no code implementations • 23 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.
no code implementations • 20 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.
no code implementations • 10 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.
no code implementations • 25 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.
no code implementations • 22 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.
no code implementations • 22 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.
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.
1 code implementation • 28 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.
no code implementations • 23 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.
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.
no code implementations • 11 Jun 2021 • Wenshuo Guo, Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I. Jordan, Ion Stoica
The allocations at a CE are Pareto efficient and fair.
no code implementations • 30 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.
no code implementations • 24 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.
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.
1 code implementation • 7 Nov 2020 • Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, Michael I. Jordan
We observe that offline metrics are correlated with online performance over a range of environments.
2 code implementations • 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.
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.
no code implementations • 23 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.