depth Separations in Neural Networks: What is Actually Being Separated?
Itay Safran, Ronen Eldan, Ohad Shamir; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2664-2666
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How do infinite width bounded norm networks look in function space?
Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2667-2690
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Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks
Ohad Shamir; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2691-2713
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Learning Linear Dynamical Systems with Semi-Parametric Least Squares
Max Simchowitz, Ross Boczar, Benjamin Recht; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2714-2802
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Finite-Time Error Bounds For Linear Stochastic Approximation andTD Learning
R. Srikant, Lei Ying; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2803-2830
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Robustness of Spectral Methods for Community Detection
Ludovic Stephan, Laurent Massoulié; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2831-2860
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Maximum Entropy Distributions: Bit Complexity and Stability
Damian Straszak, Nisheeth K. Vishnoi; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2861-2891
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Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression
Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2892-2897
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Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2898-2933
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Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions
Adrien Taylor, Francis Bach; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2934-2992
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The Relative Complexity of Maximum Likelihood Estimation, MAP Estimation, and Sampling
Christopher Tosh, Sanjoy Dasgupta; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:2993-3035
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The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint
Stephen Tu, Benjamin Recht; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3036-3083
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Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen, Maxim Raginsky; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3084-3114
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Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds
Santosh Vempala, John Wilmes; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3115-3117
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Estimation of smooth densities in Wasserstein distance
Jonathan Weed, Quentin Berthet; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3118-3119
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Estimating the Mixing Time of Ergodic Markov Chains
Geoffrey Wolfer, Aryeh Kontorovich; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3120-3159
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Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate
Lijun Zhang, Zhi-Hua Zhou; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3160-3179
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Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning?
Amit Daniely, Vitaly Feldman; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3180-3184
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Open Problem: How fast can a multiclass test set be overfit?
Vitaly Feldman, Roy Frostig, Moritz Hardt; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3185-3189
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Open Problem: Do Good Algorithms Necessarily Query Bad Points?
Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3190-3193
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Open Problem: Risk of Ruin in Multiarmed Bandits
Filipo S. Perotto, Mathieu Bourgais, Bruno C. Silva, Laurent Vercouter; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3194-3197
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Open Problem: Monotonicity of Learning
Tom Viering, Alexander Mey, Marco Loog; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3198-3201
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Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory
Blake Woodworth, Nathan Srebro; Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3202-3210
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