9 papers
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…
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 ,…
Stow: Robotic Packing of Items into Fabric Pods
Nicolas Hudson, Josh Hooks, Rahul Warrier +34
This paper presents a compliant manipulation system capable of placing items onto densely packed shelves. The wide diversity of items and strict business requirements for high prod…
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…
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
Adhyyan Narang, Andrew Wagenmaker, Lillian Ratliff +1
In this paper, we study the non-asymptotic sample complexity for the pure exploration problem in contextual bandits and tabular reinforcement learning (RL): identifying an epsilon-…
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…