Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes

• URL: http://arxiv.org/abs/2511.17399v1
• Date: Fri, 21 Nov 2025 17:00:00 GMT
• Title: Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes
• Authors: Wei-Kai Chang, Rajiv Khanna,
• Abstract summary: Coreset selection aims to accelerate training by identifying small, representative subsets of data that approximate the performance of the full dataset.<n> gradient based methods stand out due to their strong theoretical underpinnings and practical benefits, particularly under limited data budgets.<n>We propose a novel framework that addresses these limitations. First, we establish a connection between posterior sampling and loss landscapes, enabling robust coreset selection even in high data corruption scenarios.
• Score: 7.446140380340418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As deep learning models continue to scale, the growing computational demands have amplified the need for effective coreset selection techniques. Coreset selection aims to accelerate training by identifying small, representative subsets of data that approximate the performance of the full dataset. Among various approaches, gradient based methods stand out due to their strong theoretical underpinnings and practical benefits, particularly under limited data budgets. However, these methods face challenges such as naive stochastic gradient descent (SGD) acting as a surprisingly strong baseline and the breakdown of representativeness due to loss curvature mismatches over time. In this work, we propose a novel framework that addresses these limitations. First, we establish a connection between posterior sampling and loss landscapes, enabling robust coreset selection even in high data corruption scenarios. Second, we introduce a smoothed loss function based on posterior sampling onto the model weights, enhancing stability and generalization while maintaining computational efficiency. We also present a novel convergence analysis for our sampling-based coreset selection method. Finally, through extensive experiments, we demonstrate how our approach achieves faster training and enhanced generalization across diverse datasets than the current state of the art.

Related papers

• RS-Prune: Training-Free Data Pruning at High Ratios for Efficient Remote Sensing Diffusion Foundation Models [14.093802378976315]
  Diffusion-based remote sensing (RS) generative foundation models rely on large amounts of globally representative data.<n>We propose a training-free, two-stage data pruning approach that quickly select a high-quality subset under high pruning ratios.<n> Experiments show that, even after pruning 85% of the training data, our method significantly improves convergence and generation quality.
  arXiv  Detail & Related papers  (2025-12-29T06:44:06Z)
• Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining [55.262510814326035]
  Existing reweighting strategies primarily focus on group-level data importance.<n>We introduce novel algorithms for dynamic, instance-level data reweighting.<n>Our framework allows us to devise reweighting strategies deprioritizing redundant or uninformative data.
  arXiv  Detail & Related papers  (2025-02-10T17:57:15Z)
• Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction [48.30341580103962]
  We propose a novel re-visible dual-domain self-supervised deep unfolding network to address these issues.<n>We design a deep unfolding network based on Chambolle and Pock Proximal Point Algorithm (DUN-CP-PPA) to achieve end-to-end reconstruction.<n> Experiments conducted on the fastMRI and IXI datasets demonstrate that our method significantly outperforms state-of-the-art approaches in terms of reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-07T12:29:32Z)
• Adaptive Dataset Quantization [2.0105434963031463]
  We introduce a versatile framework for dataset compression, namely Adaptive dataset Quantization (ADQ)<n>We propose a novel adaptive sampling strategy through the evaluation of generated bins' representativeness score, diversity score and importance score.<n>Our method not only exhibits superior generalization capability across different architectures, but also attains state-of-the-art results, surpassing DQ by average 3% on various datasets.
  arXiv  Detail & Related papers  (2024-12-22T07:08:29Z)
• Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning [50.809769498312434]
  We propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS) Our method achieves 54.51% accuracy with only 10% training data, surpassing random selection by 7.83% and other comparison methods by at least 12.69%.
  arXiv  Detail & Related papers  (2023-11-22T03:45:30Z)
• Towards Accelerated Model Training via Bayesian Data Selection [45.62338106716745]
  We propose a more reasonable data selection principle by examining the data's impact on the model's generalization loss. Recent work has proposed a more reasonable data selection principle by examining the data's impact on the model's generalization loss. This work solves these problems by leveraging a lightweight Bayesian treatment and incorporating off-the-shelf zero-shot predictors built on large-scale pre-trained models.
  arXiv  Detail & Related papers  (2023-08-21T07:58:15Z)
• Adaptive Second Order Coresets for Data-efficient Machine Learning [5.362258158646462]
  Training machine learning models on datasets incurs substantial computational costs. We propose AdaCore to extract subsets of the training examples for efficient machine learning.
  arXiv  Detail & Related papers  (2022-07-28T05:43:09Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Online Coreset Selection for Rehearsal-based Continual Learning [65.85595842458882]
  In continual learning, we store a subset of training examples (coreset) to be replayed later to alleviate catastrophic forgetting. We propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting.
  arXiv  Detail & Related papers  (2021-06-02T11:39:25Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)

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.