Prediction-Oriented Subsampling from Data Streams

• URL: http://arxiv.org/abs/2508.03868v1
• Date: Tue, 05 Aug 2025 19:30:28 GMT
• Title: Prediction-Oriented Subsampling from Data Streams
• Authors: Benedetta Lavinia Mussati, Freddie Bickford Smith, Tom Rainforth, Stephen Roberts,
• Abstract summary: Key challenge for learning models from data streams is capturing relevant information while keeping computational costs manageable.<n>We argue for an information-theoretic method centred on reducing uncertainty in downstream predictions of interest.
• Score: 17.21293400236517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data is often generated in streams, with new observations arriving over time. A key challenge for learning models from data streams is capturing relevant information while keeping computational costs manageable. We explore intelligent data subsampling for offline learning, and argue for an information-theoretic method centred on reducing uncertainty in downstream predictions of interest. Empirically, we demonstrate that this prediction-oriented approach performs better than a previously proposed information-theoretic technique on two widely studied problems. At the same time, we highlight that reliably achieving strong performance in practice requires careful model design.

Related papers

• Robust Molecular Property Prediction via Densifying Scarce Labeled Data [51.55434084913129]
  In drug discovery, compounds most critical for advancing research often lie beyond the training set.<n>We propose a novel meta-learning-based approach that leverages unlabeled data to interpolate between in-distribution (ID) and out-of-distribution (OOD) data.<n>We demonstrate significant performance gains on challenging real-world datasets.
  arXiv  Detail & Related papers  (2025-06-13T15:27:40Z)
• StreamEnsemble: Predictive Queries over Spatiotemporal Streaming Data [0.8437187555622164]
  We propose StreamEnembles, a novel approach to predictive queries overtemporal (ST) data distributions. Our experimental evaluation reveals that this method markedly outperforms traditional ensemble methods and single model approaches in terms of accuracy and time.
  arXiv  Detail & Related papers  (2024-09-30T23:50:16Z)
• DRoP: Distributionally Robust Data Pruning [11.930434318557156]
  We conduct the first systematic study of the impact of data pruning on classification bias of trained models.<n>We propose DRoP, a distributionally robust approach to pruning and empirically demonstrate its performance on standard computer vision benchmarks.
  arXiv  Detail & Related papers  (2024-04-08T14:55:35Z)
• A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation [35.46631415365955]
  We introduce a conditional diffusion framework called C$2$TSD, which incorporates disentangled temporal (trend and seasonality) representations as conditional information. Our experiments on three real-world datasets demonstrate the superior performance of our approach compared to a number of state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-18T11:59:04Z)
• Re-thinking Data Availablity Attacks Against Deep Neural Networks [53.64624167867274]
  In this paper, we re-examine the concept of unlearnable examples and discern that the existing robust error-minimizing noise presents an inaccurate optimization objective. We introduce a novel optimization paradigm that yields improved protection results with reduced computational time requirements.
  arXiv  Detail & Related papers  (2023-05-18T04:03:51Z)
• Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models [6.7015527471908625]
  We study prediction of future outcomes with supervised models that use privileged information during learning. privileged information comprises samples of time series observed between the baseline time of prediction and the future outcome. We show that our approach is generally preferable to classical learning, particularly when data is scarce.
  arXiv  Detail & Related papers  (2021-10-28T10:07:29Z)
• A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data [0.0]
  We present a data-driven approach to automatically extract an appropriate model structure from experimentally observed processes. We demonstrate our approach on a simple benchmark problem, where it beats the state of the art meta-learning techniques.
  arXiv  Detail & Related papers  (2021-10-07T18:23:14Z)
• Representation Learning for Sequence Data with Deep Autoencoding Predictive Components [96.42805872177067]
  We propose a self-supervised representation learning method for sequence data, based on the intuition that useful representations of sequence data should exhibit a simple structure in the latent space. We encourage this latent structure by maximizing an estimate of predictive information of latent feature sequences, which is the mutual information between past and future windows at each time step. We demonstrate that our method recovers the latent space of noisy dynamical systems, extracts predictive features for forecasting tasks, and improves automatic speech recognition when used to pretrain the encoder on large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2020-10-07T03:34:01Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)
• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data [135.64775986546505]
  We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting. We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
  arXiv  Detail & Related papers  (2020-06-22T14:49:33Z)
• Focus of Attention Improves Information Transfer in Visual Features [80.22965663534556]
  This paper focuses on unsupervised learning for transferring visual information in a truly online setting. The computation of the entropy terms is carried out by a temporal process which yields online estimation of the entropy terms. In order to better structure the input probability distribution, we use a human-like focus of attention model.
  arXiv  Detail & Related papers  (2020-06-16T15:07:25Z)
• Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting [12.0855096102517]
  We present a representation learning framework for financial time series forecasting. In this paper, we propose to first learn compact representations from time series data, then use the learned representations to train a simpler model for predicting time series movements.
  arXiv  Detail & Related papers  (2020-02-18T15:24:33Z)

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.