Jackknife estimation for Markov processes with no mixing constraints. - Kevin Oshiro; Changlong Wu; Narayana P. Santhanam (ISIT, 2017)

Greedy Algorithm with Approximation Ratio for Sampling Noisy Graph Signals. - Changlong Wu; Wenxin Chen; June Zhang (ICASSP, 2018)

Redundancy of Unbounded Memory Markov Classes with Continuity Conditions. - Changlong Wu; Maryam Hosseini; Narayana Santhanam (ISIT, 2018)

Being correct eventually almost surely. - Changlong Wu; Narayana Santhanam (ISIT, 2019)

Almost Uniform Sampling From Neural Networks. - Changlong Wu; Narayana Prasad Santhanam (CISS, 2020)

Entropy property testing with finitely many errors. - Changlong Wu; Narayana Santhanam (ISIT, 2020)

Prediction with Finitely many Errors Almost Surely. - Changlong Wu; Narayana Santhanam (AISTATS, 2021)

Non-uniform Consistency of Online Learning with Random Sampling. - Changlong Wu; Narayana Santhanam (ALT, 2021)

Estimating Properties of Dynamic Graphical Models. - Changlong Wu; Narayana Santhanam (ISIT, 2021)

Sequential vs. Fixed Design Regrets in Online Learning. - Changlong Wu; Mohsen Heidari; Ananth Grama; Wojciech Szpankowski (ISIT, 2022)

Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm. - Changlong Wu; Mohsen Heidari; Ananth Grama; Wojciech Szpankowski (NeurIPS, 2022)

Online Learning in Dynamically Changing Environments. - Changlong Wu; Ananth Grama; Wojciech Szpankowski (COLT, 2023)

Learning Functional Distributions with Private Labels. - Changlong Wu; Yifan Wang; Ananth Grama; Wojciech Szpankowski (ICML, 2023)

Identifying Root-Cause Changes for User-Reported Incidents in Online Service Systems. - Yujin Zhao; Ling Jiang; Ye Tao 011; Songlin Zhang; Changlong Wu; Tong Jia; Xiaosong Huang; Ying Li 012; Zhonghai Wu (ISSRE, 2023)

How to Manage Change-Induced Incidents? Lessons from the Study of Incident Life Cycle. - Yujin Zhao; Ling Jiang; Ye Tao 011; Songlin Zhang; Changlong Wu; Yifan Wu 002; Tong Jia; Ying Li 012; Zhonghai Wu (ISSRE, 2023)

Robust Online Classification: From Estimation to Denoising. - Changlong Wu; Ananth Grama; Wojciech Szpankowski (CoRR, 2023)

Expected Worst Case Regret via Stochastic Sequential Covering. - Changlong Wu; Mohsen Heidari; Ananth Grama; Wojciech Szpankowski (Trans. Mach. Learn. Res., 2023)

Regret Bounds for Log-Loss via Bayesian Algorithms. - Changlong Wu; Mohsen Heidari; Ananth Grama; Wojciech Szpankowski (IEEE Trans. Inf. Theory, 2023)

Online Distribution Learning with Local Privacy Constraints. - Jin Sima; Changlong Wu; Olgica Milenkovic; Wojciech Szpankowski (AISTATS, 2024)

Oracle-Efficient Hybrid Online Learning with Unknown Distribution. - Changlong Wu; Jin Sima; Wojciech Szpankowski (COLT, 2024)

A Theory of Fault-Tolerant Learning. - Changlong Wu; Yifan Wang; Ananth Grama (ICML, 2024)

Minimax Regret with Unbounded Weights. - Michael Drmota; Philippe Jacquet; Changlong Wu; Wojciech Szpankowski (ISIT, 2024)

Information-theoretic Limits of Online Classification with Noisy Labels. - Changlong Wu; Ananth Grama; Wojciech Szpankowski (NeurIPS, 2024)

OCRCL: Online Contrastive Learning for Root Cause Localization of Business Incidents. - Xiaosong Huang; Hongyi Liu; Yifan Wu 002; Yujin Zhao; Changlong Wu; Songlin Zhang; Ling Jiang; Tong Jia; Ying Li 012; Zhonghai Wu (SANER, 2024)

Online Distribution Learning with Local Private Constraints. - Jin Sima; Changlong Wu; Olgica Milenkovic; Wojciech Szpankowski (CoRR, 2024)

No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models. - Changlong Wu; Ananth Grama; Wojciech Szpankowski (ICLR, 2025)