Kefan Dong

According to our database1, Kefan Dong authored at least 17 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time.
CoRR, 2023

Toward L<sub>∞</sub>-recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields.
CoRR, 2023

Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Asymptotic Instance-Optimal Algorithms for Interactive Decision Making.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Toward L_∞Recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Model-Based Offline Reinforcement Learning with Local Misspecification.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2021
Design of Experiments for Stochastic Contextual Linear Bandits.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Refined Analysis of FPL for Adversarial Markov Decision Processes.
CoRR, 2020

Multinomial Logit Bandit with Low Switching Cost.
Proceedings of the 37th International Conference on Machine Learning, 2020

On the Expressivity of Neural Networks for Deep Reinforcement Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP.
Proceedings of the 8th International Conference on Learning Representations, 2020

Root-n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank.
Proceedings of the Conference on Learning Theory, 2020

2019
Bootstrapping the Expressivity with Model-based Planning.
CoRR, 2019

√n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank.
CoRR, 2019

Exploration via Hindsight Goal Generation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019


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