Cover Image

Exponential convergence rate for Iterative Markovian Fitting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sokolov, Kirill, Korotin, Alexander
Format:	Preprint
Published:	2025
Subjects:	Information Theory Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Exponential Convergence Guarantees for Iterative Markovian Fitting
by: Silveri, Marta Gentiloni, et al.
Published: (2025)

Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting
by: Kholkin, Sergei, et al.
Published: (2024)

Monitoring State Transitions in Markovian Systems with Sampling Cost
by: Saurav, Kumar, et al.
Published: (2025)

Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
by: Chang, Serina, et al.
Published: (2024)

Exponential families from a single KL identity
by: Dymetman, Marc
Published: (2026)

Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
by: Barannikov, Serguei, et al.
Published: (2020)

Correcting Class Imbalance in Prior-Data Fitted Networks for Tabular Classification
by: McDowell, Samuel, et al.
Published: (2026)

Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence
by: Pananjady, Ashwin, et al.
Published: (2024)

On the Exponential Convergence for Offline RLHF with Pairwise Comparisons
by: Chen, Zhirui, et al.
Published: (2024)

Iterative Counterfactual Data Augmentation
by: Plyler, Mitchell, et al.
Published: (2025)

Exponentially Consistent Nonparametric Linkage-Based Clustering of Data Sequences
by: Singh, Bhupender, et al.
Published: (2024)

Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection
by: Cichocki, Andrzej, et al.
Published: (2024)

Variational Entropic Optimal Transport
by: Dyachenko, Roman, et al.
Published: (2026)

Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective
by: Puranik, Bhagyashree, et al.
Published: (2023)

An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
by: Düngler, Johanna, et al.
Published: (2025)

Minimax optimal submatrix detection: Sharp non-asymptotic rates
by: Knight, Parker, et al.
Published: (2026)

A Connection Between Learning to Reject and Bhattacharyya Divergences
by: Soen, Alexander
Published: (2025)

Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation
by: Schmid, Luca, et al.
Published: (2024)

Unifying Information-Theoretic and Pair-Counting Clustering Similarity
by: Gates, Alexander J.
Published: (2025)

Pointwise convergence of Fourier series and deep neural network for the indicator function of d-dimensional ball
by: Kawasumi, Ryota, et al.
Published: (2023)

Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
by: Kümmerle, Christian, et al.
Published: (2023)

Exponential quantum advantage in processing massive classical data
by: Zhao, Haimeng, et al.
Published: (2026)

On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy
by: Jia, Zeyu, et al.
Published: (2025)

Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
by: Kögler, Kevin, et al.
Published: (2024)

Mutual Information Estimation via Normalizing Flows
by: Butakov, Ivan, et al.
Published: (2024)

On the Properties and Estimation of Pointwise Mutual Information Profiles
by: Czyż, Paweł, et al.
Published: (2023)

Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability
by: Chen, Fan, et al.
Published: (2024)

Impossibility of latent inner product recovery via rate distortion
by: Mao, Cheng, et al.
Published: (2024)

Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
by: Chen, Fan, et al.
Published: (2025)

On the rates of convergence for learning with convolutional neural networks
by: Yang, Yunfei, et al.
Published: (2024)

Interpretive Efficiency: Information-Geometric Foundations of Data Usefulness
by: Katende, Ronald
Published: (2025)

Measuring the Predictability of Recommender Systems using Structural Complexity Metrics
by: Abeliuk, Andrés, et al.
Published: (2024)

Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
by: Li, Haoming, et al.
Published: (2024)

Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing
by: Matsumoto, Namiko, et al.
Published: (2022)

Connecting Jensen-Shannon and Kullback-Leibler Divergences: A New Bound for Representation Learning
by: Dorent, Reuben, et al.
Published: (2025)

Technical Report with Proofs for A Full Picture in Conformance Checking: Efficiently Summarizing All Optimal Alignments
by: Bär, Philipp, et al.
Published: (2025)

Bounds on the Excess Minimum Risk via Generalized Information Divergence Measures
by: Omanwar, Ananya, et al.
Published: (2025)

Rate-Distortion-Perception Theory for the Quadratic Wasserstein Space
by: Qu, Xiqiang, et al.
Published: (2025)

Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior
by: Sefidgaran, Milad, et al.
Published: (2025)

Balancing Client Participation in Federated Learning Using AoI
by: Javani, Alireza, et al.
Published: (2025)