33 citations · 49 across the 9 of their papers we have counts for
14 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…
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…
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…
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…