Cover Image

Information Geometry of Absorbing Markov-Chain and Discriminative Random Walks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Kimura, Masanari
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Graph-Smoothed Bayesian Black-Box Shift Estimator and Its Information Geometry
by: Kimura, Masanari
Published: (2025)

Theoretical and Practical Analysis of Fréchet Regression via Comparison Geometry
by: Kimura, Masanari, et al.
Published: (2025)

Higher-Order Asymptotics of Test-Time Adaptation for Batch Normalization Statistics
by: Kimura, Masanari
Published: (2025)

Edgeworth Expansion for Semi-hard Triplet Loss
by: Kimura, Masanari
Published: (2025)

Understanding Test-Time Augmentation
by: Kimura, Masanari
Published: (2024)

Heteroscedastic Double Bayesian Elastic Net
by: Kimura, Masanari
Published: (2025)

Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds
by: Kimura, Masanari, et al.
Published: (2024)

Geometric Insights into Focal Loss: Reducing Curvature for Enhanced Model Calibration
by: Kimura, Masanari, et al.
Published: (2024)

Generalized Power Priors for Improved Bayesian Inference with Historical Data
by: Kimura, Masanari, et al.
Published: (2025)

Test-Time Augmentation Meets Variational Bayes
by: Kimura, Masanari, et al.
Published: (2024)

A Short Survey on Importance Weighting for Machine Learning
by: Kimura, Masanari, et al.
Published: (2024)

Self-Repellent Random Walks on General Graphs -- Achieving Minimal Sampling Variance via Nonlinear Markov Chains
by: Doshi, Vishwaraj, et al.
Published: (2023)

True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration
by: Qinghua, et al.
Published: (2026)

Repelling Random Walks
by: Reid, Isaac, et al.
Published: (2023)

Distances for Markov Chains, and Their Differentiation
by: Brugère, Tristan, et al.
Published: (2023)

On permutation-invariant neural networks
by: Kimura, Masanari, et al.
Published: (2024)

Distributed Graph Embedding with Information-Oriented Random Walks
by: Fang, Peng, et al.
Published: (2023)

Random Walk Guided Hyperbolic Graph Distillation
by: Long, Yunbo, et al.
Published: (2025)

Towards Understanding Transformers in Learning Random Walks
by: Shi, Wei, et al.
Published: (2025)

INFO-SEDD: Continuous Time Markov Chains as Scalable Information Metrics Estimators
by: Foresti, Alberto, et al.
Published: (2025)

Pairwise Markov Chains for Volatility Forecasting
by: Azeraf, Elie
Published: (2024)

Revisiting Random Walks for Learning on Graphs
by: Kim, Jinwoo, et al.
Published: (2024)

Empirical PAC-Bayes Bounds for Markov Chains
by: Karagulyan, Vahe, et al.
Published: (2025)

Deep Thinking by Markov Chain of Continuous Thoughts
by: Liu, Jiayu, et al.
Published: (2025)

Coreset Markov Chain Monte Carlo
by: Chen, Naitong, et al.
Published: (2023)

Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property
by: Yoshikawa, Yuya, et al.
Published: (2024)

Linear Discriminant Analysis with the Randomized Kaczmarz Method
by: Chi, Jocelyn T., et al.
Published: (2022)

Learning Long Range Dependencies on Graphs via Random Walks
by: Chen, Dexiong, et al.
Published: (2024)

Descriptive Kernel Convolution Network with Improved Random Walk Kernel
by: Lee, Meng-Chieh, et al.
Published: (2024)

Causal Inference on Stopped Random Walks in Online Advertising
by: Yu, Jia Yuan
Published: (2026)

Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)

Representing Molecules as Random Walks Over Interpretable Grammars
by: Sun, Michael, et al.
Published: (2024)

A Practice in Enrollment Prediction with Markov Chain Models
by: Zhao, Yan, et al.
Published: (2024)

Markov Chain Estimation with In-Context Learning
by: Lepage, Simon, et al.
Published: (2025)

Stereographic Markov Chain Monte Carlo
by: Yang, Jun, et al.
Published: (2022)

Gaussian Invariant Markov Chain Monte Carlo
by: Titsias, Michalis K., et al.
Published: (2025)

A singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on $n$-dimensional Classes
by: Benfenati, Alessandro, et al.
Published: (2024)

Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks
by: Yi, Bongsoo, et al.
Published: (2021)

On the Statistical Query Complexity of Learning Semiautomata: a Random Walk Approach
by: Giapitzakis, George, et al.
Published: (2025)

Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Models
by: Liang, Yuchen, et al.
Published: (2025)