arXiv Machine Learning
Jun 24
Target-Aware Linear Regression Under Distribution Shift
arXiv:2606. 22775v2 Announce Type: replace-cross Abstract: Distribution shift between training and deployment is a pervasive challenge for modern AI systems.
Adaptive prediction theory combining offline and online learning
The paper studies a two‑stage learning framework that first trains an offline model using approximate nonlinear‑least‑squares estimation and then adapts it online with a meta‑LMS algorithm to handle parameter drift in nonlinear stochastic dynamical systems. It provides an upper bound on the offline generalization error that accounts for strong data correlation and distribution shift via Kullback‑Leibler divergence, and it demonstrates that the combined offline‑online approach outperforms methods that rely solely on offline or online learning. Both theoretical analysis and empirical experiments support the claimed performance gains.
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arXiv Machine Learning
Jun 3
Online Learning with Gradient-Variation Interval Regret
arXiv:2606. 03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval.
arXiv AI
Jul 7
The Three Regimes of Offline-to-Online Reinforcement Learning
arXiv:2510. 01460v4 Announce Type: replace-cross Abstract: Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online interactions for fine-tuning.
arXiv Machine Learning
Jun 11
Momentum LMS Theory beyond Stationarity: Stability, Tracking, and Regret
arXiv:2602. 11995v2 Announce Type: replace Abstract: In large-scale data processing scenarios, data often arrive in sequential streams generated by complex systems that exhibit drifting distributions and time-varying system parameters.
arXiv Machine Learning
Jul 21
Online learning of neural state-space models
arXiv:2607. 17614v1 Announce Type: cross Abstract: Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) models that achieve state-of-the-art performance in offline settings by estimating initial model states from past input-output data.
arXiv AI
Aug 13
Dynamics Models for Offline Hyperparameter Selection in Real-World RL
arXiv:2608. 11349v1 Announce Type: cross Abstract: A key obstacle to deploying reinforcement learning in real-world systems is hyperparameter selection, particularly when simulators are unavailable and online experimentation is costly.
arXiv AI
Jul 17
Fully Offline Reinforcement Learning
arXiv:2505. 22442v3 Announce Type: replace-cross Abstract: Offline RL (ORL) promises safe and sample-efficient deployment but existing methods rely on undocumented online interactions for hyperparameter tuning and lack reliable fully offline estimates of initial online performance.
arXiv Machine Learning
Jun 15
Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models
arXiv:2606. 14222v1 Announce Type: new Abstract: The rapid evolution of Time Series Foundation Models (TSFMs) has advanced zero-shot forecasting across diverse domains.
arXiv Machine Learning
Jul 23
Online Variance Reduction for Domain Adaptation on Streaming Data
arXiv:2607. 20374v1 Announce Type: new Abstract: This paper studies the problem of stochastic variance reduction (SVR) for the maximum mean discrepancy (MMD) and correlation alignment (CORAL) loss functions.
arXiv Machine Learning
Jun 4
Offline-to-Online Learning in Linear Bandits
arXiv:2606. 04305v1 Announce Type: new Abstract: We study online learning with an additional offline dataset in the stochastic linear bandit setting.
arXiv Machine Learning
Jun 30
Analysis of Adam Algorithms for Stochastic Dynamic Systems
arXiv:2606. 28879v1 Announce Type: new Abstract: The adaptive moment estimation algorithm, known as Adam, is widely used in modern machine learning, owing to its low per-iteration complexity and strong empirical performance.
Hugging Face Trending Papers
Jun 24
A probabilistic framework for online test-time adaptation
This paper presents a probabilistic framework for online test-time adaptation problems. In them, a model is trained on labeled data but must adapt to unlabeled data at test time under the assumption that training and test distributions potentially differ, that is, there might have been a distributional shift.