17 citations · 39 across the 10 of their papers we have counts for
7 papers · 1 filter
Strategic Classification under Unknown Personalized Manipulation
Han Shao, Avrim Blum, Omar Montasser
We study the fundamental mistake bound and sample complexity in the strategic classification, where agents can strategically manipulate their feature vector up to an extent in orde…
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Han Shao, Lee Cohen, Avrim Blum +3
In classic reinforcement learning (RL) and decision making problems, policies are evaluated with respect to a scalar reward function, and all optimal policies are the same with reg…
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
Avrim Blum, Nika Haghtalab, Richard Lanas Phillips +1
In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents. However, little is known about ho…
Robust learning under clean-label attack
Avrim Blum, Steve Hanneke, Jian Qian +1
We study the problem of robust learning under clean-label data-poisoning attacks, where the attacker injects (an arbitrary set of) correctly-labeled examples to the training set to…
Online Learning with Primary and Secondary Losses
Avrim Blum, Han Shao
We study the problem of online learning with primary and secondary losses. For example, a recruiter making decisions of which job applicants to hire might weigh false positives and…
Stochastic Bandits with Vector Losses: Minimizing -Norm of Relative Losses
Xuedong Shang, Han Shao, Jian Qian
Multi-armed bandits are widely applied in scenarios like recommender systems, for which the goal is to maximize the click rate. However, more factors should be considered, e.g., us…
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
Han Shao, Xiaotian Yu, Irwin King +1
In linear stochastic bandits, it is commonly assumed that payoffs are with sub-Gaussian noises. In this paper, under a weaker assumption on noises, we study the problem of \underli…