Chunlin Sun

Orcid: 0000-0001-7389-4808

According to our database1, Chunlin Sun authored at least 13 papers between 2021 and 2024.

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

Timeline

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Links

On csauthors.net:

Bibliography

2024
Decoupling Learning and Decision-Making: Breaking the O(√T) Barrier in Online Resource Allocation with First-Order Methods.
CoRR, 2024

Decoupling Learning and Decision-Making: Breaking the O(T) Barrier in Online Resource Allocation with First-Order Methods.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Learning to Make Adherence-aware Advice.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

When No-Rejection Learning is Consistent for Regression with Rejection.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Simple and fast algorithm for binary integer and online linear programming.
Math. Program., July, 2023

When No-Rejection Learning is Optimal for Regression with Rejection.
CoRR, 2023

Predict-then-Calibrate: A New Perspective of Robust Contextual LP.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming.
Proceedings of the International Conference on Machine Learning, 2023

Solving Linear Programs with Fast Online Learning Algorithms.
Proceedings of the International Conference on Machine Learning, 2023

2022
Learning from Stochastically Revealed Preference.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
An Adaptive State Aggregation Algorithm for Markov Decision Processes.
CoRR, 2021

Boosting Method in Approximately Solving Linear Programming with Fast Online Algorithm.
CoRR, 2021

The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks.
Proceedings of the 38th International Conference on Machine Learning, 2021


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