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16 papers in the last four weeks, up 220% on the four weeks before. 0.2% of all new papers.

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Latest papers 102

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Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the $1+K$ Lower Bound
May 26, 2026Eichi UeharaCausal Discovery Methods Identifiability
aflo, Inc., Tokyo, Japan
Markovian Circuit Tracing for Transformer State Dynamic
May 20, 2026Abdullah XTransformer Architectures Hidden Markov Models
Project AWARE and Zephara AI
Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models
May 18, 2026Abdelhakim Ziani, Andras Horvath, Paolo BallariniLong-Tailed Distribution Generative Models
Université Paris Saclay, Lab. MICS, CentraleSupélec, Gif-sur-Yvette, France · Università di Torino, Torino, Italy
On Gaussian approximation for entropy-regularized Q-learning with function approximation
May 17, 2026Artemy Rubtsov, Rahul Singh, Eric Moulines +2Entropy Regularized Reinforcement Learning Q-Learning
HSE University, Moscow, Russia · Mohamed Bin Zayed University of AI, UAE · EPITA, France +1
MATE: Solving Contextual Markov Decision Processes with Memory of Accumulated Transition Embeddings
May 17, 2026Himchan Hwang, Hyeokju Jeong, Gene Chung +3Markov Decision Processes Markov
Seoul National University · Saige · Ulsan National Institute of Science and Technology (UNIST)
DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
May 14, 2026Manrui Jiang, Jingru Huang, Yong Chen +1Hidden Markov Models Latent States
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China · Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA 52242, USA.
Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed +2Meanflow Generative Flow Networks
Department of Computer Science, University of Kentucky Lexington, Kentucky 40506, USA
Model-based Bootstrap of Controlled Markov Chains
May 12, 2026Ziwei Su, Imon Banerjee, Diego KlabjanModel-Based Reinforcement Learning Model-Based Bootstrap
Department of Industrial Engineering and Management Sciences Northwestern University Evanston, IL 60208
Discrete Flow Matching for Offline-to-Online Reinforcement Learning
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong JuFlow Policies Offline Reinforcement Learning
Department of Computer Science, University of Kentucky
Optimal and Scalable MAPF via Multi-Marginal Optimal Transport and Schrödinger Bridges
May 11, 2026Usman A. Khan, Joseph W. DurhamMulti-Agent Path Finding Differentiable Optimal Transport
Amazon Robotics, Boston, MA, USA · Boston College, Chestnut Hill, MA, USA · UAK holds concurrent appointments as an Amazon Scholar with Amazon Robotics and as a Professor of Computer Science at Boston College
Mixing Times of Glauber Dynamics on Masked Language Models
May 11, 2026Suvadip Sana, Sami Wolf, Neer Mehta +4Markov Distributional Information
Department of Statistics and Data Science Cornell University · Cornell University · Department Cornell of University Mathematics
Weighted Rules under the Stable Model Semantics
May 10, 2026Joohyung Lee, Yi WangProbabilistic Model Markov
School of Computing, Informatics and Decision Systems Engineering Arizona State University, Tempe, USA
Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
May 9, 2026Le-Tuyet-Nhi Pham, Giovanni Conforti, Zhenjie Ren +1Conditional Flow Matching Generative Models
École Polytechnique, CMAP, IP Paris Route de Saclay, 91120 Palaiseau, France · Università degli Studi di Padova, Via Trieste, 63, 35131 Padova, Italia · LaMME, Université Évry Paris-Saclay Boulevard François Mitterrand 91000, France
Exact Regular-Constrained Variable-Order Markov Generation via Sparse Context-State Belief Propagation
May 8, 2026François PachetMarkov Automata
LIP6, Sorbonne Université · Ynosound
Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
May 8, 2026Erik Börve, Nikolce Murgovski, Morteza Haghir Chehreghani +1Model Predictive Control Robust Trajectory
Chalmers University of Technology, Chalmersgatan 4, 412 96 Göteborg, Sweden · Volvo Group Trucks Technology, Gropegårdsgatan 2, 417 15 Göteborg, Sweden
Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure
May 8, 2026Jiamin Xu, Jacqueline Maasch, Kyra GanCausal Representation Learning Offline Reinforcement Learning
Cornell Tech, New York, NY
A Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
May 7, 2026Ege C. Kaya, Abolfazl HashemiTemporal Difference Distributional Reinforcement Learning
Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47906
Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models
May 7, 2026Jose Benitez-Aurioles, Ricardo Silva, Brian McMillan +1Clinical Prediction Chronic Kidney Disease
Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom · Department of Statistical Science, UCL, London, United Kingdom · Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, United Kingdom
Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale
May 7, 2026Sunmin Oh, Sang-Yun Oh, Gunwoong ParkCausal Discovery Methods Linear Structural Equation Models
Department of Statistics, Seoul National University, South Korea · Department of Statistics and Applied Probability, University of California Santa Barbara, CA, USA · Scientific Data Division, Lawrence Berkeley National Laboratory, CA, USA +2
Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization
May 6, 2026Jan Rovirosa, Jesse SchmolzeMarkov Neural Dynamics
University of Wisconsin – Madison, Madison, WI, USA
Memory as a Markov Matrix: Sample Efficient Knowledge Expansion via Token-to-Dictionary Mapping
May 5, 2026Kaustubh Pethkar, Ziyang Xiong, Zuofeng Shang +1Large Language Model Memory Autoregressive Language Models
New Jersey Institute of Technology · University of California, Berkeley
Tree-Conditioned Edit Flows for Ancestral Sequence Reconstruction
May 5, 2026Emil Sharafutdinov, Ingemar AndréPhylogenetic Inference Sequence Modeling
Center for Molecular Protein Science Lund University Box 124, 221 00 Lund
Bridging the Gap Between Average and Discounted TD Learning
May 3, 2026Haoxing Tian, Zaiwei Chen, Ioannis Ch. Paschalidis +1Temporal Difference Bellman Equation
Department of Electrical and Computer Engineering Boston University · Edwardson School of Industrial Engineering Purdue University
QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
May 3, 2026Xing Lei, Jincheng Wang, Xuetao Zhang +1Goal-Conditioned Reinforcement Learning Sequence Modeling
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Computer Science, University College London · School of Engineering, Westlake University
Stable Blanket with Hidden Variables and Cycles
May 3, 2026Hanqing XiangCausal Graph Markov
Department of Mathematics, KTH Royal Institute of Technology, Sweden · University of Copenhagen
Stability and Generalization for Decentralized Markov SGD
May 3, 2026Jiahuan Wang, Ziqing Wen, Ping Luo +2Stochastic Gradient Descent Markov
National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha, 410073, China
Focus and Dilution: The Multi-stage Learning Process of Attention
May 2, 2026Zheng-An Chen, Pengxiao Lin, Zhi-Qin John Xu +1Transformer Attention Transformer Architectures
School of Mathematical Sciences, Shanghai Jiao Tong University. · Institute of Natural Sciences, Shanghai Jiao Tong University. · MOE-LSC, Shanghai Jiao Tong University. +2
Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation
May 1, 2026Andrzej Ruszczynski, Tiangang ZhangQ-Learning Markov
Department of Management Science and Information Systems, Rutgers University
From graphemic dependence to lexical structure: a Markovian perspective on Dante's Commedia
Apr 24, 2026Angelo Maria SabatiniMarkov Semantic Anchor
The BioRobotics Institute · Scuola Superiore Sant’Anna · Pisa, Italy
Markov reads Pushkin, again: A statistical journey into the poetic world of Evgenij Onegin
Apr 22, 2026Angelo Maria SabatiniStylometric Poetry
The BioRobotics Institute Scuola Superiore Sant’Anna Pisa, Italy
Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems
Mar 5, 2026Tomoki Yamagami, Mikio Hasegawa, Takatomo Mihana +2Autocorrelations Stochastic Multi-Armed Bandits
Department of Information and Computer Sciences, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama 338–8570, Japan. · Department of Electrical Engineering, Tokyo University of Science, 6–3–1, Niijuku, Katsushika-ku, Tokyo 125–8585, Japan. · Department of Information Physics and Computing, The University of Tokyo, 7–3–1 Hongo, Bunkyo-ku, Tokyo 113–8656, Japan.
Incremental Learning of Sparse Attention Patterns in Transformers
Feb 22, 2026Oğuz Kaan Yüksel, Rodrigo Alvarez Lucendo, Nicolas FlammarionTransformer Attention Transformer Architectures
TML Lab, EPFL
Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
Feb 16, 2026Daniil Dmitriev, Zhihan Huang, Yuting WeiScore-Based Diffusion Model Diffusion Dynamics
Department of Statistics and Data Science, the Wharton School, University of Pennsylvania
Calculating Mutual Information between a Reward Maximizer and its Environment
Feb 13, 2026Alfred Harwood, Jose Faustino, Alex AltairOptimal Policies Mutual Information
1Dovetail Research · University of Sao Paulo
Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning
Dec 1, 2025Sebastian Sanokowski, Kaustubh Patil, Majid KhadivDiffusion-Based Reinforcement Learning Methods Markov Decision Processes
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University Munich · Practical Project Student Exchange Program, Technical University Munich · MIT World Peace University
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
Feb 14, 2025Marco Bondaschi, Nived Rajaraman, Xiuying Wei +5Mamba-Based Models In-Context Learning
EPFL · UC Berkeley · Google DeepMind +1
Generalization Bounds for Markov Algorithms through Entropy Flow Computations
Feb 11, 2025Benjamin Dupuis, Maxime Haddouche, George Deligiannidis +1Generalization Bounds Markov
INRIA - D´epartement d’Informatique de l’Ecole Normale Sup´erieure PSL Research University - CNRS Paris, France · Department of Statistics, University of Oxford Oxford, UK
Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs
Oct 18, 2024Takashi Furuya, Anastasis KratsiosNeural Operators Stochastic Differential Equations
Doshisha University, Japan · Department of Mathematics McMaster University and Vector Institute, Canada
Towards Complete Causal Explanation with Expert Knowledge
Jul 10, 2024Aparajithan Venkateswaran, Emilija PerkovićAcyclic Graphs Causal
Microsoft · Redmond, WA 98052, USA · Department of Statistics +2
Double Fuzzy Probabilistic Interval Linguistic Term Set and a Dynamic Fuzzy Decision Making Model based on Markov Process with tts Application in Multiple Criteria Group Decision Making
Nov 30, 2021Zongmin LiuFuzzy Markov
Stanford University, Stanford, CA 94305 USA
A polynomial time algebraic solution to exact marginal inference in Markov Random Field models
Sep 25, 2017Ikhlef BecharProbabilistic Inference Markov
Aix Marseille Univ, Universit´e de Toulon, CNRS, ENSAM, LSIS La Garde 83957, France
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Date pendingPhilip Jordan, Maryam KamgarpourMean Field Games Nash Equilibrium
SYCAMORE, EPFL

May 26, 2026, stat.MLJ/K move · Enter open · S save

Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the $1+K$ Lower Bound

Eichi Uehara

aflo, Inc., Tokyo, Japan

Causal Discovery Methods Identifiability Finite-Sample Certificates Impossibility Oracles Lower Bounds+2

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Causal-discovery algorithms return a directed graph, yet provide no principled means of distinguishing edge directions identified by the data from those assigned without an identifying assumption. Under the standard Markov and faithfulness conditions, the observational distribution identifies only a Markov equivalence class; orientations within that class are not determined by the joint distribution and cannot be recovered from additional samples alone, but require either a functional restriction or an intervention. We introduce a protocol for observational causal discovery on continuous data that attaches to each candidate edge a discrete impossibility certificate: a RESOLVED code records the identifiability theorem under which the direction was committed, while an IMPOSSIBLE code records the failure mode together with the specific question a domain expert must answer to resolve it. The bivariate cascade is extended with five gated identifiability tiers LSNM, IGCI, Stein, MDL, and PEIT that abstain when their precondition test rejects. Two oracle primitives, the meta-hub query and the node-children query, jointly establish an upper bound of

1+K

expert interactions sufficient to recover any DAG, where

K

denotes the number of non-leaf vertices. Under an ideal-oracle assumption, the bound is met exactly on the asia, sachs, child, and alarm benchmarks.

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Latest papers 102