MODE: Multi-Objective Adaptive Coreset Selection

• URL: http://arxiv.org/abs/2512.21152v1
• Date: Wed, 24 Dec 2025 12:43:40 GMT
• Title: MODE: Multi-Objective Adaptive Coreset Selection
• Abstract summary: mode adapts selection criteria to training phases: emphasizing class balance early, diversity during representation learning, and uncertainty at convergence.<n>We show that MODE achieves (1-1/e)-approximation with O(n log n) complexity and demonstrates competitive accuracy while providing interpretable insights into data utility evolution.
• Score: 24.64208190328432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Mode(Multi-Objective adaptive Data Efficiency), a framework that dynamically combines coreset selection strategies based on their evolving contribution to model performance. Unlike static methods, \mode adapts selection criteria to training phases: emphasizing class balance early, diversity during representation learning, and uncertainty at convergence. We show that MODE achieves (1-1/e)-approximation with O(n \log n) complexity and demonstrates competitive accuracy while providing interpretable insights into data utility evolution. Experiments show \mode reduces memory requirements

Related papers

• Automating aggregation strategy selection in federated learning [0.0]
  We present an end-to-end framework that automates, streamlines, and adapts aggregation strategy selection for federated learning.<n>Our approach enhances robustness and generalisation under non-IID conditions while reducing the need for manual intervention.
  arXiv  Detail & Related papers  (2026-04-09T10:08:28Z)
• AdaptMMBench: Benchmarking Adaptive Multimodal Reasoning for Mode Selection and Reasoning Process [35.95284812390557]
  We propose AdaptMMBench, a benchmark for adaptive multimodal reasoning across five domains: real-world, OCR, GUI, knowledge, and math.<n>Our evaluation reveals that while adaptive mode selection scales with model capacity, it notably decouples from final accuracy.
  arXiv  Detail & Related papers  (2026-02-02T19:00:27Z)
• Improving Multimodal Sentiment Analysis via Modality Optimization and Dynamic Primary Modality Selection [54.10252086842123]
  Multimodal Sentiment Analysis (MSA) aims to predict sentiment from language, acoustic, and visual data in videos.<n>This paper proposes a modality optimization and dynamic primary modality selection framework (MODS)<n>Experiments on four benchmark datasets demonstrate that MODS outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-09T11:13:32Z)
• Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts [29.52183168979229]
  We propose SMoEStereo, a novel framework that adapts VFMs for stereo matching through a tailored, scene-specific fusion of Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE) modules.<n>Our method exhibits state-of-the-art cross-domain and joint generalization across multiple benchmarks without dataset-specific adaptation.
  arXiv  Detail & Related papers  (2025-07-07T03:19:04Z)
• MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings [75.0617088717528]
  MoCa is a framework for transforming pre-trained VLM backbones into effective bidirectional embedding models.<n>MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2025-06-29T06:41:00Z)
• Modality Equilibrium Matters: Minor-Modality-Aware Adaptive Alternating for Cross-Modal Memory Enhancement [13.424541949553964]
  We propose a Shapley-guided alternating training framework that adaptively prioritizes minor modalities to balance and thus enhance the fusion.<n>We evaluate the performance in both balance and accuracy across four multimodal benchmark datasets, where our method achieves state-of-the-art (SOTA) results.
  arXiv  Detail & Related papers  (2025-05-26T02:02:57Z)
• Enhancing Online Continual Learning with Plug-and-Play State Space Model and Class-Conditional Mixture of Discretization [72.81319836138347]
  Online continual learning (OCL) seeks to learn new tasks from data streams that appear only once, while retaining knowledge of previously learned tasks.<n>Most existing methods rely on replay, focusing on enhancing memory retention through regularization or distillation.<n>We introduce a plug-and-play module, S6MOD, which can be integrated into most existing methods and directly improve adaptability.
  arXiv  Detail & Related papers  (2024-12-24T05:25:21Z)
• Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning [115.79349923044663]
  Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples.<n>Existing methods face a critical dilemma: static architectures rely on a fixed parameter space to learn from data that arrive sequentially, prone to overfitting to the current session.<n>In this study, we explore the potential of Selective State Space Models (SSMs) for FSCIL.
  arXiv  Detail & Related papers  (2024-07-08T17:09:39Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation [23.667430143035787]
  We propose a novel MOdality DEcorrelating STable learning framework, MODEST for brevity, to learn users' stable preference. Inspired by sample re-weighting techniques, the proposed method aims to estimate a weight for each item, such that the features from different modalities in the weighted distribution are decorrelated. Our method could be served as a play-and-plug module for existing multimedia recommendation backbones.
  arXiv  Detail & Related papers  (2023-06-25T09:09:11Z)
• MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning [68.12870241637636]
  We propose MILO, a model-agnostic subset selection framework that decouples the subset selection from model training. Our empirical results indicate that MILO can train models $3times - 10 times$ faster and tune hyperparameters $20times - 75 times$ faster than full-dataset training or tuning without performance.
  arXiv  Detail & Related papers  (2023-01-30T20:59:30Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)
• Balancing Constraints and Submodularity in Data Subset Selection [43.03720397062461]
  We show that one can achieve similar accuracy to traditional deep-learning models, while using less training data. We propose a novel diversity driven objective function, and balancing constraints on class labels and decision boundaries using matroids.
  arXiv  Detail & Related papers  (2021-04-26T19:22:27Z)

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.