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Found 9 papers, 1 papers with code
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
We revisit the problem of stochastic online learning with feedback graphs, with the goal of devising algorithms that are optimal, up to constants, both asymptotically and in finite time.
The Pareto Frontier of model selection for general Contextual Bandits
Recent progress in model selection raises the question of the fundamental limits of these techniques.
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
Our results show that optimistic algorithms can not achieve the information-theoretic lower bounds even in deterministic MDPs unless there is a unique optimal policy.
Corralling Stochastic Bandit Algorithms
We study the problem of corralling stochastic bandit algorithms, that is combining multiple bandit algorithms designed for a stochastic environment, with the goal of devising a corralling algorithm that performs almost as well as the best base algorithm.
Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives
In this paper, we revisit the problem of private stochastic convex optimization.
Bandits with Feedback Graphs and Switching Costs
We give a new algorithm whose regret guarantee depends only on the domination number of the graph.
Policy Regret in Repeated Games
We revisit the notion of policy regret and first show that there are online learning settings in which policy regret and external regret are incompatible: any sequence of play that achieves a favorable regret with respect to one definition must do poorly with respect to the other.
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
We study the statistical and computational aspects of kernel principal component analysis using random Fourier features and show that under mild assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve $O(1/\epsilon^2)$ sample complexity.
Stochastic Approximation for Canonical Correlation Analysis
We propose novel first-order stochastic approximation algorithms for canonical correlation analysis (CCA).
