Spin Model of Two Random Walkers in Complex Networks. - Wenshuo Guo; Juntao Wang 002; Kwok Yip Szeto (COMPLEX NETWORKS, 2017)

Minimization of Systemic Risk for Directed Network Using Genetic Algorithm. - Wenshuo Guo; Kwok Yip Szeto (EvoApplications (1), 2017)

Optimization of financial network stability by genetic algorithm. - Juntao Wang 002; Wenshuo Guo; Kwok Yip Szeto (WI, 2017)

Accelerated Primal-Dual Coordinate Descent for Computational Optimal Transport. - Wenshuo Guo; Nhat Ho; Michael I. Jordan (CoRR, 2019)

Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. - Wenshuo Guo; Nhat Ho; Michael I. Jordan (AISTATS, 2020)

Neural Kernels Without Tangents. - Vaishaal Shankar; Alex Fang; Wenshuo Guo; Sara Fridovich-Keil; Jonathan Ragan-Kelley; Ludwig Schmidt; Benjamin Recht (ICML, 2020)

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models. - Harikrishna Narasimhan; Andrew Cotter; Yichen Zhou; Serena Lutong Wang; Wenshuo Guo (NeurIPS, 2020)

Robust Optimization for Fairness with Noisy Protected Groups. - Serena Lutong Wang; Wenshuo Guo; Harikrishna Narasimhan; Andrew Cotter; Maya R. Gupta; Michael I. Jordan (NeurIPS, 2020)

Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty. - Wenshuo Guo; Mihaela Curmei; Serena Lutong Wang; Benjamin Recht; Michael I. Jordan (CoRR, 2020)

Do Offline Metrics Predict Online Performance in Recommender Systems? - Karl Krauth; Sarah Dean; Alex Zhao; Wenshuo Guo; Mihaela Curmei; Benjamin Recht; Michael I. Jordan (CoRR, 2020)

A Variational Inequality Approach to Bayesian Regression Games. - Wenshuo Guo; Michael I. Jordan; Tianyi Lin (CDC, 2021)

The Stereotyping Problem in Collaboratively Filtered Recommender Systems. - Wenshuo Guo; Karl Krauth; Michael I. Jordan; Nikhil Garg 001 (EAAMO, 2021)

Robust Learning of Optimal Auctions. - Wenshuo Guo; Michael I. Jordan; Emmanouil Zampetakis (NeurIPS, 2021)

Test-time Collective Prediction. - Celestine Mendler-Dünner; Wenshuo Guo; Stephen Bates; Michael I. Jordan (NeurIPS, 2021)

Multi-Source Causal Inference Using Control Variates. - Wenshuo Guo; Serena Lutong Wang; Peng Ding; Yixin Wang 002; Michael I. Jordan (CoRR, 2021)

Online Learning of Competitive Equilibria in Exchange Economies. - Wenshuo Guo; Kirthevasan Kandasamy; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (CoRR, 2021)

Semilinear transformations in coding theory and their application to cryptography. - Wenshuo Guo; Fangwei Fu 001 (CoRR, 2021)

Polynomial-Time Key Recovery Attack on the Lau-Tan Cryptosystem Based on Gabidulin Codes. - Wenshuo Guo; Fangwei Fu 001 (CoRR, 2021)

Expanded Gabidulin Codes and Their Application to Cryptography. - Wenshuo Guo; Fangwei Fu 001 (IACR Cryptol. ePrint Arch., 2021)

Cryptanalysis and Repair of a Gabidulin Code Based Cryptosystem from ACISP 2018. - Wenshuo Guo; Fangwei Fu 001 (ACISP, 2022)

Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. - Wenshuo Guo; Kumar Krishna Agrawal; Aditya Grover; Vidya K. Muthukumar; Ashwin Pananjady (AISTATS, 2022)

Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. - Wenshuo Guo; Kirthevasan Kandasamy; Joseph Gonzalez 001; Michael I. Jordan; Ion Stoica (AISTATS, 2022)

McEliece-Type Encryption Based on Gabidulin Codes with No Hidden Structure. - Wenshuo Guo; Fangwei Fu 001 (Inscrypt, 2022)

Partial Identification with Noisy Covariates: A Robust Optimization Approach. - Wenshuo Guo; Mingzhang Yin; Yixin Wang 002; Michael I. Jordan (CLeaR, 2022)

No-Regret Learning in Partially-Informed Auctions. - Wenshuo Guo; Michael I. Jordan; Ellen Vitercik (ICML, 2022)

Off-Policy Evaluation with Policy-Dependent Optimization Response. - Wenshuo Guo; Michael I. Jordan; Angela Zhou (NeurIPS, 2022)

Mechanisms that Incentivize Data Sharing in Federated Learning. - Sai Praneeth Karimireddy; Wenshuo Guo; Michael I. Jordan (CoRR, 2022)

Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. - Wenshuo Guo; Serena Lutong Wang; Peng Ding; Yixin Wang 002; Michael I. Jordan (Trans. Mach. Learn. Res., 2022)

Towards Socially and Economically Beneficial Machine Learning - Wenshuo Guo (2022)

Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws. - Kush Bhatia; Wenshuo Guo; Jacob Steinhardt (AISTATS, 2023)

Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. - Romil Bhardwaj; Kirthevasan Kandasamy; Asim Biswal; Wenshuo Guo; Benjamin Hindman; Joseph Gonzalez 001; Michael I. Jordan; Ion Stoica (OSDI, 2023)

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. - Wenshuo Guo; Nika Haghtalab; Kirthevasan Kandasamy; Ellen Vitercik (EC, 2023)

Two modifications for Loidreau's code-based cryptosystem. - Wenshuo Guo; Fang-Wei Fu 001 (Appl. Algebra Eng. Commun. Comput., 2024)