Adaptive Learning

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7 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization
Jun 6, 2026Wentao Zhang, Yutong Zhang, Wentao MoOnline Convex Optimization Bandit Convex Optimization
Tsinghua Shenzhen International Graduate School, Tsinghua University, China · College of Mathematics, Sichuan University, China
A physical adaptive material motor unit neural network: a hygromorph composite material machine
Jun 5, 2026Charles de Kergariou, David Correa, Adam W. Perriman +2Materials Science Adaptive Learning
Bristol Composites Institute, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, University Walk, Bristol BS8 1TR, United Kingdom · School of Architecture, University of Waterloo, 7 Melville Street South, Cambridge, Ontario, N1S 2H4, Canada · Research School of Chemistry and John Curtin School of Medical Research, Australian National University, Canberra ACT2601, Australia +2
SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice
Jun 2, 2026Tianfu Wang, Max Xiong, Jianxun Lian +8Intelligent Tutoring Systems Adaptive Learning
HKUST (GZ) · Duke University · MSRA +1
Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
May 28, 2026Qian Chang, Ciprian Doru Giurcaneanu, Runsong Jia +6Temporal GNNs Graph Representation Learning
University of Auckland, Auckland, New Zealand · University of Technology Sydney, Sydney, Australia · Central China Normal University, Wuhan, China
Adaptive Bandit Algorithms for Contextual Matching Markets
May 27, 2026Shiyun Lin, Simon Mauras, Vianney Perchet +1Multi-Armed Bandits Contextual Bandits
Center for Statistical Science, School of Mathematical Sciences, Peking University · INRIA, FairPlay Joint Team · CREST, ENSAE, IP Paris; Criteo AI lab, FairPlay Joint Team +1
Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM
May 26, 2026Younghun Lee, Amir Bralin, Nobel Sanjay Rebello +1Intelligent Tutoring Systems AI in Education
Department of Computer Science · Purdue University · Department of Physics and Astronomy +1
Capture-Calibrate-Coach: A Graph-Based Framework for Knowledge Monitoring Estimation and Adaptive Feedback
May 25, 2026Gen Li, Li Chen, Cheng Tang +5Intelligent Tutoring Systems Adaptive Learning
Kyushu University, Fukuoka, Japan · Osaka Kyoiku University, Osaka, Japan · South China Normal University, Guangzhou, China +2
Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation
May 24, 2026Kordel K. France, Ovidiu DaescuNon-Stationary RL Partially Observable RL
Department of Computer Science, University of Texas at Dallas, USA
SEAL: Synergistic Co-Evolution of Agents and Learning Environments
May 23, 2026Yihao Hu, Zhihao Wen, Xiujin Liu +3LLM Agent Training Self-Improving Agents
Ant Group · Westlake University · University of Michigan–Ann Arbor +1
Estimating Learners' Skill Acquisition Without Temporal Information
May 22, 2026Ryosuke Nagai, Kyohei Atarashi, Koh Takeuchi +2Knowledge Tracing Adaptive Learning
SODA · Kyoto University, Kyoto, Japan · Inria, France
Curriculum reinforcement learning with measurable task representation learning
May 22, 2026Yongyan Wen, Siyuan Li, Mingjian Fu +3Representation Learning Curriculum RL
Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China · Fuzhou University, Fuzhou, 350108, Fujian, China · China Academy of Sciences, Beijing, 100190, China +1
CourseBlueprint: A Structured Pipeline for Adaptive Pedagogical Video Generation Grounded in Course Corpora
May 22, 2026Md Zabirul Islam, Md Motaleb Hossen Manik, Ge WangGenerative AI in Education Video Generation
AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems
May 21, 2026Priyamvada Tripathi, Bill KapralosIntelligent Tutoring Systems AI in Education
Durham College, 2000 Simcoe Street North Oshawa, Ontario, Canada. L1G 0C5 · Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario, Canada. L1G 0C5
Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology
May 21, 2026Christo Kurisummoottil Thomas, Emilio Calvanese StrinatiDistribution Shift Robustness Wireless Communications
Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA · CEA-Leti, Grenoble, France
Nonparametric Learning and Earning with One-Point Feedback under Nonstationarity
May 20, 2026Xiangyu Yang, Feng Xu, Jian-Qiang Hu +1Non-Stationary Bandits Dynamic Pricing
School of Management, Shandong University · School of Management, Fudan University · Department of Applied Mathematics and Statistics, Stony Brook University
Beyond Predefined Learning Objects: A Thinking-Learning Interaction Model for Up-to-Date Autonomous Robot Learning
May 17, 2026Hong SuRobot Policy Learning Continual Robot Learning
School of Computer Science, Chengdu University of Information Technology, Chengdu, China
Artificial Adaptive Intelligence: The Missing Stage Between Narrow and General Intelligence
May 16, 2026Boris KriukAutomated Machine Learning Adaptive Learning
Can Vision Language Models Be Adaptive in Mathematics Education? A Learner Model-based Rubric Study
May 15, 2026Jie Gao, Yongan Yu, Junzhu Su +3VLM Evaluation Intelligent Tutoring Systems
1McGill University · 2Mila – Quebec AI Institute · 1McGill +1
Silent Collapse in Recursive Learning Systems
May 14, 2026Zhipeng ZhangModel Collapse Adaptive Learning
China Mobile Research Institute, Beijing 100053, China · China Mobile GBA (Greater Bay Area) Innovation Institute, Guangzhou 510656, China
EMA: Efficient Model Adaptation for Learning-based Systems
May 13, 2026Daiyang Yu, Xinyu Chen, Yihan Zhang +3Adaptive Learning Data-Efficient Learning
University of Illinois Urbana-Champaign USA · The Hong Kong University of Science and Technology Hong Kong, China
From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning
May 12, 2026Eric S. Qiu, Danielle R. Thomas, Boyuan Guo +2Intelligent Tutoring Systems Time Series Forecasting
Cornell University · Ithaca, NY, USA · Carnegie Mellon University +1
Consolidation-Expansion Operator Mechanics:A Unified Framework for Adaptive Learning
May 11, 2026Debashis GuhaAdaptive Learning Adaptive Computation
S P Jain School of Global Management
Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints
May 10, 2026Siteng Kang, Xinhua ZhangAdaptive Sampling Physics-Informed ML
University of Illinois Chicago
Instance-Adaptive Online Multicalibration
May 10, 2026Zhiming Huang, Jamie Morgenstern, Aaron Roth +1Probability Calibration Model Calibration
Paul G. Allen School of Computer Science and Engineering, University of Washington · Department of Computer and Information Sciences, University of Pennsylvania
PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering
May 9, 2026Naoki Masuyama, Yusuke Nojima, Stefan Wermter +3Clustering Unsupervised Clustering
Graduate School of Informatics, Osaka Metropolitan University · Knowledge Technology Research Group, Department of Informatics, University of Hamburg · Graduate School of Environmental, Life, Natural Science and Technology, Okayama University +2
Streaming Adversarial Robustness in Fuzzy ARTMAP: Mechanism-Aligned Evaluation, Progressive Training, and Interpretable Diagnostics
May 7, 2026Shane Cairns, Leonardo Enzo Brito da Silva, Sasha Petrenko +2Adversarial Training Neural Network Robustness
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA · Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, RN 59078-900, Brazil · Kummer Institute Center for Artificial Intelligence and Autonomous Systems (KICAIAS), Missouri University of Science and Technology, Rolla, MO, USA
On the Divergence of Differential Temporal Difference Learning without Local Clocks
May 7, 2026David Antrobius, Shangtong ZhangReinforcement Learning Average-Reward RL
Department of Computer Science University of Virginia
Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
May 7, 2026Yutong Wang, Yannig Goude, Qiwei YaoEnsemble Learning Time Series Forecasting
Department of Statistics, LSE · Électricité de France R&D
Adaptive Learning Strategies for AoA-Based Outdoor Localization: A Comprehensive Framework
May 6, 2026Bac Trinh-Nguyen, Sara Berri, Sin G. Teo +2Wireless Localization Adaptive Learning
1ETIS UMR 8051, CY Cergy Paris University, ENSEA, CNRS, FR · Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), SG · 3Singapore Institute of Technology (SIT), SG +1
Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning
May 6, 2026Anastasis Kratsios, A. Martina Neuman, Philipp PetersenReLU Neural Networks In-Context Learning
McMaster University and the Vector Institute, Main St., Hamilton, L8S 4L8, Ontario, Canada · University of Vienna, Faculty of Mathematics, Kolingasse 14-16, 1090 Wien, Austria

Jun 6, 2026, cs.LGJ/K move · Enter open · S save

Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization

Wentao Zhang, Yutong Zhang, Wentao Mo

Tsinghua Shenzhen International Graduate School, Tsinghua University, China · College of Mathematics, Sichuan University, China

Online Convex Optimization Bandit Convex Optimization Adaptive Learning

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We study high-probability regret bounds for online convex optimization (OCO) with strongly convex losses and establish three results that resolve open questions at the intersection of noise adaptivity, feedback structure, and constraint satisfaction. For the full-information setting with sub-Gaussian stochastic gradients, we prove a noise-adaptive high-probability regret bound in which the martingale deviation term scales with the noise level

σ

rather than the gradient bound

G

, yielding a multiplicative improvement of

G/σ

over the classical Azuma-Hoeffding baseline. Our analysis introduces an exponential supermartingale argument that bypasses the bounded-difference requirement of Freedman's inequality, enabling direct treatment of unbounded sub-Gaussian noise without truncation artifacts. For bandit feedback, we prove a minimax lower bound: the high-probability regret scales linearly in

\log(1/δ)

, in contrast to the

\sqrt{\log(1/δ)}

confidence cost under full information. This constitutes a formal separation in the confidence cost of strongly convex OCO across feedback models. Regarding constrained OCO with stochastic constraints satisfying a Slater condition, we provide simultaneous high-probability guarantees for both cumulative regret and long-run constraint violation, achieving

\mathcal{O}(\sqrt{T\log(m/δ)})

regret and

\mathcal{O}(\sqrt{T}/(ζδ) + m\sqrt{T\log(m/δ)})

violation. Synthetic experiments corroborate all theoretical predictions.

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