2 citations · 2 across the 5 of their papers we have counts for
6 papers
Anytime Contracts for AI Services
Boris Velasevic, Eden Saig, Eric Mazumdar
AI services are prone to moral hazard, as model providers can silently route requests to cheaper, lower-quality models, and the inherent stochasticity of model outputs makes this d…
Knowledge-Centric Self-Improvement
Xuefei Julie Wang, Lauren Hyoseo Yoon, Chengrui Qu +4
Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the agent's own code. This agent-centric view…
Training Generalizable Collaborative Agents via Strategic Risk Aversion
Chengrui Qu, Yizhou Zhang, Nicolas Lanzetti +1
Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals. Unfortunately, existing approaches to learning policies for such…
Provably Convergent Actor-Critic for MARL through Risk-aversion
Yizhou Zhang, Eric Mazumdar
Learning stationary policies in infinite-horizon general-sum Markov games (MGs) remains a fundamental open problem in Multi-Agent Reinforcement Learning (MARL). While stationary st…
Algorithmic Collective Action in Machine Learning
Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner +1
We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective…
A Note on Zeroth-Order Optimization on the Simplex
Tijana Zrnic, Eric Mazumdar
We construct a zeroth-order gradient estimator for a smooth function defined on the probability simplex. The proposed estimator queries the simplex only. We prove that projected gr…