Optimization
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
On the conceptual front, we identify connections between these three problems and present a framework that brings all these challenges under a common umbrella. We then present a (randomized) algorithm for reviewer assignment that can optimally solve the reviewer-assignment problem under any given constraints on the probability of assignment for any reviewer-paper pair.
FERERO: AFlexibleFrameworkfor Preference-GuidedMulti-ObjectiveLearning
To solve this problem, convergent algorithms are developed with both single-loop and stochastic variants. Notably, this is the firstsingle-loop primalalgorithmforconstrained optimization toourknowledge.
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
Meta learning is a promising paradigm to enable skill transfer across tasks.
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm Qi Wang
Meta learning is a promising paradigm to enable skill transfer across tasks.
Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
We study the infinite-horizon restless bandit problem with the average reward criterion, in both discrete-time and continuous-time settings.
