:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wang, Jingyuan, Ren, Zhimei, Zhan, Ruohan, Zhou, Zhengyuan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2412.14297
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptively Learning to Select-Rank in Online Platforms
by: Wang, Jingyuan, et al.
Published: (2024)

Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
by: Chen, Junbao, et al.
Published: (2024)

On the Foundation of Distributionally Robust Reinforcement Learning
by: Wang, Shengbo, et al.
Published: (2023)

Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces
by: Wang, Shengbo, et al.
Published: (2024)

Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality
by: Jin, Ying, et al.
Published: (2022)

DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
by: Cui, Mingxuan, et al.
Published: (2025)

Single-Trajectory Distributionally Robust Reinforcement Learning
by: Liang, Zhipeng, et al.
Published: (2023)

Post Reinforcement Learning Inference
by: Syrgkanis, Vasilis, et al.
Published: (2023)

A Finite Sample Complexity Bound for Distributionally Robust Q-learning
by: Wang, Shengbo, et al.
Published: (2023)

Statistical Properties of Robust Satisficing
by: Li, Zhiyi, et al.
Published: (2024)

Learning an Optimal Assortment Policy under Observational Data
by: Han, Yuxuan, et al.
Published: (2025)

Sample Complexity of Variance-reduced Distributionally Robust Q-learning
by: Wang, Shengbo, et al.
Published: (2023)

Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage
by: Jin, Ying, et al.
Published: (2024)

Distributionally Robust Federated Learning with Client Drift Minimization
by: Krouka, Mounssif, et al.
Published: (2025)

Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds
by: Xu, Yunbei, et al.
Published: (2026)

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
by: Yu, En, et al.
Published: (2023)

An Information-Theoretic Analysis for Federated Learning under Concept Drift
by: Peng, Fu, et al.
Published: (2025)

Federated Learning with Profile Mapping under Distribution Shifts and Drifts
by: Li, Mohan, et al.
Published: (2026)

Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees
by: Gui, Yu, et al.
Published: (2024)

Generalized Incremental Learning under Concept Drift across Evolving Data Streams
by: Yu, En, et al.
Published: (2025)

Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
by: Zhan, Ruohan, et al.
Published: (2021)

Optimized Deep Learning Models for Malware Detection under Concept Drift
by: Maillet, William, et al.
Published: (2023)

Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift
by: Lee, Morgan, et al.
Published: (2025)

Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach
by: Zhan, Ruohan, et al.
Published: (2024)

Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
by: Liu, Zijian, et al.
Published: (2024)

Learning under Distributional Drift: Prequential Reproducibility as an Intrinsic Statistical Resource
by: Zaichyk, Sofiya
Published: (2025)

Scalable and Precise Patch Robustness Certification for Deep Learning Models with Top-k Predictions
by: Zhou, Qilin, et al.
Published: (2025)

Breaking the Curse of Repulsion: Optimistic Distributionally Robust Policy Optimization for Off-Policy Generative Recommendation
by: Jiang, Jie, et al.
Published: (2026)

Optimal training-conditional regret for online conformal prediction
by: Liang, Jiadong, et al.
Published: (2026)

Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting
by: Zhan, Tianxiang, et al.
Published: (2025)

Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment
by: Ashrafee, Alif, et al.
Published: (2025)

Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks
by: Vaquet, Valerie, et al.
Published: (2024)

An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity
by: Pyeon, Junghee, et al.
Published: (2025)

Distribution-Free Predictive Inference under Unknown Temporal Drift
by: Han, Elise, et al.
Published: (2024)

ACS: An interactive framework for conformal selection
by: Gui, Yu, et al.
Published: (2025)

Time to Retrain? Detecting Concept Drifts in Machine Learning Systems
by: Pham, Tri Minh Triet, et al.
Published: (2024)

Binary Anomaly Detection in Streaming IoT Traffic under Concept Drift
by: Carnier, Rodrigo Matos, et al.
Published: (2025)

Autonomous Concept Drift Threshold Determination
by: Lu, Pengqian, et al.
Published: (2025)

Counterfactual Explanations Under Concept Drift
by: Kostrzewa, Marcin, et al.
Published: (2026)