Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes

• URL: http://arxiv.org/abs/2405.10581v1
• Date: Fri, 17 May 2024 07:09:52 GMT
• Title: Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes
• Authors: Markus Lange-Hegermann, Christoph Zimmer,
• Abstract summary: This paper introduces a safe active learning framework tailored for time-varying systems. The proposed Time-aware Integrated Mean Squared Prediction Error (T-IMSPE) method minimizes posterior variance over current and future states.
• Score: 8.678546901075984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Experimental exploration of high-cost systems with safety constraints, common in engineering applications, is a challenging endeavor. Data-driven models offer a promising solution, but acquiring the requisite data remains expensive and is potentially unsafe. Safe active learning techniques prove essential, enabling the learning of high-quality models with minimal expensive data points and high safety. This paper introduces a safe active learning framework tailored for time-varying systems, addressing drift, seasonal changes, and complexities due to dynamic behavior. The proposed Time-aware Integrated Mean Squared Prediction Error (T-IMSPE) method minimizes posterior variance over current and future states, optimizing information gathering also in the time domain. Empirical results highlight T-IMSPE's advantages in model quality through toy and real-world examples. State of the art Gaussian processes are compatible with T-IMSPE. Our theoretical contributions include a clear delineation which Gaussian process kernels, domains, and weighting measures are suitable for T-IMSPE and even beyond for its non-time aware predecessor IMSPE.

Related papers

• Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models [79.28821338925947]
  Domain-Class Incremental Learning is a realistic but challenging continual learning scenario. To handle these diverse tasks, pre-trained Vision-Language Models (VLMs) are introduced for their strong generalizability. This incurs a new problem: the knowledge encoded in the pre-trained VLMs may be disturbed when adapting to new tasks, compromising their inherent zero-shot ability. Existing methods tackle it by tuning VLMs with knowledge distillation on extra datasets, which demands heavy overhead. We propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of
  arXiv  Detail & Related papers  (2024-07-07T12:19:37Z)
• VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation [0.0]
  This work introduces VIRL, a Volume-Informed Representation Learning approach to pre-train a 3D geometric encoder. The model pre-trained by VIRL shows substantial enhancements on demonstrating improved generalizability with limited data.
  arXiv  Detail & Related papers  (2024-06-18T05:30:26Z)
• Intrusion Detection System with Machine Learning and Multiple Datasets [0.0]
  In this paper, an enhanced intrusion detection system (IDS) that utilizes machine learning (ML) is explored. Ultimately, this improved system can be used to combat the attacks made by unethical hackers.
  arXiv  Detail & Related papers  (2023-12-04T14:58:19Z)
• Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization [51.34904967046097]
  Continual learning seeks to overcome the challenge of catastrophic forgetting, where a model forgets previously learnt information. We introduce a novel prior-based method that better constrains parameter growth, reducing catastrophic forgetting. Results show that BAdam achieves state-of-the-art performance for prior-based methods on challenging single-headed class-incremental experiments.
  arXiv  Detail & Related papers  (2023-09-15T17:10:51Z)
• Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Methods Integrated in Instrumentation & Control Systems [1.8978726202765634]
  Trustworthiness of datadriven neural network-based machine learning algorithms is not adequately assessed. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption. We demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset.
  arXiv  Detail & Related papers  (2023-08-08T18:25:42Z)
• Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials [0.0]
  We develop an ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage. The predictions achieve a good level of accuracy, similar to other ML algorithms.
  arXiv  Detail & Related papers  (2022-10-19T12:20:07Z)
• Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors [68.8204255655161]
  This paper compares four state-of-the-art algorithms in two real applications: gesture recognition based on accelerometer data and image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs.
  arXiv  Detail & Related papers  (2022-09-01T17:05:20Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [63.18590014127461]
  This paper introduces a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We study the feasibility of the resulting robust safety-critical controller. We then use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach [0.0]
  This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. It includes effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data. It also includes a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data.
  arXiv  Detail & Related papers  (2022-04-11T02:46:22Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [81.7983463275447]
  Learning-based control algorithms require data collection with abundant supervision for training. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
  arXiv  Detail & Related papers  (2020-05-09T05:57:43Z)

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.