Cover Image

Distributional Dataset Distillation with Subtask Decomposition

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Qin, Tian, Deng, Zhiwei, Alvarez-Melis, David
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Label is Worth a Thousand Images in Dataset Distillation
by: Qin, Tian, et al.
Published: (2024)

Investigating the Robustness of Subtask Distillation under Spurious Correlation
by: Chormai, Pattarawat, et al.
Published: (2026)

What is Dataset Distillation Learning?
by: Yang, William, et al.
Published: (2024)

Sometimes I am a Tree: Data Drives Unstable Hierarchical Generalization
by: Qin, Tian, et al.
Published: (2024)

To Backtrack or Not to Backtrack: When Sequential Search Limits Model Reasoning
by: Qin, Tian, et al.
Published: (2025)

DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
by: Geuter, Jonathan, et al.
Published: (2025)

Understanding the Role of Functional Diversity in Weight-Ensembling with Ingredient Selection and Multidimensional Scaling
by: Rojas, Alex, et al.
Published: (2024)

Strongly Isomorphic Neural Optimal Transport Across Incomparable Spaces
by: Sotiropoulou, Athina, et al.
Published: (2024)

Boomerang Distillation Enables Zero-Shot Model Size Interpolation
by: Kangaslahti, Sara, et al.
Published: (2025)

Random Scaling of Emergent Capabilities
by: Zhao, Rosie, et al.
Published: (2025)

Identifying Selections for Unsupervised Subtask Discovery
by: Qiu, Yiwen, et al.
Published: (2024)

Continuous Language Model Interpolation for Dynamic and Controllable Text Generation
by: Kangaslahti, Sara, et al.
Published: (2024)

GraphTool-Instruction: Revolutionizing Graph Reasoning in LLMs through Decomposed Subtask Instruction
by: Wang, Rongzheng, et al.
Published: (2024)

Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks
by: Han, Shuai, et al.
Published: (2024)

Bispectral OT: Dataset Comparison using Symmetry-Aware Optimal Transport
by: Ma, Annabel, et al.
Published: (2025)

TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation
by: Zhang, Jiaqing, et al.
Published: (2025)

IGDA: Interactive Graph Discovery through Large Language Model Agents
by: Havrilla, Alex, et al.
Published: (2025)

Neural Bandit Based Optimal LLM Selection for a Pipeline of Subtasks
by: Atalar, Baran, et al.
Published: (2025)

WARC-Bench: Web Archive Based Benchmark for GUI Subtask Executions
by: Srivastava, Sanjari, et al.
Published: (2025)

Guided Speculative Inference for Efficient Test-Time Alignment of LLMs
by: Geuter, Jonathan, et al.
Published: (2025)

Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMs
by: Yang, Zhipeng, et al.
Published: (2025)

Distilling Long-tailed Datasets
by: Zhao, Zhenghao, et al.
Published: (2024)

Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training
by: Kim, Jaeyeon, et al.
Published: (2026)

OT on the Map: Quantifying Domain Shifts in Geographic Space
by: Zhang, Haoran, et al.
Published: (2026)

An Efficient NAS-based Approach for Handling Imbalanced Datasets
by: Yao, Zhiwei
Published: (2024)

Diffusion Models as Dataset Distillation Priors
by: Su, Duo, et al.
Published: (2025)

Technical Report on Text Dataset Distillation
by: Ogawa, Keith Ando, et al.
Published: (2025)

Subtask-Aware Visual Reward Learning from Segmented Demonstrations
by: Kim, Changyeon, et al.
Published: (2025)

DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
by: Maekawa, Aru, et al.
Published: (2024)

Group Distributionally Robust Dataset Distillation with Risk Minimization
by: Vahidian, Saeed, et al.
Published: (2024)

Dissimilar Batch Decompositions of Random Datasets
by: Ganesan, Ghurumuruhan
Published: (2025)

Not All Instances Are Equally Valuable: Towards Influence-Weighted Dataset Distillation
by: Deng, Qiyan, et al.
Published: (2025)

Exploration by Random Distribution Distillation
by: Fang, Zhirui, et al.
Published: (2025)

Decomposing Elements of Problem Solving: What "Math" Does RL Teach?
by: Qin, Tian, et al.
Published: (2025)

Harmonic Dataset Distillation for Time Series Forecasting
by: Hong, Seungha, et al.
Published: (2026)

Distilling Reinforcement Learning into Single-Batch Datasets
by: Wilhelm, Connor, et al.
Published: (2025)

Self-Supervised Dataset Distillation for Transfer Learning
by: Lee, Dong Bok, et al.
Published: (2023)

Finding Stable Subnetworks at Initialization with Dataset Distillation
by: McDermott, Luke, et al.
Published: (2025)

Prioritize Alignment in Dataset Distillation
by: Li, Zekai, et al.
Published: (2024)

A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)
by: Nayak, Nihal V., et al.
Published: (2026)