Changlong Wu

Orcid: 0000-0001-5255-1275

According to our database1, Changlong Wu authored at least 24 papers between 2017 and 2024.

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Bibliography

2024
Online Distribution Learning with Local Private Constraints.
CoRR, 2024

OCRCL: Online Contrastive Learning for Root Cause Localization of Business Incidents.
Proceedings of the IEEE International Conference on Software Analysis, 2024

Minimax Regret with Unbounded Weights.
Proceedings of the IEEE International Symposium on Information Theory, 2024

A Theory of Fault-Tolerant Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Oracle-Efficient Hybrid Online Learning with Unknown Distribution.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Online Distribution Learning with Local Privacy Constraints.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Regret Bounds for Log-Loss via Bayesian Algorithms.
IEEE Trans. Inf. Theory, September, 2023

Expected Worst Case Regret via Stochastic Sequential Covering.
Trans. Mach. Learn. Res., 2023

Robust Online Classification: From Estimation to Denoising.
CoRR, 2023

How to Manage Change-Induced Incidents? Lessons from the Study of Incident Life Cycle.
Proceedings of the 34th IEEE International Symposium on Software Reliability Engineering, 2023

Identifying Root-Cause Changes for User-Reported Incidents in Online Service Systems.
Proceedings of the 34th IEEE International Symposium on Software Reliability Engineering, 2023

Learning Functional Distributions with Private Labels.
Proceedings of the International Conference on Machine Learning, 2023

Online Learning in Dynamically Changing Environments.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sequential vs. Fixed Design Regrets in Online Learning.
Proceedings of the IEEE International Symposium on Information Theory, 2022

2021
Estimating Properties of Dynamic Graphical Models.
Proceedings of the IEEE International Symposium on Information Theory, 2021

Non-uniform Consistency of Online Learning with Random Sampling.
Proceedings of the Algorithmic Learning Theory, 2021

Prediction with Finitely many Errors Almost Surely.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Entropy property testing with finitely many errors.
Proceedings of the IEEE International Symposium on Information Theory, 2020

Almost Uniform Sampling From Neural Networks.
Proceedings of the 54th Annual Conference on Information Sciences and Systems, 2020

2019
Being correct eventually almost surely.
Proceedings of the IEEE International Symposium on Information Theory, 2019

2018
Redundancy of Unbounded Memory Markov Classes with Continuity Conditions.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Greedy Algorithm with Approximation Ratio for Sampling Noisy Graph Signals.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017
Jackknife estimation for Markov processes with no mixing constraints.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017


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