Stochastic Processes

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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From Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps
Sep 27, 2026Yichen Wang, Ziyi Wang, Wenlian Lu +5Probability Measures Stochastic Processes
Shanghai Center for Mathematical Sciences, Shanghai, China · Department of Mathematics, Fudan University, Shanghai, China · Center for Applied Mathematics, Fudan University, Shanghai, China +2
From Processing to Functionality: Engineering Accessible Material States in Cu-Embedded SiO $_x$ Memristive Devices
Sep 24, 2026Tobias Gergs, Rouven Lamprecht, Sahitya Yarragolla +5Reram Crossbar Stochastic Processes
Theoretical Electrical Engineering, Department of Electrical and Information Engineering, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany · Chair of Applied Electrodynamics and Plasma Technology, Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, 44780 Bochum, Germany · Fraunhofer Institute for Electronic Nano Systems ENAS, 09126 Chemnitz, Germany +4
StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean
Sep 8, 2026Idan Davidovich, Debargha Ganguly, Vikash Singh +1Theorem Proving Stochastic Processes
Case Western Reserve University
On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective
Aug 13, 2026Nestor R. Barraza, Gabriel PenaFundamental Limits Probabilistic Inference
Universidad Nacional de Tres de Febrero. Departamento de Ciencia y Tecnología. · Universidad de Buenos Aires. Facultad de Ingeniería.
Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation
Aug 11, 2026Yueting Zhu, Yuehao Song, Kaicheng Zhang +5Video Diffusion Models Video Generation
Huazhong University of Science & Technology · Anyverse Dynamics · Horizon Robotics
Robust Control under Stationary Ambiguity
Aug 5, 2026Konrad J. Mueller, Amira Akkari, Ben Wood +1Stochastic Optimal Control Robust Control
1Imperial College London · 2JPMorgan Chase & Co.† · University of St. Gallen
Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics
Jul 20, 2026Vignesh Tirukkonda, Gautam DasarathyProbabilistic Graphical Models Optimal Learner
Arizona State University
Interpreting Quantum Learning Models via Stochastic Processes
Jul 19, 2026Johannes Fankhauser, Lukas J. Fiderer, Hans J. BriegelQuantum Learning Stochastic Processes
University of Innsbruck, Institute for Theoretical Physics, Technikerstrasse 21a, A-6020 Innsbruck, Austria
A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
Jul 17, 2026Owen Lockwood, Jérémy Béjanin, Joost Bus +4Thermodynamics Inference Workloads
Extropic Corporation, San Francisco, California 94111, USA · 2Noumenal Labs Inc, Dallas, Texas 75229, USA · 1Extropic Corporation, San Francisco, California 94111, USA +1
LatentFlow: A General Framework for Conditioning Stochastic Processes
Jul 14, 2026Louis Sharrock, Lachlan Astfalck, Henry MossStochastic Processes Latent Flow
University College London · University of New South Wales · Lancaster University
Graphical conditional generative modeling for digital twin modeling
Jun 15, 2026Zongren Zou, Théo Bourdais, Ricardo Baptista +1Cognitive Digital Twins Surrogate Models
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA · Department of Statistical Sciences, University of Toronto, Toronto, Canada
Nonlocal Mean Field Schrödinger Bridge with Learned Interactions
Jun 2, 2026Daisuke Inoue, Dante Kalise, Mathieu LaurièreSchrödinger Bridges Mean-Field Limit
Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom. · Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, NYU-ECNU Institute of Mathematical Sciences, NYU Shanghai, Shanghai 200126, People’s Republic of China.
Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy
May 28, 2026Moyi Tian, Daniel A. Messenger, Vanja Dukic +2System Identification Network Science
Department of Applied Mathematics, University of Colorado, Boulder, CO 80309 United States · Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545 United States
Estimated Dynamic Equilibrium Model: Supply and Demand as a Sample Path of a Stochastic Process
May 14, 2026Mikhail L. Arbuzov, Sisong Bei, Alexey ShvetsEquilibrium Stochastic Processes
Independent Researcher · Palo Alto Networks
Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions
May 12, 2026Alexander Shen, Mikael KuuselaSurrogates Uncertainty Score
Department of Statistics and Data Science Carnegie Mellon University Pittsburgh, PA 15213
Unifying Deep Stochastic Processes for Image Enhancement
May 2, 2026Wojciech Kozłowski, Radosław Kuczbański, Kamil Adamczewski +2Image Enhancement Diffusion Bridges
Wrocław University of Science and Technology, Wrocław, Poland · Tooploox, Warsaw, Poland
ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
Apr 30, 2026Gabe Guo, Thanawat Sornwanee, Lutong Hao +3Stochastic Processes Autoregressive Model
Stanford University
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.
A Jump-Diffusion Framework for Irregular Time Series Generation
May 29, 2025O. Pfohl, J. Chemseddine, P. Hagemann +3Time-Series Generation Stochastic Processes
Department of Mathematics, Humboldt-Universität zu Berlin, 12489, Berlin, Germany · Department of Mathematics, Technische Universität Berlin, 10623, Berlin, Germany · Bundesanstalt für Materialforschung und -prüfung, 12205, Berlin, Germany +1
Spectral Diffusion Processes
Sep 28, 2022Angus Phillips, Thomas Seror, Michael Hutchinson +3Stochastic Processes Diffusion Dynamics
Dept. of Statistics, University of Oxford, Oxford, UK. · Dept. of Computer Science ENS, CNRS, PSL University Paris, France. · Dept. of Engineering, University of Cambridge, Cambridge, UK.

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From Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps

Yichen Wang, Ziyi Wang, Wenlian Lu, Chenghuang Shen+4

Shanghai Center for Mathematical Sciences, Shanghai, China · Department of Mathematics, Fudan University, Shanghai, China · Center for Applied Mathematics, Fudan University, Shanghai, China+2

Probability Measures Stochastic Processes Distributions Neural Approximations Distributional Reinforcement Learning Nonlinearity

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Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than individual observations. We develop an approximation-theoretic framework for distribution-to-distribution learning and extend it to mappings between stochastic processes. For continuous operators on

W_2

-compact families of finite-dimensional probability laws, we establish uniform neural approximation in the 2-Wasserstein metric using finite law statistics, a simplex-valued neural map, and a shared atomic output support that guarantees valid probability measures. We further extend this principle to probability laws on separable Hilbert spaces through finite-rank orthogonal projections. These results establish the representational feasibility of learning transformations between probability laws rather than deterministic vectors or functions. To demonstrate practical relevance, we study two problems naturally defined at the distribution level: prediction of first-passage-time distributions for an Ornstein--Uhlenbeck process and nonlinear response-path laws of a Duffing oscillator. Because the theory is model-agnostic and broader than any single practical architecture, the experiments use task-adapted neural models rather than reproducing the theoretical construction exactly. In both problems, the proposed models outperform a fixed-feature MLP baseline and distribution-space kernel regression. These experiments complement the theory by demonstrating the practical learnability of distribution-to-distribution transformations in random systems.

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