Lee, Jason D.

Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks

Soltanolkotabi, M., Javanmard, A., & Lee, J. D. (2019) · IEEE Transactions on Information Theory

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 356

Implicit Bias of Gradient Descent on Linear Convolutional Networks

Gunasekar, S., Lee, J., Soudry, D., & Srebro, N. (2018) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 75

Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Cai, T., Li, Y., Geng, Z., Peng, H., Lee, J. D., Chen, D., & Dao, T. (2024) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 27

What Makes a Reward Model a Good Teacher? An Optimization Perspective

Razin, N., Wang, Z., Strauss, H., Wei, S., Lee, J. D., & Arora, S. (2025) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 2

Reward Collapse in Aligning Large Language Models

Song, Z., Cai, T., Lee, J. D., & Su, W. J. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

An Information-Theoretic Analysis of In-Context Learning

Jeon, H. J., Lee, J. D., Lei, Q., & Van Roy, B. (2024) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 1

Learning What to Remember: Test-Time Training via Context Distillation

Wang, Z., Dang, X., Zhu, R.-J., Wen, Z., Fu, H., Chai, W., & Lee, J. D. (2026) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 0

Learning Compositional Functions with Transformers from Easy-to-Hard Data

Wang, Z., Nichani, E., Bietti, A., Damian, A., Hsu, D., Lee, J. D., & Wu, D. (2025) · arXiv

Machine Learning and AlgorithmsNeural Networks and Applications

Cited by 0