1 citations · 1 across the 6 of their papers we have counts for
6 papers
Locally Adaptive Multi-Objective Learning
Jivat Neet Kaur, Isaac Gibbs, Michael I. Jordan
We consider the general problem of learning a predictor that satisfies multiple objectives of interest simultaneously, a broad framework that captures a range of specific learning…
How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics
Yurong Chen, Yu He, Michael I. Jordan +1
Standard methods for aligning large language models with human preferences learn from pairwise comparisons among sampled candidate responses and regularize toward a reference polic…
A Collectivist, Economic Perspective on AI
Michael I. Jordan
Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word ``intellige…
Marketplace Operators Can Induce Competitive Pricing
Tiffany Ding, Dominique Perrault-Joncas, Orit Ronen +4
As e-commerce marketplaces continue to grow in popularity, it has become increasingly important to understand the role and impact of marketplace operators on competition and social…
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints
Jordan Lekeufack, Michael I. Jordan
We study Online Convex Optimization (OCO) with adversarial constraints, where an online algorithm must make sequential decisions to minimize both convex loss functions and cumulati…
Reduced-Rank Multi-objective Policy Learning and Optimization
Ezinne Nwankwo, Michael I. Jordan, Angela Zhou
Evaluating the causal impacts of possible interventions is crucial for informing decision-making, especially towards improving access to opportunity. However, if causal effects are…