Cover Image

Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kungurtsev, Vyacheslav, Peng, Yuanfang, Gu, Jianyang, Vahidian, Saeed, Quinn, Anthony, Idlahcen, Fadwa, Chen, Yiran
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons
by: Kungurtsev, Vyacheslav, et al.
Published: (2024)

Efficient Dataset Distillation via Minimax Diffusion
by: Gu, Jianyang, et al.
Published: (2023)

CONCORD: Concept-Informed Diffusion for Dataset Distillation
by: Gu, Jianyang, et al.
Published: (2025)

Exploring the Impact of Dataset Bias on Dataset Distillation
by: Lu, Yao, et al.
Published: (2024)

CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
by: Wang, Qinsi, et al.
Published: (2024)

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
by: Kimiaei, Morteza, et al.
Published: (2025)

Machine Learning Algorithms for Improving Black Box Optimization Solvers
by: Kimiaei, Morteza, et al.
Published: (2025)

Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
by: Morafah, Mahdi, et al.
Published: (2024)

Stochastic Langevin Differential Inclusions with Applications to Machine Learning
by: Difonzo, Fabio V., et al.
Published: (2022)

Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size
by: Bozorgnia, Farid, et al.
Published: (2025)

Probabilistic Iterative Hard Thresholding for Sparse Learning
by: Bergamaschi, Matteo, et al.
Published: (2024)

"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"
by: Kungurtsev, Vyacheslav, et al.
Published: (2025)

Quasi-Ergodic Control of Multi-Periodic Autoregressive Processes: Formulation and Examples
by: Kungurtsev, Vyacheslav
Published: (2025)

A Three-Tier Time-Scale Architecture for Controlling Complex Nonlinear Systems
by: Kungurtsev, Vyacheslav
Published: (2026)

Tame Riemannian Stochastic Approximation
by: Aspman, Johannes, et al.
Published: (2023)

Taming Diffusion for Dataset Distillation with High Representativeness
by: Zhao, Lin, et al.
Published: (2025)

Learning Generalized Hamiltonians using fully Symplectic Mappings
by: Choudhary, Harsh, et al.
Published: (2024)

Binarizing Physics-Inspired GNNs for Combinatorial Optimization
by: Krutský, Martin, et al.
Published: (2025)

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
by: Cheruiyot, Kemboi, et al.
Published: (2025)

A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
by: Curtis, Frank E., et al.
Published: (2023)

Federated Sinkhorn
by: Kulcsar, Jeremy, et al.
Published: (2025)

Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management
by: Mastropietro, Daniel, et al.
Published: (2026)

Optimal Control of Two-Phase Membrane Problem
by: Bozorgnia, Farid, et al.
Published: (2024)

A Sequential Quadratic Programming Method for Optimization with Stochastic Objective Functions, Deterministic Inequality Constraints and Robust Subproblems
by: Qiu, Songqiang, et al.
Published: (2023)

Towards Tsallis Fully Probabilistic Design
by: Kungurtsev, Vyacheslav, et al.
Published: (2026)

Stochastic Approximation for Expectation Objective and Expectation Inequality-Constrained Nonconvex Optimization
by: Facchinei, Francisco, et al.
Published: (2023)

Grounding and Enhancing Informativeness and Utility in Dataset Distillation
by: Wang, Shaobo, et al.
Published: (2026)

Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
by: Kungurtsev, Vyacheslav, et al.
Published: (2024)

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

On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
by: Sun, Peng, et al.
Published: (2023)

Temporal Saliency-Guided Distillation: A Scalable Framework for Distilling Video Datasets
by: Gu, Xulin, et al.
Published: (2025)

Dataset Distillation for Offline Reinforcement Learning
by: Light, Jonathan, et al.
Published: (2024)

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)

QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation
by: Dhasade, Akash, et al.
Published: (2023)

Hebbian Learning from First Principles
by: Albanese, Linda, et al.
Published: (2024)

The stochastic Ravine accelerated gradient method with general extrapolation coefficients
by: Attouch, Hedy, et al.
Published: (2024)

Trilevel and Multilevel Optimization using Monotone Operator Theory
by: Shafiei, Allahkaram, et al.
Published: (2021)

Undecidable problems associated with variational quantum algorithms
by: Korpas, Georgios, et al.
Published: (2025)