Papers
316 papers found
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies
Shlomi Weitzman, Sivan Sabato
Adversarial Contextual Bandits Go Kernelized
Gergely Neu, Julia Olkhovskaya, Sattar Vakili
Adversarial Online Collaborative Filtering
Stephen Pasteris, Fabio Vitale, Mark Herbster et al.
Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
Kabir Aladin Verchand, Mengqi Lou, Ashwin Pananjady
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
Jacob Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov et al.
Computation with Sequences of Assemblies in a Model of the Brain
Max Dabagia, Christos Papadimitriou, Santosh Vempala
Concentration of empirical barycenters in metric spaces
Victor-Emmanuel Brunel, Jordan Serres
Corruption-Robust Lipschitz Contextual Search
Shiliang Zuo
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
Shubhada Agrawal, Timothée Mathieu, Debabrota Basu et al.
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
Michael Menart, Enayat Ullah, Raman Arora et al.
Distances for Markov Chains, and Their Differentiation
Tristan Brugère, Zhengchao Wan, Yusu Wang
Dueling Optimization with a Monotone Adversary
Avrim Blum, Meghal Gupta, Gene Li et al.
Efficient Agnostic Learning with Average Smoothness
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
Importance-Weighted Offline Learning Done Right
Germano Gabbianelli, Gergely Neu, Matteo Papini
Improving Adaptive Online Learning Using Refined Discretization
Zhiyu Zhang, Heng Yang, Ashok Cutkosky et al.
Learning bounded-degree polytrees with known skeleton
Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya et al.
Learning Hypertrees From Shortest Path Queries
Shaun M Fallat, Valerii Maliuk, Seyed Ahmad Mojallal et al.
Learning Spanning Forests Optimally in Weighted Undirected Graphs with CUT queries
Hang Liao, Deeparnab Chakrabarty
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
Multiclass Learnability Does Not Imply Sample Compression
Chirag Pabbaraju
Multiclass Online Learnability under Bandit Feedback
Ananth Raman, Vinod Raman, Unique Subedi et al.
Near-continuous time Reinforcement Learning for continuous state-action spaces
Lorenzo Croissant, Marc Abeille, Bruno Bouchard
Not All Learnable Distribution Classes are Privately Learnable
Mark Bun, Gautam Kamath, Argyris Mouzakis et al.