13 papers
Universal Dynamic Portfolios
Yu-Jie Zhang, Yu-Xiang Wang, Peng Zhao +1
Cover's Universal Portfolio (Cover, 1991) matches the performance of the best constant rebalanced portfolio in hindsight. We generalize this framework to compete with an arbitrary…
Batched Stochastic Linear Bandits with 1-Bit Communication Constraints
Ivan Lau, Daniel McMorrow, Kevin Jamieson +1
We study stochastic linear bandits under a natural combination of batching and communication constraints: the time horizon is partitioned into batches of equal size , and during…
Near-Optimal Regret in Adversarial Kernel Bandits
Yu-Jie Zhang, Hao Qiu, Jonathan Scarlett +1
We study the adversarial kernel bandit problem, in which the loss at each round is induced by an arbitrary bounded element of a reproducing kernel Hilbert space (RKHS). We propose…
On the Power of Adaptivity for -Best Arm Identification in Linear Bandits
Arnab Maiti, Yunbei Xu, Kevin Jamieson
We study the minimax sample complexity of -best arm identification in linear bandits. Given a compact action set that spans and an unknown…
On The Complexity of Best-Arm Identification in Non-Stationary Linear Bandits
Leo Maynard-Zhang, Zhihan Xiong, Kevin Jamieson +1
We study the fixed-budget best-arm identification (BAI) problem in non-stationary linear bandits. Concretely, given a fixed time budget , finite arm set $\mathcal{…
Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners
Arnab Maiti, Junyan Liu, Kevin Jamieson +1
We study the discrete Bertrand pricing game with a non-increasing demand function. The game has players who simultaneously choose prices from the set $\{1/k, 2/k, \ldots,…
Efficient Uncoupled Learning Dynamics with Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
Arnab Maiti, Claire Jie Zhang, Kevin Jamieson +3
In this paper, we study last-iterate convergence of learning algorithms in bilinear saddle-point problems, a preferable notion of convergence that captures the day-to-day behavior…
Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
Shinji Ito, Kevin Jamieson, Haipeng Luo +2
We study online learning in finite-horizon episodic Markov decision processes (MDPs) under the challenging aggregate bandit feedback model, where the learner observes only the cumu…
On the Universal Near Optimality of Hedge in Combinatorial Settings
Zhiyuan Fan, Arnab Maiti, Kevin Jamieson +2
In this paper, we study the classical Hedge algorithm in combinatorial settings. In each round, the learner selects a vector from a set ,…
Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
Junyan Liu, Arnab Maiti, Artin Tajdini +2
We initiate the study of a repeated principal-agent problem over a finite horizon , where a principal sequentially interacts with types of agents arriving in an advers…
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
Arnab Maiti, Zhiyuan Fan, Kevin Jamieson +2
In this paper, we study the online shortest path problem in directed acyclic graphs (DAGs) under bandit feedback against an adaptive adversary. Given a DAG with a sour…
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits
Arnab Maiti, Ross Boczar, Kevin Jamieson +1
We study the sample complexity of identifying the pure strategy Nash equilibrium (PSNE) in a two-player zero-sum matrix game with noise. Formally, we are given a stochastic model w…
Query-Efficient Algorithm to Find all Nash Equilibria in a Two-Player Zero-Sum Matrix Game
Arnab Maiti, Ross Boczar, Kevin Jamieson +1
We study the query complexity of finding the set of all Nash equilibria in two-player zero-sum matrix games. Fearnley and Savani (2016) s…