Online Learning

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Online learning studies how to make predictions and decisions as data arrive, rather than training once on a fixed dataset. Common approaches include Online Mirror Descent and Follow-the-Regularized-Leader for optimization, exponential-weights methods for choosing among experts, and bandit algorithms when feedback is limited. Researchers analyze regret and mistake bounds in adversarial, stochastic, and changing environments, and apply these methods to areas such as forecasting, auctions, and resource allocation. A central challenge is adapting quickly to change while remaining stable and efficient when feedback is delayed, noisy, or costly.

Latest papers

131 papers24 papers in the last four weeks, up 60% on the four weeks before. 0.1% of all new papers.

AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals
Feb 19, 2026Xueyi Wang, Claudine J. C. Lamoth, Elisabeth WilhelmWearable Health Monitoring Explainable Time Series Forecasting
Engineering and Technology institute Groningen (ENTEG), University of Groningen, Groningen, 9742AG, the Netherlands · Department of Human Movement Sciences, University Medical Center Groningen, Groningen, Netherlands
Multi-Round Human-AI Collaboration with User-Specified Requirements
Feb 19, 2026Sima Noorani, Shayan Kiyani, Hamed Hassani +1Human-AI Complementarity Human-AI Collaboration
University of Pennsylvania
Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction
Feb 19, 2026Lunjia Hu, Kevin Tian, Chutong YangBlackwell Approachability Online Learning
Northeastern University · University of Texas at Austin
Online Learning with Improving Agents: Multiclass, Budgeted Agents and Bandit Learners
Feb 19, 2026Sajad Ashkezari, Shai Ben-DavidOnline Learning Strategic Classification
University of Waterloo
A Study on Multi-Class Online Fuzzy Classifiers for Dynamic Environments
Feb 16, 2026Kensuke Ajimoto, Yuma Yamamoto, Yoshifumi Kusunoki +1Multiclass Classification Fuzzy Logic
Graduate School of Informatics, Osaka Metropolitan University, Osaka, Japan
Hybrid Feedback-Guided Optimal Learning for Wireless Interactive Panoramic Scene Delivery
Feb 6, 2026Xiaoyi Wu, Juaren Steiger, Bin Li +1Wireless Resource Allocation Online Learning
IEEE/ACM TRANSACTIONS ON NETWORKING
Adaptive Conformal Inference through the Lens of Blackwell Approachability
Oct 17, 2025Guillaume Principato, Gilles StoltzBlackwell Approachability Conformal Prediction
Universit´e Paris-Saclay, CNRS, Inria, Laboratoire de math´ematiques d’Orsay, 91405, Orsay, France · EDF R&D, Palaiseau, France · HEC Paris, Jouy-en-Josas, France
Bridging MOOCs, Smart Teaching, and AI-Assisted Learning: A Unified Quantitative Model
Jul 18, 2025Bo Yuan, Jiazi HuPersonalized Learning Educational Technology
The University of Queensland Brisbane, Australia · AI U Course Kunming, P.R. China
Plan-Driven Adaptive Bidding for First-Price Auctions with Budget Constraints under Nonstationarity
May 5, 2025Yige Wang, Jiashuo JiangPrimal-Dual Optimization Automated Bidding
Department of Industrial Engineering & Decision Analytics, Hong Kong University of Science and Technology
Learning in Structured Stackelberg Games
Apr 11, 2025Maria-Florina Balcan, Kiriaki Fragkia, Keegan HarrisGame Theory Stackelberg Games
Carnegie Mellon University, Pittsburgh, USA · University of California, Berkeley, USA
Online Learning: A Modern Introduction Using Convex Optimization
Dec 31, 2019Francesco OrabonaOnline Convex Optimization Follow-the-Regularized-Leader
KAUST

Feb 19, 2026J/K move · Enter open · S save

AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals

Xueyi Wang, Claudine J. C. Lamoth, Elisabeth Wilhelm

Engineering and Technology institute Groningen (ENTEG), University of Groningen, Groningen, 9742AG, the Netherlands · Department of Human Movement Sciences, University Medical Center Groningen, Groningen, Netherlands

Wearable Health Monitoring Explainable Time Series Forecasting Time Series Forecasting Online Learning Multivariate Time Series Forecasting

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Continuous stress forecasting could potentially contribute to lifestyle interventions. This paper presents a novel, explainable, and individualized approach for stress prediction using physiological data from consumer-grade smartwatches. We develop a time series forecasting model that leverages multivariate features, including heart rate variability, activity patterns, and sleep metrics, to predict stress levels across 16 temporal horizons (History window: 3, 5, 7, 9 days; forecasting window: 1, 3, 5, 7 days). Our evaluation involves 16 participants monitored for 10-15 weeks. We evaluate our approach across 16 participants, comparing against state-of-the-art time series models (Informer, TimesNet, PatchTST) and traditional baselines (CNN, LSTM, CNN-LSTM) across multiple temporal horizons. Our model achieved performance with an MSE of 0.053, MAE of 0.190, and RMSE of 0.226 in optimal settings (5-day input, 1-day prediction). A comparison with the baseline models shows that our model outperforms TimesNet, PatchTST, CNN-LSTM, LSTM, and CNN under all conditions, representing improvements of 36.9%, 25.5%, and 21.5% over the best baseline. According to the explanability analysis, sleep metrics are the most dominant and consistent stress predictors (importance: 1.1, consistency: 0.9-1.0), while activity features exhibit high inter-participant variability (0.1-0.2). Most notably, the model captures individual-specific patterns where identical features can have opposing effects across users, validating its personalization capabilities. These findings establish that consumer wearables, combined with adaptive and interpretable deep learning, can deliver relevant stress assessment adapted to individual physiological responses, providing a foundation for scalable, continuous, explainable mental health monitoring in real-world settings.