Search Results for author: Wenshuo Guo

Found 18 papers, 4 papers with code

Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter

no code implementations23 May 2019 Wenshuo Guo, Nhat Ho, Michael. I. Jordan

First, we introduce the \emph{accelerated primal-dual randomized coordinate descent} (APDRCD) algorithm for computing the OT distance.

Robust Optimization for Fairness with Noisy Protected Groups

1 code implementation NeurIPS 2020 Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael. I. Jordan

Second, we introduce two new approaches using robust optimization that, unlike the naive approach of only relying on $\hat{G}$, are guaranteed to satisfy fairness criteria on the true protected groups G while minimizing a training objective.

Fairness

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models

no code implementations NeurIPS 2020 Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo

In machine learning applications such as ranking fairness or fairness over intersectional groups, one often encounters optimization problems with an extremely large number of constraints.

Fairness

A Variational Inequality Approach to Bayesian Regression Games

no code implementations24 Mar 2021 Wenshuo Guo, Michael I. Jordan, Tianyi Lin

Bayesian regression games are a special class of two-player general-sum Bayesian games in which the learner is partially informed about the adversary's objective through a Bayesian prior.

regression Stochastic Optimization

Multi-Source Causal Inference Using Control Variates

no code implementations30 Mar 2021 Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan

Across simulations and two case studies with real data, we show that this control variate can significantly reduce the variance of the ATE estimate.

Causal Inference Epidemiology +2

Test-time Collective Prediction

no code implementations NeurIPS 2021 Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan

An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points.

The Stereotyping Problem in Collaboratively Filtered Recommender Systems

no code implementations23 Jun 2021 Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg

First, we introduce a notion of joint accessibility, which measures the extent to which a set of items can jointly be accessed by users.

Collaborative Filtering Recommendation Systems

Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits

1 code implementation28 Jun 2021 Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady

We introduce the "inverse bandit" problem of estimating the rewards of a multi-armed bandit instance from observing the learning process of a low-regret demonstrator.

Experimental Design

Robust Learning of Optimal Auctions

no code implementations NeurIPS 2021 Wenshuo Guo, Michael I. Jordan, Manolis Zampetakis

The proposed algorithms operate beyond the setting of bounded distributions that have been studied in prior works, and are guaranteed to obtain a fraction $1-O(\alpha)$ of the optimal revenue under the true distribution when the distributions are MHR.

Partial Identification with Noisy Covariates: A Robust Optimization Approach

no code implementations22 Feb 2022 Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan

Directly adjusting for these imperfect measurements of the covariates can lead to biased causal estimates.

Causal Inference

No-Regret Learning in Partially-Informed Auctions

no code implementations22 Feb 2022 Wenshuo Guo, Michael I. Jordan, Ellen Vitercik

We formalize this problem as an online learning task where the goal is to have low regret with respect to a myopic oracle that has perfect knowledge of the distribution over items and the seller's masking function.

Off-Policy Evaluation with Policy-Dependent Optimization Response

no code implementations25 Feb 2022 Wenshuo Guo, Michael I. Jordan, Angela Zhou

Under this framework, a decision-maker's utility depends on the policy-dependent optimization, which introduces a fundamental challenge of \textit{optimization} bias even for the case of policy evaluation.

Causal Inference Decision Making +1

Mechanisms that Incentivize Data Sharing in Federated Learning

no code implementations10 Jul 2022 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 problems which are otherwise too data-intensive / expensive to be solved individually.

Federated Learning

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty

no code implementations20 Feb 2023 Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik

Customers with few relevant reviews may hesitate to make a purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews so that buyers can confidently estimate their values.

Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws

no code implementations23 Feb 2023 Kush Bhatia, Wenshuo Guo, Jacob Steinhardt

We specifically show that the well-studied problem of Gaussian process (GP) bandit optimization is a special case of our framework, and that our bounds either improve or are competitive with known regret guarantees for the Mat\'ern kernel.

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