:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yoshida, Kotaro, Slavakis, Konstantinos
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2505.16126
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Nonparametric Bellman Mappings for Value Iteration in Distributed Reinforcement Learning
by: Akiyama, Yuki, et al.
Published: (2025)

Nonconvex Regularization for Feature Selection in Reinforcement Learning
by: Suzuki, Kyohei, et al.
Published: (2025)

Gaussian-Mixture-Model Q-Functions for Policy Iteration in Reinforcement Learning
by: Vu, Minh, et al.
Published: (2025)

Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization
by: Vu, Minh, et al.
Published: (2024)

Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering
by: Akiyama, Yuki, et al.
Published: (2024)

Multilinear Kernel Regression and Imputation via Manifold Learning
by: Nguyen, Duc Thien, et al.
Published: (2024)

Online reinforcement learning via sparse Gaussian mixture model Q-functions
by: Vu, Minh, et al.
Published: (2025)

Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration
by: Yoshida, Kotaro, et al.
Published: (2024)

External Division of Two Bregman Proximity Operators for Poisson Inverse Problems
by: Haishima, Kazuki, et al.
Published: (2026)

Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning
by: Nguyen, Duc Thien, et al.
Published: (2024)

Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
by: Lin, Guang, et al.
Published: (2025)

Kernel Regression of Multi-Way Data via Tensor Trains with Hadamard Overparametrization: The Dynamic Graph Flow Case
by: Nguyen, Duc Thien, et al.
Published: (2025)

How Does Preconditioning Guide Feature Learning in Deep Neural Networks?
by: Yoshida, Kotaro, et al.
Published: (2025)

An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
by: Naganuma, Hiroki, et al.
Published: (2023)

Gradient Extrapolation for Debiased Representation Learning
by: Asaad, Ihab, et al.
Published: (2025)

Advancing Extrapolative Predictions of Material Properties through Learning to Learn
by: Noda, Kohei, et al.
Published: (2024)

DisTaC: Conditioning Task Vectors via Distillation for Robust Model Merging
by: Yoshida, Kotaro, et al.
Published: (2025)

Domain Generalization In Robust Invariant Representation
by: Gupta, Gauri, et al.
Published: (2023)

Identifying Representations for Intervention Extrapolation
by: Saengkyongam, Sorawit, et al.
Published: (2023)

Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
by: Liu, Rui, et al.
Published: (2025)

Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
by: Mai, Sijie, et al.
Published: (2026)

Continual Learning of Domain-Invariant Representations
by: Janetzky, Pascal, et al.
Published: (2026)

From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual Matching
by: Bai, Ruqi, et al.
Published: (2025)

Learning to Extrapolate to New Tasks: A Relational Approach to Task Extrapolation
by: Ousherovitch, Adam, et al.
Published: (2026)

Revisiting Generalization Measures Beyond IID: An Empirical Study under Distributional Shift
by: Nakai, Sora, et al.
Published: (2026)

Unsupervised Learning of Group Invariant and Equivariant Representations
by: Winter, Robin, et al.
Published: (2022)

Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
by: Wang, Jiyao, et al.
Published: (2026)

Engression: Extrapolation through the Lens of Distributional Regression
by: Shen, Xinwei, et al.
Published: (2023)

A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
by: Malekzadeh, Parvin, et al.
Published: (2024)

Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization
by: Li, Xiner, et al.
Published: (2023)

Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations
by: Slavutsky, Yuli, et al.
Published: (2023)

Distribution Shift Is Key to Learning Invariant Prediction
by: Zheng, Hong, et al.
Published: (2026)

Adversarial Representation Learning for Robust Privacy Preservation in Audio
by: Gharib, Shayan, et al.
Published: (2023)

Learning Repetition-Invariant Representations for Polymer Informatics
by: Zhu, Yihan, et al.
Published: (2025)

Causal Neighbourhood Learning for Invariant Graph Representations
by: Job, Simi, et al.
Published: (2026)

Tensor-Based Binary Graph Encoding for Variational Quantum Classifiers
by: An, Shiwen, et al.
Published: (2025)

Sufficient Invariant Learning for Distribution Shift
by: Kim, Taero, et al.
Published: (2022)

Learning Time-Scale Invariant Population-Level Neural Representations
by: Patel, Eshani, et al.
Published: (2025)

Robust Gradient Descent via Heavy-Ball Momentum with Predictive Extrapolation
by: Ali, Sarwan
Published: (2025)

Feedback-to-Rubrics: Can We Learn Expert Criteria from Inline Comments?
by: Yoshida, Kotaro, et al.
Published: (2026)