Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling

• URL: http://arxiv.org/abs/2607.07083v1
• Date: Wed, 08 Jul 2026 07:12:17 GMT
• Title: Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling
• Abstract summary: Subsampling significantly reduces the number of measurements.<n>Active Deep Probabilistic Subsampling (A-DPS) jointly optimize both the subsampling pattern and the downstream task model.<n>Here, we enhance A-DPS by integrating a deterministic (fixed) prior-informed sampling pattern derived from the training dataset, along with group-based sampling via top-k sampling.
• Score: 4.1015533948392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subsampling significantly reduces the number of measurements, thereby streamlining data processing and transfer overhead, and shortening acquisition time across diverse real-world applications. The recently introduced Active Deep Probabilistic Subsampling (A-DPS) approach jointly optimizes both the subsampling pattern and the downstream task model, enabling instance- and subject-specific sampling trajectories and effective adaptation to new data at inference time. However, this approach does not fully leverage valuable dataset priors and relies on top-1 sampling, which can impede the optimization process. Herein, we enhance A-DPS by integrating a deterministic (fixed) prior-informed sampling pattern derived from the training dataset, along with group-based sampling via top-k sampling, to achieve more robust optimization, method we call Prior-aware and context-guided Group-based Active DPS (PGA-DPS). We also provide a theoretical analysis supporting improved optimization via group sampling, and validate this with empirical results. We evaluated PGA-DPS on three tasks: classification, image reconstruction, and segmentation, using the MNIST, CIFAR-10, fastMRI knee, and hyperspectral AeroRIT datasets, respectively. In every case, PGA-DPS outperformed A-DPS, DPS, and all other sampling methods.

Related papers

• TapSampling: Inference-Time Sampling with a Task-Progress-Understanding Verifier for Robotic Manipulation [61.35569005726248]
  Existing embodied control research demonstrates remarkable performance improvements by scaling training data and model size.<n>Non-deterministic generative models, such as diffusion and autoregressive models, have been widely adopted in the field of embodied control.<n>We propose textbfTapSampling, a plug-and-play framework for inference-time sampling.
  arXiv  Detail & Related papers  (2026-05-25T08:03:31Z)
• Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
  Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
  arXiv  Detail & Related papers  (2026-03-04T14:48:53Z)
• TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering [40.89512526196666]
  We propose a novel temporal filtration-enhanced approach to construct a high-quality positive sample set.<n>We provide theoretical insights into why TFPS can improve Recall@k and NDCG@k.
  arXiv  Detail & Related papers  (2026-02-26T01:32:45Z)
• VADE: Variance-Aware Dynamic Sampling via Online Sample-Level Difficulty Estimation for Multimodal RL [38.782188833641676]
  Group-based policy optimization methods like GRPO and GSPO have become standard for training multimodal models.<n>They suffer from a critical emphgradient vanishing problem when all responses within a group receive identical rewards.<n>We propose textbfVADE, a sampling framework via online sample-level difficulty textbfEstimation.
  arXiv  Detail & Related papers  (2025-11-24T08:59:54Z)
• Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization [37.54165341391688]
  We introduce a novel problem: Sample Scheduling for DPO.<n>We propose SamS, an efficient and effective algorithm that adaptively selects samples in each training batch.<n>This work points to a promising new direction for improving LLM alignment through batch-wise sample selection.
  arXiv  Detail & Related papers  (2025-06-08T10:26:09Z)
• Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization [66.67988187816185]
  We aim to emphscale up the number of on-policy samples via repeated random sampling to improve alignment performance.<n>Our experiments reveal that this strategy leads to a emphdecline in performance as the sample size increases.<n>We introduce a scalable preference data construction strategy that consistently enhances model performance as the sample scale increases.
  arXiv  Detail & Related papers  (2025-02-24T04:22:57Z)
• Preference Optimization with Multi-Sample Comparisons [53.02717574375549]
  We introduce a novel approach that extends post-training to include multi-sample comparisons.<n>These approaches fail to capture critical characteristics such as generative diversity and bias.<n>We demonstrate that multi-sample comparison is more effective in optimizing collective characteristics than single-sample comparison.
  arXiv  Detail & Related papers  (2024-10-16T00:59:19Z)
• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance.<n>Data selection has shown promise in identifying the most representative samples from the entire dataset.<n>We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios [79.02009938011447]
  We propose a sampling scheme, which selects optimal subsets of unlabeled samples with fixed batch size from the unlabeled data pool. Experimental results show its effectiveness on both classical Machine Learning (ML) and Deep Learning (DL) tasks.
  arXiv  Detail & Related papers  (2022-07-04T04:11:44Z)
• On Sampling Collaborative Filtering Datasets [9.041133460836361]
  We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. We develop an oracle, Data-Genie, which can suggest the sampling scheme that is most likely to preserve model performance for a given dataset.
  arXiv  Detail & Related papers  (2022-01-13T02:39:22Z)

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.