Search Results for author: Wenshuo Guo

Found 16 papers, 4 papers with code

Off-Policy Evaluation with Policy-Dependent Optimization Response

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

In 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

No-Regret Learning in Partially-Informed Auctions

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

When the distribution over items is known to the buyer and the mask is a SimHash function mapping $\mathbb{R}^d$ to $\{0, 1\}^{\ell}$, our algorithm has regret $\tilde {\mathcal{O}}((Td\ell)^{\frac{1}{2}})$.

online learning

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

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

no code implementations29 Sep 2021 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.

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.

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

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

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.

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

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.

Stochastic Optimization

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

Neural Kernels Without Tangents

1 code implementation ICML 2020 Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelley, Benjamin Recht

We investigate the connections between neural networks and simple building blocks in kernel space.

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

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.

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