1 citations · 3 across the 19 of their papers we have counts for
4 papers · 1 filter
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…
Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
Shinji Ito, Haipeng Luo, Arnab Maiti +2
Learning to play zero-sum games is a fundamental problem in game theory and machine learning. While significant progress has been made in minimizing external regret in the self-pla…
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…