Papers
Empirical bounds for functions with weak interactions
Andreas Maurer, Massimiliano Pontil
Small-loss bounds for online learning with partial information
Thodoris Lykouris, Karthik Sridharan, Éva Tardos
Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk
Paul Hand, Vladislav Voroninski
Learning Patterns for Detection with Multiscale Scan Statistics
James Sharpnack
Best of both worlds: Stochastic & adversarial best-arm identification
Yasin Abbasi-Yadkori, Peter Bartlett, Victor Gabillon et al.
Hardness of Learning Noisy Halfspaces using Polynomial Thresholds
Arnab Bhattacharyya, Suprovat Ghoshal, Rishi Saket
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability
Belinda Tzen, Tengyuan Liang, Maxim Raginsky
Time-Space Tradeoffs for Learning Finite Functions from Random Evaluations, with Applications to Polynomials
Paul Beame, Shayan Oveis Gharan, Xin Yang
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms
Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
Incentivizing Exploration by Heterogeneous Users
Bangrui Chen, Peter Frazier, David Kempe
Log-concave sampling: Metropolis-Hastings algorithms are fast!
Raaz Dwivedi, Yuansi Chen, Martin J Wainwright et al.
Lower Bounds for Higher-Order Convex Optimization
Naman Agarwal, Elad Hazan
Nonstochastic Bandits with Composite Anonymous Feedback
Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour
Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data
Victor Chernozhukov, Kaspar Wüthrich, Zhu Yinchu
Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models
Jean Barbier, Florent Krzakala, Nicolas Macris et al.
Private Sequential Learning
John Tsitsiklis, Kuang Xu, Zhi Xu
Fitting a Putative Manifold to Noisy Data
Charles Fefferman, Sergei Ivanov, Yaroslav Kurylev et al.
Averaging Stochastic Gradient Descent on Riemannian Manifolds
Nilesh Tripuraneni, Nicolas Flammarion, Francis Bach et al.
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints
Wenlong Mou, Liwei Wang, Xiyu Zhai et al.
Accelerating Stochastic Gradient Descent for Least Squares Regression
Prateek Jain, Sham M. Kakade, Rahul Kidambi et al.
Calibrating Noise to Variance in Adaptive Data Analysis
Vitaly Feldman, Thomas Steinke
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods
Yan Shuo Tan, Roman Vershynin
Active Tolerant Testing
Avrim Blum, Lunjia Hu
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification
Max Simchowitz, Horia Mania, Stephen Tu et al.