Compressive Meta-Learning

• URL: http://arxiv.org/abs/2508.11090v1
• Date: Thu, 14 Aug 2025 22:08:06 GMT
• Title: Compressive Meta-Learning
• Authors: Daniel Mas Montserrat, David Bonet, Maria Perera, Xavier Giró-i-Nieto, Alexander G. Ioannidis,
• Abstract summary: Compressive learning is a framework that enables efficient processing by using random, non-linear features.<n>We propose a framework that meta-learns both the encoding and decoding stages of compressive learning methods.<n>We explore multiple applications -- including neural network-based compressive PCA, compressive ridge regression, compressive k-means, and autoencoders.
• Score: 49.300635370079874
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid expansion in the size of new datasets has created a need for fast and efficient parameter-learning techniques. Compressive learning is a framework that enables efficient processing by using random, non-linear features to project large-scale databases onto compact, information-preserving representations whose dimensionality is independent of the number of samples and can be easily stored, transferred, and processed. These database-level summaries are then used to decode parameters of interest from the underlying data distribution without requiring access to the original samples, offering an efficient and privacy-friendly learning framework. However, both the encoding and decoding techniques are typically randomized and data-independent, failing to exploit the underlying structure of the data. In this work, we propose a framework that meta-learns both the encoding and decoding stages of compressive learning methods by using neural networks that provide faster and more accurate systems than the current state-of-the-art approaches. To demonstrate the potential of the presented Compressive Meta-Learning framework, we explore multiple applications -- including neural network-based compressive PCA, compressive ridge regression, compressive k-means, and autoencoders.

Related papers

• Seq2Seq2Seq: Lossless Data Compression via Discrete Latent Transformers and Reinforcement Learning [3.2641459166493405]
  We propose a novel compression method based on Reinforcement Learning applied to a T5 language model architecture.<n>This approach enables the compression of data into sequences of tokens rather than traditional vector representations.<n>By leveraging the latent information within language models, our system effectively compresses data without requiring explicit content understanding.
  arXiv  Detail & Related papers  (2026-02-12T16:30:55Z)
• Transformers from Compressed Representations [74.48571451824569]
  TEMPEST (TransformErs froM comPressed rEpreSenTations) is a method that exploits the inherent byte-stream structure of compressed files to design an effective tokenization and encoding strategy.<n>Our proposal substantially reduces the number of tokens required for semantic classification, thereby lowering both computational complexity and memory usage.
  arXiv  Detail & Related papers  (2025-10-26T13:48:03Z)
• Private Training & Data Generation by Clustering Embeddings [74.00687214400021]
  Differential privacy (DP) provides a robust framework for protecting individual data.<n>We introduce a novel principled method for DP synthetic image embedding generation.<n> Empirically, a simple two-layer neural network trained on synthetically generated embeddings achieves state-of-the-art (SOTA) classification accuracy.
  arXiv  Detail & Related papers  (2025-06-20T00:17:14Z)
• Learning Structured Compressed Sensing with Automatic Resource Allocation [10.298464166235272]
  Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software.<n>We introduce Structured COmpressed Sensing with Automatic Resource Allocation (SCOSARA) with an information theory-based unsupervised learning strategy.
  arXiv  Detail & Related papers  (2024-10-24T17:53:33Z)
• Compressed Predictive Information Coding [6.220929746808418]
  We develop a novel information-theoretic framework, Compressed Predictive Information Coding (CPIC), to extract useful representations from dynamic data. We derive variational bounds of the CPIC loss which induces the latent space to capture information that is maximally predictive. We demonstrate that CPIC is able to recover the latent space of noisy dynamical systems with low signal-to-noise ratios.
  arXiv  Detail & Related papers  (2022-03-03T22:47:58Z)
• COIN++: Data Agnostic Neural Compression [55.27113889737545]
  COIN++ is a neural compression framework that seamlessly handles a wide range of data modalities. We demonstrate the effectiveness of our method by compressing various data modalities.
  arXiv  Detail & Related papers  (2022-01-30T20:12:04Z)
• Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution [71.29848468762789]
  We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel's measurements are given as convolution of a common source signal and sparse filter. We demonstrate that our method is superior to classical structured compressive sparse multichannel blind-deconvolution methods in terms of accuracy and speed of sparse filter recovery.
  arXiv  Detail & Related papers  (2020-10-22T02:34:33Z)
• 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)
• Sketching Datasets for Large-Scale Learning (long version) [24.478376776509045]
  "Compressive learning" is an approach to large-scale machine learning where datasets are massively compressed before learning. A sketch is first constructed by computing carefully chosen nonlinear random features and averaging them over the whole dataset. Parameters are then learned from the sketch, without access to the original dataset.
  arXiv  Detail & Related papers  (2020-08-04T21:29:05Z)
• Multilinear Compressive Learning with Prior Knowledge [106.12874293597754]
  Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system. Key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable?
  arXiv  Detail & Related papers  (2020-02-17T19:06:05Z)

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.