Closing the loop in learning with missing data

• URL: http://arxiv.org/abs/2608.09030v1
• Date: Mon, 10 Aug 2026 02:30:37 GMT
• Title: Closing the loop in learning with missing data
• Abstract summary: We look at what should a machine learning model learn when data is missing during training?<n>We derive adaptation mechanisms with Lyapunov stability characteristics that throttle model updates in ways that preserve learning coherence under partial, intermittent observability.<n>We evaluate the efficacy of our directional observability-aware adaptive learning approach on multimodal contexts, reinforcing its premise in promoting learning coherence and stability even in pathologically sparse domains and problems.
• Score: 1.0312968200748116
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: What should a machine learning model learn when data is missing during training? We look at the learning process from a dynamical systems perspective, cast data missingness as a structured loss of actuation that limits controllability of the parameter error dynamics, and ultimately derive adaptation mechanisms with Lyapunov stability characteristics that throttle model updates in ways that preserve learning coherence under partial, intermittent observability. Under recurrent excitation, our analysis provides ISS-type residual-to-state bounds with respect to a bounded closed-loop mismatch between the loss residual and the preconditioned update geometry. We evaluate the efficacy of our directional observability-aware adaptive learning approach on multimodal contexts, reinforcing its premise in promoting learning coherence and stability even in pathologically sparse domains and problems.

Related papers

• Continual Segmentation under Joint Nonstationarity [8.522964568257455]
  Evolving data streams induce joint nonstationarity in continual semantic segmentation.<n>This setting reflects practical structured prediction systems, yet remains largely unexplored in prior continual learning work.<n>We formalize continual segmentation under coupled class, domain, and label shifts and investigate learning in heterogeneous dense prediction environments.
  arXiv  Detail & Related papers  (2026-05-19T22:20:00Z)
• Non-Parametric Rehearsal Learning via Conditional Mean Embeddings [88.89267783967263]
  We propose a non-parametric rehearsal learning approach for textitavoiding undesired future (AUF) problems.<n>Specifically, we use kernel machinery to reformulate the AUF objective into a unified representation that disentangles desirability modeling from action-induced distributional changes.
  arXiv  Detail & Related papers  (2026-05-09T15:30:52Z)
• Degradation of Feature Space in Continual Learning [2.322400467239964]
  We investigate whether promoting feature-space isotropy can enhance representation quality in continual learning.<n>We find that isotropic regularization fails to improve, and can in fact degrade, model accuracy in continual settings.
  arXiv  Detail & Related papers  (2026-02-06T10:26:34Z)
• Training instability in deep learning follows low-dimensional dynamical principles [24.97566911521709]
  Training unfolds as a high-dimensional dynamical system in which small perturbations to optimization, data, parameters, or learning signals can induce abrupt and irreversible collapse.<n>We propose a unified dynamical perspective that characterizes training stability as an intrinsic property of learning systems.
  arXiv  Detail & Related papers  (2026-01-19T15:37:45Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space [66.51378598755933]
  We propose a novel method, called Rotation Control Unlearning (RCU), to quantify and control the unlearning degree in the continuous unlearning process.<n>The skew symmetric loss is designed to construct the existence of the cognitive rotation space, where the changes of rotational angle can simulate the continuous unlearning process.<n> Experiments on multiple datasets confirm that our method without retained dataset achieves SOTA performance.
  arXiv  Detail & Related papers  (2025-09-30T03:59:29Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• The Importance of Being Lazy: Scaling Limits of Continual Learning [60.97756735877614]
  We show that increasing model width is only beneficial when it reduces the amount of feature learning, yielding more laziness.<n>We study the intricate relationship between feature learning, task non-stationarity, and forgetting, finding that high feature learning is only beneficial with highly similar tasks.
  arXiv  Detail & Related papers  (2025-06-20T10:12:38Z)
• Temporal-Difference Variational Continual Learning [77.92320830700797]
  We propose new learning objectives that integrate the regularization effects of multiple previous posterior estimations.<n>Our approach effectively mitigates Catastrophic Forgetting, outperforming strong Variational CL methods.
  arXiv  Detail & Related papers  (2024-10-10T10:58:41Z)
• Investigating the Edge of Stability Phenomenon in Reinforcement Learning [20.631461205889487]
  We explore the edge of stability phenomenon in reinforcement learning (RL) Despite significant differences to supervised learning, the edge of stability phenomenon can be present in off-policy deep RL. Our results suggest that, while neural network structure can lead to optimisation dynamics that transfer between problem domains, certain aspects of deep RL optimisation can differentiate it from domains such as supervised learning.
  arXiv  Detail & Related papers  (2023-07-09T15:46:27Z)
• Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation [5.147919654191323]
  This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation. The proposed framework is validated on a planar quadrotor example.
  arXiv  Detail & Related papers  (2021-12-15T15:57:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.