Papers
1,396 papers found
Tighter PAC-Bayes Bounds Through Coin-Betting
Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij et al.
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
Dylan J. Foster, Noah Golowich, Yanjun Han
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality
Alireza Mousavi-Hosseini, Tyler K. Farghly, Ye He et al.
U-Calibration: Forecasting for an Unknown Agent
Bobby Kleinberg, Renato Paes Leme, Jon Schneider et al.
Uniqueness of BP fixed point for the Potts model and applications to community detection
Yuzhou Gu, Yury Polyanskiy
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets
Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay
Universal Rates for Multiclass Learning
Steve Hanneke, Shay Moran, Qian Zhang
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms
Aniket Das, Dheeraj M. Nagaraj, Anant Raj
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
Heyang Zhao, Jiafan He, Dongruo Zhou et al.
VO$Q$L: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation
Alekh Agarwal, Yujia Jin, Tong Zhang
Weak Recovery Threshold for the Hypergraph Stochastic Block Model
Yuzhou Gu, Yury Polyanskiy
Zeroth-order Optimization with Weak Dimension Dependency
Pengyun Yue, Long Yang, Cong Fang et al.
A bounded-noise mechanism for differential privacy
Yuval Dagan, Gil Kur
Accelerated SGD for Non-Strongly-Convex Least Squares
Aditya Varre, Nicolas Flammarion
Adaptive Bandit Convex Optimization with Heterogeneous Curvature
Haipeng Luo, Mengxiao Zhang, Peng Zhao
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds
Shinji Ito, Taira Tsuchiya, Junya Honda
Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev
Sinho Chewi, Murat A Erdogdu, Mufan Li et al.
An Efficient Minimax Optimal Estimator For Multivariate Convex Regression
Gil Kur, Eli Putterman
Approximate Cluster Recovery from Noisy Labels
Buddhima Gamlath, Silvio Lattanzi, Ashkan Norouzi-Fard et al.
A Private and Computationally-Efficient Estimator for Unbounded Gaussians
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal et al.
A Sharp Memory-Regret Trade-off for Multi-Pass Streaming Bandits
Arpit Agarwal, Sanjeev Khanna, Prathamesh Patil
Assemblies of neurons learn to classify well-separated distributions
Max Dabagia, Santosh S Vempala, Christos Papadimitriou
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
Spencer Frei, Niladri S Chatterji, Peter Bartlett