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ORCID31st COLT 2018: Stockholm, Sweden
- Sébastien Bubeck, Vianney Perchet, Philippe Rigollet:
Conference On Learning Theory, COLT 2018, Stockholm, Sweden, 6-9 July 2018. Proceedings of Machine Learning Research 75, PMLR 2018
Preface
- Sébastien Bubeck, Philippe Rigollet:
Conference on Learning Theory 2018: Preface. 1
Best Paper Awards
- Yuanzhi Li, Tengyu Ma, Hongyang Zhang:
Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations. 2-47 - Matthew S. Brennan, Guy Bresler, Wasim Huleihel:
Reducibility and Computational Lower Bounds for Problems with Planted Sparse Structure. 48-166 - Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Logistic Regression: The Importance of Being Improper. 167-208
Regular Papers
- Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos:
Actively Avoiding Nonsense in Generative Models. 209-227 - Vladimir Kolmogorov:
A Faster Approximation Algorithm for the Gibbs Partition Function. 228-249 - Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Exponential Convergence of Testing Error for Stochastic Gradient Methods. 250-296 - Noah Golowich, Alexander Rakhlin, Ohad Shamir:
Size-Independent Sample Complexity of Neural Networks. 297-299 - Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan:
Underdamped Langevin MCMC: A non-asymptotic analysis. 300-323 - Zalan Borsos, Andreas Krause, Kfir Y. Levy:
Online Variance Reduction for Stochastic Optimization. 324-357 - Johannes Kirschner, Andreas Krause:
Information Directed Sampling and Bandits with Heteroscedastic Noise. 358-384 - Constantinos Daskalakis, Nishanth Dikkala, Nick Gravin:
Testing Symmetric Markov Chains From a Single Trajectory. 385-409 - Ahmed El Alaoui, Michael I. Jordan:
Detection limits in the high-dimensional spiked rectangular model. 410-438 - Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht:
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. 439-473 - Avrim Blum, Lunjia Hu:
Active Tolerant Testing. 474-497 - Yan Shuo Tan, Roman Vershynin:
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods. 498-534 - Vitaly Feldman, Thomas Steinke:
Calibrating Noise to Variance in Adaptive Data Analysis. 535-544 - Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent for Least Squares Regression. 545-604 - Wenlong Mou, Liwei Wang, Xiyu Zhai, Kai Zheng:
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints. 605-638 - Dmitry Yarotsky:
Optimal approximation of continuous functions by very deep ReLU networks. 639-649 - Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. 650-687 - Charles Fefferman, Sergei Ivanov, Yaroslav Kurylev, Matti Lassas, Hariharan Narayanan:
Fitting a Putative Manifold to Noisy Data. 688-720 - John N. Tsitsiklis, Kuang Xu, Zhi Xu:
Private Sequential Learning. 721-727 - Jean Barbier, Florent Krzakala
, Nicolas Macris, Léo Miolane, Lenka Zdeborová:
Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models. 728-731 - Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu:
Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data. 732-749 - Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour:
Nonstochastic Bandits with Composite Anonymous Feedback. 750-773 - Naman Agarwal, Elad Hazan:
Lower Bounds for Higher-Order Convex Optimization. 774-792 - Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright, Bin Yu:
Log-concave sampling: Metropolis-Hastings algorithms are fast! 793-797 - Bangrui Chen, Peter I. Frazier, David Kempe:
Incentivizing Exploration by Heterogeneous Users. 798-818 - Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. 819-842 - Paul Beame, Shayan Oveis Gharan, Xin Yang:
Time-Space Tradeoffs for Learning Finite Functions from Random Evaluations, with Applications to Polynomials. 843-856 - Belinda Tzen, Tengyuan Liang, Maxim Raginsky:
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. 857-875 - Arnab Bhattacharyya, Suprovat Ghoshal, Rishi Saket:
Hardness of Learning Noisy Halfspaces using Polynomial Thresholds. 876-917 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek, Michal Valko:
Best of both worlds: Stochastic & adversarial best-arm identification. 918-949 - James Sharpnack:
Learning Patterns for Detection with Multiscale Scan Statistics. 950-969 - Paul Hand, Vladislav Voroninski:
Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk. 970-978 - Thodoris Lykouris, Karthik Sridharan, Éva Tardos:
Small-loss bounds for online learning with partial information. 979-986 - Andreas Maurer, Massimiliano Pontil:
Empirical bounds for functions with weak interactions. 987-1010 - Shiva Prasad Kasiviswanathan, Mark Rudelson:
Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression. 1011-1041 - Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. 1042-1085 - Oren Mangoubi, Nisheeth K. Vishnoi:
Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing. 1086-1124 - Yuanzhi Li, Yingyu Liang:
Learning Mixtures of Linear Regressions with Nearly Optimal Complexity. 1125-1144 - Yuval Dagan, Ohad Shamir:
Detecting Correlations with Little Memory and Communication. 1145-1198 - Gal Dalal, Gugan Thoppe, Balázs Szörényi, Shie Mannor:
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning. 1199-1233 - Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart:
Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities. 1234-1262 - Chen-Yu Wei, Haipeng Luo:
More Adaptive Algorithms for Adversarial Bandits. 1263-1291 - Yin Tat Lee, Aaron Sidford, Santosh S. Vempala:
Efficient Convex Optimization with Membership Oracles. 1292-1294 - Asaf B. Cassel, Shie Mannor, Assaf Zeevi:
A General Approach to Multi-Armed Bandits Under Risk Criteria. 1295-1306 - Tim Roughgarden, Joshua R. Wang:
An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization. 1307-1325 - Vishesh Jain, Frederic Koehler, Elchanan Mossel:
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity. 1326-1347 - Mikhail Belkin:
Approximation beats concentration? An approximation view on inference with smooth radial kernels. 1348-1361 - Yu Cheng, Rong Ge:
Non-Convex Matrix Completion Against a Semi-Random Adversary. 1362-1394 - Vishesh Jain, Frederic Koehler, Elchanan Mossel:
The Vertex Sample Complexity of Free Energy is Polynomial. 1395-1419 - Adam R. Klivans, Pravesh K. Kothari, Raghu Meka:
Efficient Algorithms for Outlier-Robust Regression. 1420-1430 - Ana Busic, Sean P. Meyn:
Action-Constrained Markov Decision Processes With Kullback-Leibler Cost. 1431-1444 - Marco Mondelli, Andrea Montanari:
Fundamental Limits of Weak Recovery with Applications to Phase Retrieval. 1445-1450 - Oliver Hinder:
Cutting plane methods can be extended into nonconvex optimization. 1451-1454 - Sanjeev Arora, Wei Hu, Pravesh K. Kothari:
An Analysis of the t-SNE Algorithm for Data Visualization. 1455-1462 - Andrea Locatelli, Alexandra Carpentier:
Adaptivity to Smoothness in X-armed bandits. 1463-1492 - Ashok Cutkosky, Francesco Orabona:
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces. 1493-1529 - Michela Meister, Gregory Valiant:
A Data Prism: Semi-verified learning in the small-alpha regime. 1530-1546 - Ido Nachum, Jonathan Shafer, Amir Yehudayoff:
A Direct Sum Result for the Information Complexity of Learning. 1547-1568 - Jason M. Altschuler, Kunal Talwar:
Online learning over a finite action set with limited switching. 1569-1573 - Niangjun Chen, Gautam Goel, Adam Wierman:
Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent. 1574-1594 - Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang:
Faster Rates for Convex-Concave Games. 1595-1625 - David Durfee, Kevin A. Lai, Saurabh Sawlani:
$\ell_1$ Regression using Lewis Weights Preconditioning and Stochastic Gradient Descent. 1626-1656 - Guy Bresler, Dheeraj Nagaraj:
Optimal Single Sample Tests for Structured versus Unstructured Network Data. 1657-1690 - Jalaj Bhandari, Daniel Russo, Raghav Singal:
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation. 1691-1692 - Cynthia Dwork, Vitaly Feldman:
Privacy-preserving Prediction. 1693-1702 - Hongyi Zhang, Suvrit Sra:
An Estimate Sequence for Geodesically Convex Optimization. 1703-1723 - Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
The Externalities of Exploration and How Data Diversity Helps Exploitation. 1724-1738 - Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford:
Efficient Contextual Bandits in Non-stationary Worlds. 1739-1776 - Espen Bernton:
Langevin Monte Carlo and JKO splitting. 1777-1798 - Nina Holden, Robin Pemantle, Yuval Peres:
Subpolynomial trace reconstruction for random strings \{and arbitrary deletion probability. 1799-1840 - Jonathan Weed:
An explicit analysis of the entropic penalty in linear programming. 1841-1855 - Chicheng Zhang:
Efficient active learning of sparse halfspaces. 1856-1880 - Samory Kpotufe, Guillaume Martinet:
Marginal Singularity, and the Benefits of Labels in Covariate-Shift. 1882-1886 - Rishabh Dudeja, Daniel Hsu:
Learning Single-Index Models in Gaussian Space. 1887-1930 - Yingjie Fei, Yudong Chen:
Hidden Integrality of SDP Relaxations for Sub-Gaussian Mixture Models. 1931-1965 - Jason M. Klusowski, Yihong Wu:
Counting Motifs with Graph Sampling. 1966-2011 - Piotr Indyk, Tal Wagner:
Approximate Nearest Neighbors in Limited Space. 2012-2036 - Cheng Mao, Ashwin Pananjady, Martin J. Wainwright:
Breaking the $1/\sqrtn$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time. 2037-2042 - Daniel Alabi, Nicole Immorlica, Adam Kalai:
Unleashing Linear Optimizers for Group-Fair Learning and Optimization. 2043-2066 - Dirk van der Hoeven, Tim van Erven, Wojciech Kotlowski:
The Many Faces of Exponential Weights in Online Learning. 2067-2092 - Andre Wibisono:
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem. 2093-3027 - Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Online Learning: Sufficient Statistics and the Burkholder Method. 3028-3064 - John C. Duchi, Feng Ruan, Chulhee Yun:
Minimax Bounds on Stochastic Batched Convex Optimization. 3065-3162 - Yanjun Han, Ayfer Özgür, Tsachy Weissman:
Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints. 3163-3188 - Yanjun Han, Jiantao Jiao, Tsachy Weissman:
Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance. 3189-3221 - Gergely Neu, Lorenzo Rosasco:
Iterate Averaging as Regularization for Stochastic Gradient Descent. 3222-3242 - Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli:
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. 3243-3270 - Themis Gouleakis, Christos Tzamos, Manolis Zampetakis:
Certified Computation from Unreliable Datasets. 3271-3294
Open Problems
- Nan Jiang, Alekh Agarwal:
Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon. 3395-3398 - Elad Hazan, Roi Livni:
Open problem: Improper learning of mixtures of Gaussians. 3399-3402
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