Co-occurring keywords · papers mentioning both
Papers
Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
COLT 2026
Efficient Decentralized Learning of Generalized Quantal Response Equilibrium
UAI 2026
Learning from Equivalence Queries, Revisited
COLT 2026
Online Market Making and the Value of Observing the Order Book
COLT 2026
Policy Optimization for Adversarial Linear Mixture MDPs with Unknown Transitions and Bandit Feedback
UAI 2026
Truly Adapting to Adversarial Constraints in Constrained MABs
COLT 2026
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
JMLR 2025
Adaptive Threshold Sampling for Pure Exploration in Submodular Bandits
UAI 2025
Best of Both Worlds: Regret Minimization versus Minimax Play
ICML 2025
Clustering Items through Bandit Feedback: Finding the Right Feature out of Many
ICML 2025
Clustering with bandit feedback: breaking down the computation/information gap
ALT 2025
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
COLT 2025
Fixed-Budget Change Point Identification in Piecewise Constant Bandits
AISTATS 2025
Fixed-Confidence Multiple Change Point Identification under Bandit Feedback
ICML 2025
For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent
ALT 2025
Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback
COLT 2025
Learning Adversarial MDPs with Stochastic Hard Constraints
ICML 2025
Learning Imperfect Information Extensive-form Games with Last-iterate Convergence under Bandit Feedback
ICML 2025
Linear Submodular Maximization with Bandit Feedback
AISTATS 2025
Non-stochastic Budgeted Online Pricing with Semi-Bandit Feedback
AAAI 2025
Online Learning of Coalition Structures by Selfish Agents
AAAI 2025
Online Nonsubmodular Optimization with Delayed Feedback in the Bandit Setting
AAAI 2025
Policy Optimization for CMDPs with Bandit Feedback: Learning Stochastic and Adversarial Constraints
ICML 2025
Single-agent Poisoning Attacks Suffice to Ruin Multi-Agent Learning
ICLR 2025
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
ICML 2025