Tianyue H. Zhang

According to our database, Tianyue H. Zhang authored at least 8 papers between 2023 and 2026.

Collaborative distances:

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

2026

Deriving Hyperparameter Scaling Laws via Modern Optimization Theory.

Egor Shulgin, Dimitri von Rütte, Tianyue H. Zhang, Niccolò Ajroldi, Bernhard Schölkopf, Antonio Orvieto

CoRR, March, 2026

2025

Understanding Adam Requires Better Rotation Dependent Assumptions.

Tianyue H. Zhang, Lucas Maes, Alan Milligan, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas, Damien Scieur, Simon Lacoste-Julien, Charles Guille-Escuret

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization.

Emiliano Penaloza, Tianyue H. Zhang, Laurent Charlin, Mateo Espinosa Zarlenga

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret.

Bingshan Hu, Zhiming Huang, Tianyue H. Zhang, Mathias Lécuyer, Nidhi Hegde

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Understanding Adam Requires Better Rotation Dependent Assumptions.

Lucas Maes, Tianyue H. Zhang, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas, Damien Scieur, Simon Lacoste-Julien, Charles Guille-Escuret

CoRR, 2024

Efficient and Adaptive Posterior Sampling Algorithms for Bandits.

Bingshan Hu, Zhiming Huang, Tianyue H. Zhang, Mathias Lécuyer, Nidhi Hegde

CoRR, 2024

On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization.

Motahareh Sohrabi, Juan Ramirez, Tianyue H. Zhang, Simon Lacoste-Julien, Jose Gallego-Posada

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning.

Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt

Proceedings of the Uncertainty in Artificial Intelligence, 2023