212 citations · 419 across the 46 of their papers we have counts for
21 papers · 1 filter
Incentive-Aware Recommender Systems in Two-Sided Markets
Xiaowu Dai, Wenlu Xu, Yuan Qi +1
Online platforms in the Internet Economy commonly incorporate recommender systems that recommend products (or "arms") to users (or "agents"). A key challenge in this domain arises…
The Sample Complexity of Online Contract Design
Banghua Zhu, Stephen Bates, Zhuoran Yang +3
We study the hidden-action principal-agent problem in an online setting. In each round, the principal posts a contract that specifies the payment to the agent based on each outcome…
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization
Tianyi Lin, Zeyu Zheng, Michael I. Jordan
Nonsmooth nonconvex optimization problems broadly emerge in machine learning and business decision making, whereas two core challenges impede the development of efficient solution…
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
Chris Junchi Li, Angela Yuan, Gauthier Gidel +2
We propose a new first-order optimization algorithm -- AcceleratedGradient-OptimisticGradient (AG-OG) Descent Ascent -- for separable convex-concave minimax optimization. The main…
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints
Tatjana Chavdarova, Tong Yang, Matteo Pagliardini +1
Yang et al. (2023) recently showed how to use first-order gradient methods to solve general variational inequalities (VIs) under a limiting assumption that analytic solutions of sp…
On-Demand Sampling: Learning Optimally from Multiple Distributions
Nika Haghtalab, Michael I. Jordan, Eric Zhao
Social and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collab…
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design
Rui Ai, Boxiang Lyu, Zhaoran Wang +2
We study reserve price optimization in multi-phase second price auctions, where the seller's prior actions affect the bidders' later valuations through a Markov Decision Process (M…
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Zixiang Chen, Chris Junchi Li, Angela Yuan +2
With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we pro…
On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization
Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
In this paper, we present several new results on minimizing a nonsmooth and nonconvex function under a Lipschitz condition. Recent work shows that while the classical notion of Cla…
Data-Driven Influence Functions for Optimization-Based Causal Inference
Michael I. Jordan, Yixin Wang, Angela Zhou
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inferen…
Valid Inference After Causal Discovery
Paula Gradu, Tijana Zrnic, Yixin Wang +1
Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challeng…
Breaking Feedback Loops in Recommender Systems with Causal Inference
Karl Krauth, Yixin Wang, Michael I. Jordan
Recommender systems play a key role in shaping modern web ecosystems. These systems alternate between (1) making recommendations (2) collecting user responses to these recommendati…
Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata
Tatjana Chavdarova, Ya-Ping Hsieh, Michael I. Jordan
Algorithms that solve zero-sum games, multi-objective agent objectives, or, more generally, variational inequality (VI) problems are notoriously unstable on general problems. Owing…
Mechanisms that Incentivize Data Sharing in Federated Learning
Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan
Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve prob…
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method
Tong Yang, Michael I. Jordan, Tatjana Chavdarova
We develop an interior-point approach to solve constrained variational inequality (cVI) problems. Inspired by the efficacy of the alternating direction method of multipliers (ADMM)…
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
Tianyi Lin, Aldo Pacchiano, Yaodong Yu +1
Motivated by applications to online learning in sparse estimation and Bayesian optimization, we consider the problem of online unconstrained nonsubmodular minimization with delayed…
Perseus: A Simple and Optimal High-Order Method for Variational Inequalities
Tianyi Lin, Michael. I. Jordan
This paper settles an open and challenging question pertaining to the design of simple and optimal high-order methods for solving smooth and monotone variational inequalities (VIs)…
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
Alessandro Barp, Lancelot Da Costa, Guilherme França +4
In this chapter, we identify fundamental geometric structures that underlie the problems of sampling, optimisation, inference and adaptive decision-making. Based on this identifica…
Partial Identification with Noisy Covariates: A Robust Optimization Approach
Wenshuo Guo, Mingzhang Yin, Yixin Wang +1
Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, o…
Off-Policy Evaluation with Policy-Dependent Optimization Response
Wenshuo Guo, Michael I. Jordan, Angela Zhou
The intersection of causal inference and machine learning for decision-making is rapidly expanding, but the default decision criterion remains an \textit{average} of individual cau…
Reinforcement Learning with Heterogeneous Data: Estimation and Inference
Elynn Y. Chen, Rui Song, Michael I. Jordan
Reinforcement Learning (RL) has the promise of providing data-driven support for decision-making in a wide range of problems in healthcare, education, business, and other domains.…