Tianyu Wang

According to our database, Tianyu Wang authored at least 18 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Batched Stochastic Bandit for Nondegenerate Functions.

Yu Liu, Yunlu Shu, Tianyu Wang

IEEE Trans. Inf. Theory, June, 2026

Windowed Fourier Propagator: A Frequency-Local Neural Operator for Wave Equations in Inhomogeneous Media.

Yiyang Cai, Zixuan Qiu, Yunlu Shu, Jiamao Wu, Yingzhou Li, Tianyu Wang, Xi Chen

CoRR, March, 2026

Revisiting stochastic gradient descent for strongly convex objectives: Tight uniform-in-time bounds.

Kang Chen, Yasong Feng, Tianyu Wang

Syst. Control. Lett., 2026

The Nesterov-Spokoiny acceleration achieves strict o(1/k2) convergence.

Weibin Peng, Yu Liu, Tianyu Wang

Eur. J. Oper. Res., 2026

2025

dame-flame: A <i>Python</i> Package Providing Fast Interpretable Matching for Causal Inference.

Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

J. Stat. Softw., 2025

2024

Convergence Rates of Zeroth Order Gradient Descent for Łojasiewicz Functions.

Tianyu Wang, Yasong Feng

INFORMS J. Comput., 2024

2023

From Random Search to Bandit Learning in Metric Measure Spaces.

Chuying Han, Yasong Feng, Tianyu Wang

CoRR, 2023

A Lipschitz Bandits Approach for Continuous Hyperparameter Optimization.

Yasong Feng, Weijian Luo, Yimin Huang, Tianyu Wang

CoRR, 2023

2022

Stochastic Zeroth Order Gradient and Hessian Estimators: Variance Reduction and Refined Bias Bounds.

Yasong Feng, Tianyu Wang

CoRR, 2022

Lipschitz Bandits with Batched Feedback.

Yasong Feng, Zengfeng Huang, Tianyu Wang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

New Directions in Bandit Learning: Singularities and Random Walk Feedback.

Tianyu Wang

PhD thesis, 2021

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference.

Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

J. Mach. Learn. Res., 2021

Batched Lipschitz Bandits.

Yasong Feng, Zengfeng Huang, Tianyu Wang

CoRR, 2021

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference.

Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

CoRR, 2021

2020

Bandits for BMO Functions.

Tianyu Wang, Cynthia Rudin

Proceedings of the 37th International Conference on Machine Learning, 2020

Towards Practical Lipschitz Bandits.

Tianyu Wang, Weicheng Ye, Dawei Geng, Cynthia Rudin

Proceedings of the FODS '20: ACM-IMS Foundations of Data Science Conference, 2020

2019

A Practical Bandit Method with Advantages in Neural Network Tuning.

Tianyu Wang, Dawei Geng, Cynthia Rudin

CoRR, 2019

2017

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference.

Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky, Tianyu Wang

CoRR, 2017