Markov Chain Monte Carlo

Also known as MCMC

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Latest papers 145

27 papers in the last four weeks, up 50% on the four weeks before. 0.2% of all new papers.

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True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration
May 28, 2026Qinghua, Ding, Venkat AnantharamMarkov Chain Monte Carlo
Devon · Department of Electrical Engineering and Computer Sciences University of California at Berkeley Berkeley, CA, United States
Reasoning with Sampling: Cutting at Decision Points
May 28, 2026Felix Zhou, Anay Mehrotra, Quanquan C. LiuMarkov Chain Monte Carlo Efficient Language Model Reasoning
Yale University · Stanford University
Solving Integer Linear Programming with Parallel Tempering
May 28, 2026Kyuil Sim, Sanghyeok Choi, Jinkyoo ParkMarkov Chain Monte Carlo Mixed-Integer Linear Programming
1KAIST · University of Edinburgh
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
The Normalized Maximum Likelihood for Regular Non-Smooth Models: Measure-Theoretic Foundations and Geometric Sampling
May 23, 2026Trenton Lau, Gary P. T. ChoiMarkov Chain Monte Carlo
Department of Mathematics, The Chinese University of Hong Kong, Hong Kong
Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
May 22, 2026Jaihoon Kim, Taehoon Yoon, Prin Phunyaphibarn +3Markov Chain Monte Carlo Amortized Inference
1KAIST · University of Michigan · 3NVIDIA
Comment on "Spin-1/2 Kagome Heisenberg Antiferromagnet: Machine Learning Discovery of the Spinon Pair-Density-Wave Ground State"
May 21, 2026Helia Kamal, Dominik Kufel, DinhDuy Vu +2Neural Quantum States Markov Chain Monte Carlo
Department of Physics, Harvard University, Cambridge, MA 02138, USA · Harvard Quantum Initiative, Cambridge, MA 02138, USA · Department of Physics, Boston University, Boston, MA 02215, USA
AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers
May 18, 2026Jungang Zou, Alex Ziyu Jiang, Qixuan ChenMarkov Chain Monte Carlo Bayesian Inference
Department of Biostatistics, Columbia University
Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue
May 13, 2026Ali Al-Lawati, Nafis Tripto, Abolfazl Ansari +3Markov Chain Monte Carlo Intent Classification
The Pennsylvania State University, USA
Manifold Sampling via Entropy Maximization
May 12, 2026Cornelius V. Braun, Tilman Burghoff, Marc ToussaintMarkov Chain Monte Carlo
Technische Universität Berlin · Robotics Institute Germany
$h$ -control: Training-Free Camera Control via Block-Conditional Gibbs Refinement
May 12, 2026Yuzhu Wang, Xi Ye, Duo Su +2Markov Chain Monte Carlo Camera-Controlled Video Generation
Department of Computer Science and Technology, Tsinghua University · South China University of Technology
Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theory
May 11, 2026Moxian QianFlow Matching Markov Chain Monte Carlo
Johannes Gutenberg University Mainz, 55128 Mainz, Germany
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
Metropolis-Adjusted Diffusion Models
May 10, 2026Kevin H. Lam, Tyler Farghly, Christopher Williams +3Markov Chain Monte Carlo Diffusion Sampling
Department of Statistics, University of Oxford · Inria, CNRS, École Normale Supérieure, PSL Research University · Department of Mathematical Sciences, University of Copenhagen
Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
May 9, 2026Jannik Graebner, Ryne BeesonMarkov Chain Monte Carlo Trajectory Optimization
PhD Candidate, Department of Mechanical and Aerospace Engineering, Princeton University, NJ, USA. · Assistant Professor, Department of Mechanical and Aerospace Engineering, Princeton University, NJ, USA.
Time-Inhomogeneous Preconditioned Langevin Dynamics
May 7, 2026Alexander Falk, Laurenz Nagler, Andreas Habring +1Markov Chain Monte Carlo Langevin Dynamics
Institute of Visual Computing Graz University of Technology Inffeldgasse 16, 8010 Graz
Conditional Diffusion Sampling
May 5, 2026Francisco M. Castro-Macías, Pablo Morales-Álvarez, Saifuddin Syed +3Markov Chain Monte Carlo Diffusion Sampling
University of Granada · Work done while visiting the University of Cambridge. · University of British Columbia +2
Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
May 5, 2026Kisung YouMarkov Chain Monte Carlo
Department of Mathematics, Baruch College · Department of Mathematics, The Graduate Center, City University of New York
Decentralized Proximal Stochastic Gradient Langevin Dynamics
May 1, 2026Mohammad Rafiqul Islam, Lingjiong ZhuMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics, Florida State University, Tallahassee, Florida, United States of America
Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision
May 1, 2026Jiali Cui, Zhiqiang Lao, Heather YuVariational Autoencoders Markov Chain Monte Carlo
Basking Ridge, New Jersey, USA Futurewei Technologies Inc · Basking Ridge, New Jersey, USA2026 Futurewei Technologies Inc
Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
Apr 28, 2026Xianghao Meng, James L. Beck, Yong Huang +1Markov Chain Monte Carlo Meta-Learning
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China · Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China · Division of Engineering and Applied Science, California Institute of Technology, CA, USA
Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
Apr 24, 2026Jie Hu, Lingyun Chen, Geeho Kim +3Markov Chain Monte Carlo Variance Reduction
CSE, Oakland University, Rochester, USA · ECE, North Carolina State University, Raleigh, USA · ECE +2
Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics
Apr 23, 2026Heng YangMarkov Chain Monte Carlo Trajectory Optimization
Harvard University
Learning to Solve the Quadratic Assignment Problem with Warm-Started MCMC Finetuning
Apr 22, 2026Yicheng Pan, Ruisong Zhou, Haijun Zou +2Markov Chain Monte Carlo Combinatorial Optimization
School of Mathematical Science, Peking University, Beijing 100871, China · State Key Laboratory of Mathematical Sciences, Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China · Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China
Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
Apr 20, 2026Emanuel Sommer, Rickmer Schulte, Sarah Deubner +2Markov Chain Monte Carlo Early Stopping
LMU Munich, MCML
Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning
Apr 18, 2026Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen +1Markov Chain Monte Carlo Bayesian Inverse Problems
Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · Agency for Science, Technology and Research, Singapore · Johns Hopkins University +1
Decentralized Learning via Random Walk with Jumps
Apr 14, 2026Zonghong Liu, Matthew Dwyer, Salim El RouayhebDecentralized Learning Markov Chain Monte Carlo
Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA · DEVCOM Army Research Laboratory, Adelphi, MD, USA
Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging
Apr 12, 2026Pinaki Mohanty, Ruqi ZhangMarkov Chain Monte Carlo
Department of Computer Science, College of Science & College of Engineering, Purdue University, West Lafayette, IN, USA
Reconsidering Dependency Networks from an Information Geometry Perspective
Apr 1, 2026Kazuya Takabatake, Shotaro AkahoGraph Structure Learning Markov Chain Monte Carlo
Independent Researcher, Izumisano, Osaka, Japan · The Institute of Statistical Mathematics, Tachikawa, Tokyo, 190-8565, Japan
Rapid mixing in positively weighted restricted Boltzmann machines
Apr 1, 2026Weiming Feng, Heng Guo, Minji YangRestricted Boltzmann Machines Markov Chain Monte Carlo
School of Computing and Data Science, The University of Hong Kong, HK · School of Informatics, University of Edinburgh, Informatics Forum, UK

May 28, 2026J/K move · Enter open · S save

True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration

Qinghua, Ding, Venkat Anantharam

Devon · Department of Electrical Engineering and Computer Sciences University of California at Berkeley Berkeley, CA, United States

Markov Chain Monte Carlo

Open paper AskPDF arXiv

We study true self-avoiding walk (TSAW) as a mechanism for improving empirical integral estimation via Markov chain Monte Carlo (MCMC). We consider finite-state adaptive sampling dynamics associated with an irreducible Markov kernel

P

on a finite set, with stationary distribution

π

, in which the transition probabilities are penalized according to empirical overuse. Our main result is that the empirical occupation counts

L_t(i)

and transition counts

N_t(i,j)

of the resulting TSAW-based walk satisfy

L_t(i)-tπ_i = O(\sqrt{\log t}) \quad\text{and}\quad N_t(i,j)-tπ_iP_{ij}=O(\sqrt{\log t}) \qquad\text{almost surely}

for every state

i

and every edge

(i,j)

with

P_{ij}>0

. Consequently, for every bounded function

f:V\to\mathbb R

, the error of our integral estimator converges as

\left|\frac1t\sum_{s=0}^{t-1} f(X_s)-\sum_{i\in V}π_i f(i)\right| = O\left(\frac{\sqrt{\log t}}{t}\right) \qquad\text{almost surely}.

These results show that, in contrast with the usual

t^{-1/2}

error scaling for empirical averages under standard random-walk-based methods, TSAW-based estimator yields empirical integral errors of order

O(\sqrt{\log t}/t)

almost surely, thereby achieving a substantially sharper dependence on the sample size

t