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Found 15 papers, 1 papers with code
Learning in Games: Robustness of Fast Convergence
We show that learning algorithms satisfying a $\textit{low approximate regret}$ property experience fast convergence to approximate optimality in a large class of repeated games.
Small-loss bounds for online learning with partial information
We develop a black-box approach for such problems where the learner observes as feedback only losses of a subset of the actions that includes the selected action.
Competitive caching with machine learned advice
Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution as compared to an offline optimum.
Stochastic bandits robust to adversarial corruptions
We introduce a new model of stochastic bandits with adversarial corruptions which aims to capture settings where most of the input follows a stochastic pattern but some fraction of it can be adversarially changed to trick the algorithm, e. g., click fraud, fake reviews and email spam.
On preserving non-discrimination when combining expert advice
We study the interplay between sequential decision making and avoiding discrimination against protected groups, when examples arrive online and do not follow distributional assumptions.
Feedback graph regret bounds for Thompson Sampling and UCB
We study the stochastic multi-armed bandit problem with the graph-based feedback structure introduced by Mannor and Shamir.
Advancing subgroup fairness via sleeping experts
We demonstrate that the task of satisfying this guarantee for multiple overlapping groups is not straightforward and show that for the simple objective of unweighted average of false negative and false positive rate, satisfying this for overlapping populations can be statistically impossible even when we are provided predictors that perform well separately on each subgroup.
Corruption-robust exploration in episodic reinforcement learning
We initiate the study of multi-stage episodic reinforcement learning under adversarial corruptions in both the rewards and the transition probabilities of the underlying system extending recent results for the special case of stochastic bandits.
Contextual Search in the Presence of Adversarial Corruptions
We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying response model.
Bandits with adversarial scaling
We study "adversarial scaling", a multi-armed bandit model where rewards have a stochastic and an adversarial component.
Constrained episodic reinforcement learning in concave-convex and knapsack settings
We propose an algorithm for tabular episodic reinforcement learning with constraints.
Bayesian decision-making under misspecified priors with applications to meta-learning
We prove that the expected reward accrued by Thompson sampling (TS) with a misspecified prior differs by at most $\tilde{\mathcal{O}}(H^2 \epsilon)$ from TS with a well specified prior, where $\epsilon$ is the total-variation distance between priors and $H$ is the learning horizon.
Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems
We study decentralized multi-agent learning in bipartite queueing systems, a standard model for service systems.
Learning in Stackelberg Games with Non-myopic Agents
Although learning in Stackelberg games is well-understood when the agent is myopic, non-myopic agents pose additional complications.
Learning to Defer in Content Moderation: The Human-AI Interplay
The classical learning-theoretic way to capture this human-AI interplay is via the framework of learning to defer, where the algorithm has the option to defer a classification task to humans for a fixed cost and immediately receive feedback.
