Yinglun Zhu

According to our database1, Yinglun Zhu authored at least 13 papers between 2017 and 2024.

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

Timeline

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PhD thesis 
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Links

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Bibliography

2024
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models.
CoRR, 2024

2023
LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning.
CoRR, 2023

Infinite Action Contextual Bandits with Reusable Data Exhaust.
Proceedings of the International Conference on Machine Learning, 2023

2022
Efficient Active Learning with Abstention.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Active Learning with Neural Networks: Insights from Nonparametric Statistics.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces.
Proceedings of the International Conference on Machine Learning, 2022

Contextual Bandits with Large Action Spaces: Made Practical.
Proceedings of the International Conference on Machine Learning, 2022

Pareto Optimal Model Selection in Linear Bandits.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Near Instance Optimal Model Selection for Pure Exploration Linear Bandits.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Pure Exploration in Kernel and Neural Bandits.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
On Regret with Multiple Best Arms.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust Outlier Arm Identification.
Proceedings of the 37th International Conference on Machine Learning, 2020

2017
ReabsNet: Detecting and Revising Adversarial Examples.
CoRR, 2017


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