Copertina

Learning Representations for Independence Testing

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Xu, Nathaniel, Liu, Feng, Sutherland, Danica J.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
di: Zhou, Zhijian, et al.
Pubblicazione: (2025)

Practical Kernel Tests of Conditional Independence
di: Pogodin, Roman, et al.
Pubblicazione: (2024)

On the Hardness of Conditional Independence Testing In Practice
di: He, Zheng, et al.
Pubblicazione: (2025)

Understanding Simplicity Bias towards Compositional Mappings via Learning Dynamics
di: Ren, Yi, et al.
Pubblicazione: (2024)

Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
di: Bae, Wonho, et al.
Pubblicazione: (2023)

Learning Dynamics of LLM Finetuning
di: Ren, Yi, et al.
Pubblicazione: (2024)

Generalized Coverage for More Robust Low-Budget Active Learning
di: Bae, Wonho, et al.
Pubblicazione: (2024)

Uncertainty Herding: One Active Learning Method for All Label Budgets
di: Bae, Wonho, et al.
Pubblicazione: (2024)

Efficient kernelized bandit algorithms via exploration distributions
di: Hu, Bingshan, et al.
Pubblicazione: (2025)

Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics
di: Wei, Aaron, et al.
Pubblicazione: (2025)

Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition
di: Mohamadi, Mohamad Amin, et al.
Pubblicazione: (2024)

A Theory for Compressibility of Graph Transformers for Transductive Learning
di: Shirzad, Hamed, et al.
Pubblicazione: (2024)

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
di: Komanduri, Aneesh, et al.
Pubblicazione: (2023)

Toward Scalable and Valid Conditional Independence Testing with Spectral Representations
di: Frohlich, Alek, et al.
Pubblicazione: (2025)

SeedER: Seed-and-Expand Retrieval from Knowledge Graphs
di: Shirzad, Hamed, et al.
Pubblicazione: (2026)

Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
di: Ren, Yi, et al.
Pubblicazione: (2024)

Independence Testing for Temporal Data
di: Shen, Cencheng, et al.
Pubblicazione: (2019)

Continual Learning of Nonlinear Independent Representations
di: Sun, Boyang, et al.
Pubblicazione: (2024)

Learning Interpretable PDE Representations for Generative Reconstructions with Structured Sparsity
di: Tsao, Valerie, et al.
Pubblicazione: (2026)

Relative Representations: Topological and Geometric Perspectives
di: García-Castellanos, Alejandro, et al.
Pubblicazione: (2024)

Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning
di: Deng, Wenlong, et al.
Pubblicazione: (2025)

Even Sparser Graph Transformers
di: Shirzad, Hamed, et al.
Pubblicazione: (2024)

A Unified Data Representation Learning for Non-parametric Two-sample Testing
di: Tian, Xunye, et al.
Pubblicazione: (2024)

Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction
di: Ruan, Jiafa, et al.
Pubblicazione: (2026)

Amortized Conditional Independence Testing
di: Duong, Bao, et al.
Pubblicazione: (2025)

Unlearning Evaluation through Subset Statistical Independence
di: Zhang, Chenhao, et al.
Pubblicazione: (2026)

On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization
di: Deng, Wenlong, et al.
Pubblicazione: (2025)

Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
di: Nipu, Ayesha Siddika, et al.
Pubblicazione: (2024)

Independence Constrained Disentangled Representation Learning from Epistemological Perspective
di: Wang, Ruoyu, et al.
Pubblicazione: (2024)

Testing Support Size More Efficiently Than Learning Histograms
di: Pinto Jr., Renato Ferreira, et al.
Pubblicazione: (2024)

Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
di: Yang, Yilin, et al.
Pubblicazione: (2023)

Independence Tests for Language Models
di: Zhu, Sally, et al.
Pubblicazione: (2025)

Empirically Calibrated Conditional Independence Tests
di: Pan, Milleno, et al.
Pubblicazione: (2026)

Causal Discovery via Conditional Independence Testing with Proxy Variables
di: Liu, Mingzhou, et al.
Pubblicazione: (2023)

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
di: Friedl, Katharina, et al.
Pubblicazione: (2025)

Geometry of Uncertainty: Learning Metric Spaces for Multimodal State Estimation in RL
di: Reichlin, Alfredo, et al.
Pubblicazione: (2026)

Mechanistic Independence: A Principle for Identifiable Disentangled Representations
di: Matthes, Stefan, et al.
Pubblicazione: (2025)

Goal-Conditioned Reinforcement Learning from Sub-Optimal Data on Metric Spaces
di: Reichlin, Alfredo, et al.
Pubblicazione: (2024)

Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries
di: Ngu, Noel, et al.
Pubblicazione: (2023)

Conditional Diffusion Models Based Conditional Independence Testing
di: Yang, Yanfeng, et al.
Pubblicazione: (2024)