:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Toyota, Shoji, Fukumizu, Kenji
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2307.11972
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Compositional simulation-based inference for time series
by: Gloeckler, Manuel, et al.
Published: (2024)

Optimal Transport for Measures with Noisy Tree Metric
by: Le, Tam, et al.
Published: (2023)

State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards
by: Tanimoto, Yuto, et al.
Published: (2024)

Diffusion Models with Double Guidance: Generate with aggregated datasets
by: Yang, Yanfeng, et al.
Published: (2025)

DoubleGen: Debiased Generative Modeling of Counterfactuals
by: Luedtke, Alex, et al.
Published: (2025)

Neural-Kernel Conditional Mean Embeddings
by: Shimizu, Eiki, et al.
Published: (2024)

Generalized Sobolev Transport for Probability Measures on a Graph
by: Le, Tam, et al.
Published: (2024)

Pairwise Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model
by: Ikeda, Kotaro, et al.
Published: (2025)

Out-of-distribution Generalization for Total Variation based Invariant Risk Minimization
by: Wang, Yuanchao, et al.
Published: (2025)

Neural Fourier Transform: A General Approach to Equivariant Representation Learning
by: Koyama, Masanori, et al.
Published: (2023)

Fast Flow Matching based Conditional Independence Tests for Causal Discovery
by: Zhao, Shunyu, et al.
Published: (2026)

Transfer learning with affine model transformation
by: Minami, Shunya, et al.
Published: (2022)

Scalable Sobolev IPM for Probability Measures on a Graph
by: Le, Tam, et al.
Published: (2025)

Generalized Sobolev IPM for Graph-Based Measures
by: Le, Tam, et al.
Published: (2025)

An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph
by: Le, Tam, et al.
Published: (2025)

Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
by: Wang, Song, et al.
Published: (2025)

Invariant Graph Transformer for Out-of-Distribution Generalization
by: Liao, Tianyin, et al.
Published: (2025)

Deceptive Risk Minimization: Out-of-Distribution Generalization by Deceiving Distribution Shift Detectors
by: Majumdar, Anirudha
Published: (2025)

Flow Matching from Viewpoint of Proximal Operators
by: Fukumizu, Kenji, et al.
Published: (2026)

Flow matching achieves almost minimax optimal convergence
by: Fukumizu, Kenji, et al.
Published: (2024)

Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
by: Kinoshita, Yuri, et al.
Published: (2023)

Optimal Empirical Risk Minimization under Temporal Distribution Shifts
by: Jeong, Yujin, et al.
Published: (2025)

Invariant Risk Minimization Is A Total Variation Model
by: Lai, Zhao-Rong, et al.
Published: (2024)

Functional Risk Minimization
by: Alet, Ferran, et al.
Published: (2024)

Unsupervised Representation Learning - an Invariant Risk Minimization Perspective
by: Norman, Yotam, et al.
Published: (2025)

Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
by: Zhang, Simon, et al.
Published: (2026)

Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem
by: Tieu, Katherine, et al.
Published: (2025)

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

Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
by: Mao, Wenyu, et al.
Published: (2024)

Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
by: Chen, Chao, et al.
Published: (2024)

Environment-Conditioned Tail Reweighting for Total Variation Invariant Risk Minimization
by: Wang, Yuanchao, et al.
Published: (2026)

Optimal Ridge Regularization for Out-of-Distribution Prediction
by: Patil, Pratik, et al.
Published: (2024)

Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
by: Zou, Yingtian, et al.
Published: (2024)

Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
by: Huang, Wei, et al.
Published: (2026)

Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
by: Liu, Haoxin, et al.
Published: (2024)

Adversarially Robust Topological Inference
by: Vishwanath, Siddharth, et al.
Published: (2022)

Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection
by: Doorenbos, Lars, et al.
Published: (2024)

Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
by: Chen, Xuexin, et al.
Published: (2024)

diffIRM: A Diffusion-Augmented Invariant Risk Minimization Framework for Spatiotemporal Prediction over Graphs
by: Mo, Zhaobin, et al.
Published: (2024)

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization
by: Schapiro, Samuel, et al.
Published: (2024)