42 citations · 120 across the 22 of their papers we have counts for
22 papers
Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws
Kush Bhatia, Wenshuo Guo, Jacob Steinhardt
Specifying reward functions for complex tasks like object manipulation or driving is challenging to do by hand. Reward learning seeks to address this by learning a reward model usi…
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy +1
In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type -- such as height for…
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…
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…
No-Regret Learning in Partially-Informed Auctions
Wenshuo Guo, Michael I. Jordan, Ellen Vitercik
Auctions with partially-revealed information about items are broadly employed in real-world applications, but the underlying mechanisms have limited theoretical support. In this wo…
Polynomial-Time Key Recovery Attack on the Lau-Tan Cryptosystem Based on Gabidulin Codes
Wenshuo Guo, Fang-Wei Fu
This paper presents a key recovery attack on the cryptosystem proposed by Lau and Tan in a talk at ACISP 2018. The Lau-Tan cryptosystem uses Gabidulin codes as the underlying decod…
Robust Learning of Optimal Auctions
Wenshuo Guo, Michael I. Jordan, Manolis Zampetakis
We study the problem of learning revenue-optimal multi-bidder auctions from samples when the samples of bidders' valuations can be adversarially corrupted or drawn from distributio…
Semilinear Transformations in Coding Theory: A New Technique in Code-Based Cryptography
Wenshuo Guo, Fang-Wei Fu
This paper presents a new technique for disturbing the algebraic structure of linear codes in code-based cryptography. This is a new attempt to exploit Gabidulin codes in the McEli…
Two Public-Key Cryptosystems Based on Expanded Gabidulin Codes
Wenshuo Guo, Fang-Wei Fu
This paper presents two public key cryptosystems based on the so-called expanded Gabidulin codes, which are constructed by expanding Gabidulin codes over the base field. Exploiting…
Test-time Collective Prediction
Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates +1
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. Agents wish to b…
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover +2
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. Existing appr…
The Stereotyping Problem in Collaboratively Filtered Recommender Systems
Wenshuo Guo, Karl Krauth, Michael I. Jordan +1
Recommender systems play a crucial role in mediating our access to online information. We show that such algorithms induce a particular kind of stereotyping: if preferences for a s…
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback
Wenshuo Guo, Kirthevasan Kandasamy, Joseph E Gonzalez +2
The sharing of scarce resources among multiple rational agents is one of the classical problems in economics. In exchange economies, which are used to model such situations, agents…
Two modifications for Loidreau's code-based cryptosystem
Wenshuo Guo, Fangwei Fu
This paper presents two modifications for Loidreau's code-based cryptosystem. Loidreau's cryptosystem is a rank metric code-based cryptosystem constructed by using Gabidulin codes…
Multi-Source Causal Inference Using Control Variates
Wenshuo Guo, Serena Wang, Peng Ding +2
While many areas of machine learning have benefited from the increasing availability of large and varied datasets, the benefit to causal inference has been limited given the strong…
A Variational Inequality Approach to Bayesian Regression Games
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…
Do Offline Metrics Predict Online Performance in Recommender Systems?
Karl Krauth, Sarah Dean, Alex Zhao +4
Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences ch…
Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty
Wenshuo Guo, Mihaela Curmei, Serena Wang +2
We study the problem of finding equilibrium strategies in multi-agent games with incomplete payoff information, where the payoff matrices are only known to the players up to some b…
Neural Kernels Without Tangents
Vaishaal Shankar, Alex Fang, Wenshuo Guo +4
We investigate the connections between neural networks and simple building blocks in kernel space. In particular, using well established feature space tools such as direct sum, ave…
Robust Optimization for Fairness with Noisy Protected Groups
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan +3
Many existing fairness criteria for machine learning involve equalizing some metric across protected groups such as race or gender. However, practitioners trying to audit or enforc…
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter
Wenshuo Guo, Nhat Ho, Michael I. Jordan
We provide theoretical complexity analysis for new algorithms to compute the optimal transport (OT) distance between two discrete probability distributions, and demonstrate their f…