Markov Models

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6 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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Global Explanations for Multivariate Time Series Forecasting Models via $K$ -Order Markov Approximations
Jun 25, 2026Amadeo TunyiMultivariate Time Series Forecasting Surrogate Modeling
XITASO GmbH Austraße 35, 86153 Augsburg, Germany
Learning in Markovian bandits with non-observable states and constrained decision epochs
Jun 25, 2026Thomas Hira, Victor Boone, Urtzi Ayesta +1Multi-Armed Bandits Markov Models
IRIT, Université de Toulouse, CNRS, Toulouse INP, Toulouse, France · Ikerbasque-UPV/EHU, University of the Basque Country, Bilbao, Spain
Confidence Sequences for Online Statistical Model Checking of Markov Decision Processes
Jun 24, 2026Konstantin Kueffner, Tobias Meggendorfer, Maximilian Weininger +1Markov Decision Processes Uncertainty Quantification
Institute of Science and Technology Austria, Klosterneuburg, Austria · Lancaster University Leipzig, Leipzig, Germany · Ruhr-University Bochum, Bochum, Germany +1
Low-Complexity Policy Tessellations in Structured Markov Decision Processes
Jun 24, 2026Fredy PokouMarkov Decision Processes Reinforcement Learning
CRIStAL · Inria, University of Lille, CNRS, Centrale Lille Villeneuve-d’Ascq, France
Entropy Objectives in Markov Decision Processes
Jun 19, 2026S. Akshay, Raghav Goyal, Aditya Neeraje +1Markov Decision Processes Markov Models
Indian Institute of Technology Bombay, Mumbai, India. · Tata Institute of Fundamental Research, Mumbai, India.
Attractive and Repulsive Pattern Control in Sequence Generation
Jun 19, 2026Francois PachetMarkov Models Controllable Music Generation
Maturing Markov Decision Processes: Decision Making under Increasing Information and Shrinking Action Sets
Jun 17, 2026Jiaxi Liu, Aiping Yang, Yuhang Yang +4Markov Decision Processes Markov Models
Ant International · School of Economics, Sichuan University · School of Economics, Fudan University
Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process
Jun 15, 2026Jongmin Lee, Ernest K. Ryu, Vaneet AggarwalMarkov Decision Processes Reinforcement Learning
Seoul National University · UCLA · Purdue University
Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs
Jun 12, 2026Tianhao Wu, Matthew Zurek, Weina Wang +1Multi-Armed Bandits Robust Markov Decision Processes
Department of Industrial and Systems Engineering, University of Wisconsin-Madison · Department of Computer Sciences, University of Wisconsin-Madison · Computer Science Department, Carnegie Mellon University
A Continuous-Time Markov Chain Framework for Insertion Language Models
Jun 8, 2026Dhruvesh Patel, Benjamin Rozonoyer, Soumitra Das +3Markov Models Language Modeling
UMass Amherst · IBM Research · University of Toronto & Vijil
Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning
Jun 7, 2026Alvaro Gonzalez, M. H. Hasan Shovo, Ali AyubMarkov Models Assistive Robotics
Concordia University, Montreal, Quebec H3G 1M8, Canada
Bayesian learning for the stochastic shortest path problem
Jun 3, 2026Chon Wai Ho, Sumeetpal S. Singh, Jiaqi GuoMarkov Decision Processes Reinforcement Learning
Department of Engineering, University of Cambridge, UK · School of Mathematics and Physics, University of Wollongong, Wollongong, Australia
A Doeblin-Anchored Contrastive Chart for Learning Markov Transition Kernels
Jun 1, 2026Ao XuContrastive Learning Markov Models
School of Artificial Intelligence Jilin University No. 2699, Qianjin Street, Chaoyang District Changchun 130012, China · Zhongguancun Academy Daniufang 2nd Ring Road, Haidian District Beijing 100094, China
TERRA: Task-Embedded Reasoning and Representation Architecture for Cross-Domain Applications
Jun 1, 2026Shayan ShokriMarkov Decision Processes Markov Models
Humanpath Labs Inc.
Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
May 29, 2026Jonathan Colaço Carr, Prakash Panangaden, Doina Precup +1Pairwise Preference Learning Markov Decision Processes
Stanford University · School of Computer Science, McGill University, Montreal, Quebec, Canada · Mila - Quebec AI Institute, Montreal, Quebec, Canada +1
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
May 29, 2026Yike Zhao, Onno Eberhard, Malek Khammassi +2Partially Observable RL Markov Models
EPFL, Lausanne, Switzerland · Max Planck Institute for Intelligent Systems, Tübingen, Germany
libhmm: A Modern C++20 Library for Hidden Markov Models with Correct MLE Emission M-Steps
May 28, 2026Gary WolfmanParameter Estimation Markov Models
Independent Researcher
Sampling Data with Chains of Forward-Backward Diffusion Steps
May 26, 2026Hyunmo Kang, Noam Itzhak Levi, Corinna Elena Wegner +2Dynamical Systems Markov Chain Monte Carlo
Johns Hopkins University · EPFL · EPFL & University of Göttingen +1
On the Error-Correcting Effects of Stochasticity in Discrete Diffusion
May 26, 2026William Yuan, Sungwon Jeong, Amirali AghazadehMarkov Models Diffusion Sampling
Georgia Institute of Technology
Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection
May 15, 2026Chenwang Wu, Yiuming Cheung, Bo Han +2AI-Generated Text Detection Markov Models
Department of Computer Science, Hong Kong Baptist University, Hong Kong, China · School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510000, China · School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui 230000, China +1
Learning POMDP World Models from Observations with Language-Model Priors
May 13, 2026Valentin Six, Frederik Panse, Mathis Fajeau +7World Model Learning Markov Models
1Max Planck Institute for Intelligent Systems · University of Tübingen · 2IRIIS +3
Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed +2Markov Models MeanFlow
Department of Computer Science, University of Kentucky Lexington, Kentucky 40506, USA
Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs
May 12, 2026Joshua Wendland, Markel Zubia, Roman Andriushchenko +6Markov Models Learning with Missing Data
1Ruhr University Bochum · 2Brno University of Technology · 3Radboud University Nijmegen +2
Mixing Times of Glauber Dynamics on Masked Language Models
May 11, 2026Suvadip Sana, Sami Wolf, Neer Mehta +4Dynamical Systems Markov Chain Monte Carlo
Department of Statistics and Data Science Cornell University · Cornell University · Department Cornell of University Mathematics
Attribution-based Explanations for Markov Decision Processes
May 10, 2026Paul Kobialka, Andrea Pferscher, Francesco Leofante +3Feature Attribution Markov Decision Processes
University of Oslo, Norway · Imperial College London, United Kingdom · RWTH Aachen University, Germany
Exact Regular-Constrained Variable-Order Markov Generation via Sparse Context-State Belief Propagation
May 8, 2026François PachetConstrained Generative Modeling Constrained Decoding
LIP6, Sorbonne Université · Ynosound
Multi-Environment POMDPs with Finite-Horizon Objectives
May 8, 2026Léonard Brice, Filip Cano, Krishnendu Chatterjee +2Belief-Space Planning Markov Models
Institute of Science and Technology Austria
Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure
May 8, 2026Jiamin Xu, Jacqueline Maasch, Kyra GanMarkov Decision Processes Causal Representation Learning
Cornell Tech, New York, NY
On-line Learning in Tree MDPs by Treating Policies as Bandit Arms
May 6, 2026Anvay Shah, Ramsundar Anandanarayanan, Sharayu Moharir +1Markov Decision Processes Markov Models
Indian Institute of Technology Bombay · Mumbai, India
Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization
May 6, 2026Jan Rovirosa, Jesse SchmolzeMarkov Models Financial Time Series
University of Wisconsin – Madison, Madison, WI, USA

Jun 25, 2026, cs.LGJ/K move · Enter open · S save

Global Explanations for Multivariate Time Series Forecasting Models via $K$ -Order Markov Approximations

Amadeo Tunyi

XITASO GmbH Austraße 35, 86153 Augsburg, Germany

Multivariate Time Series Forecasting Surrogate Modeling Explainable Time Series Forecasting Markov Models Time Series Forecasting Causal Interpretability

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While many explainable AI (XAI) methods have been proposed, most are not designed for time-series forecasting models and often rely on the implicit assumption that timestamp features are independent. This assumption ignores the fundamental property of temporal dependence and can lead to explanations that violate the sequential and causal structure of the data. We introduce \textsc{KARMA}, a method for explaining time-series predictors by constructing a Markov surrogate model that captures the temporal dependencies learned by the predictor. Our approach revolves around three main aspects: identifying the minimal history length

K

that is predictively sufficient for the model, estimating the best-fitting

K

-order Markov transition kernel from the discretized history space, and a five-level global explanation hierarchy that can be derived from the Markov transition kernel, which we illustrate using real-world weather data (Beijing PM 2.5). We also certify using complex synthetic data with known true causal edges that KARMA (i) recovers the data causal structure as learned by the model via a controlled experiment and (ii) identifies temporal dependencies better than established attribution methods such as TimeSHAP.

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