Learning the Kernel for Classification and Regression. - Chen Li; Luca Venturi; Ruitu Xu (CoRR, 2017)

On the Landscape of Synchronization Networks: A Perspective from Nonconvex Optimization. - Shuyang Ling; Ruitu Xu; Afonso S. Bandeira (SIAM J. Optim., 2019)

Meta Learning in the Continuous Time Limit. - Ruitu Xu; Lin Chen 003; Amin Karbasi (AISTATS, 2021)

Convergence and Alignment of Gradient Descent with Random Backpropagation Weights. - Ganlin Song; Ruitu Xu; John Lafferty (NeurIPS, 2021)

Convergence and Alignment of Gradient Descentwith Random Back propagation Weights. - Ganlin Song; Ruitu Xu; John Lafferty (CoRR, 2021)

Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning. - Yingjie Fei; Ruitu Xu (ICML, 2022)

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. - Yifei Min; Tianhao Wang 002; Ruitu Xu; Zhaoran Wang 001; Michael I. Jordan; Zhuoran Yang (NeurIPS, 2022)

Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning. - Yingjie Fei; Ruitu Xu (CoRR, 2022)

Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. - Ruitu Xu; Yifei Min; Tianhao Wang 002; Michael I. Jordan; Zhaoran Wang 001; Zhuoran Yang (AISTATS, 2023)

Noise-Adaptive Thompson Sampling for Linear Contextual Bandits. - Ruitu Xu; Yifei Min; Tianhao Wang 002 (NeurIPS, 2023)

Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning. - Ruitu Xu; Yifei Min; Tianhao Wang 002; Zhaoran Wang 001; Michael I. Jordan; Zhuoran Yang (CoRR, 2023)

Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning. - Yingjie Fei; Ruitu Xu (CoRR, 2024)