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Stat.CO Papers @StatCOupdates

Apr 24

David Holzm\"uller, Francis Bach. [statML]. Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation. (Replacement). arxiv.org/abs/2303.03237

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

Sampling from Gibbs distributions and computing their log-partition function are fundamental tasks in statistics, machine learning, and statistical physics. While efficient algorithms are known...

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Stat.CO Papers @StatCOupdates

Mar 13

Louis Sharrock. [statML]. Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design. arxiv.org/abs/2603.12102

Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design

Bayesian optimal experimental design (BOED) provides a powerful, decision-theoretic framework for selecting experiments so as to maximise the expected utility of the data to be collected. In...

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Stat.CO Papers @StatCOupdates

Mar 11

Qiao Liu, Wing Hung Wong. [statML]. An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inference. (Replacement). arxiv.org/abs/2601.05355

An AI-powered Bayesian Generative Modeling Approach for Arbitrary...

Modern data analysis increasingly requires flexible conditional inference P(X_B | X_A) where (X_A, X_B) is an arbitrary partition of observed variable X. Existing approaches are either restricted...

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Stat.CO Papers @StatCOupdates

Feb 24

Louis Grenioux, Maxence Noble, Marylou Gabri\'e, Alain Oliviero Durmus. [statML]. Stochastic Localization via Iterative Posterior Sampling. (Replacement). arxiv.org/abs/2402.10758

Stochastic Localization via Iterative Posterior Sampling

Building upon score-based learning, new interest in stochastic localization techniques has recently emerged. In these models, one seeks to noise a sample from the data distribution through a...

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Oxford Statistics @OxfordStats

Feb 18

We’re recruiting for a StatML CDT Administrator to support the EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML). 📅 Closing date: 12.00 noon (UK time), 6 March 2026 ➡️ Details and application information: tinyurl.com/2zcadjrc

Job vacancy for StatML CDT Administrator managing EPSRC Centre for Doctoral Training in Statistics and Machine Learning graphic.

Closing date for applications 6 March at noon.

ALT Job vacancy for StatML CDT Administrator managing EPSRC Centre for Doctoral Training in Statistics and Machine Learning graphic. Closing date for applications 6 March at noon.

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Stat.CO Papers @StatCOupdates

Feb 18

Ayush Bharti, Charita Dellaporta, Yuga Hikida, Fran\c{c}ois-Xavier Briol. [statML]. Amortised and provably-robust simulation-based inference. (Replacement). arxiv.org/abs/2602.11325

Amortised and provably-robust simulation-based inference

Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outliers and other...

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Stat.CO Papers @StatCOupdates

Feb 17

Jared N. Lakhani, Etienne Pienaar. [statML]. Optimization and Regularization Under Arbitrary Objectives. (Replacement). arxiv.org/abs/2511.19628

Optimization and Regularization Under Arbitrary Objectives

This study investigates the limitations of applying Markov Chain Monte Carlo (MCMC) methods to arbitrary objective functions, focusing on a two-block MCMC framework which alternates between...

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Stat.CO Papers @StatCOupdates

Feb 13

Ayush Bharti, Charita Dellaporta, Yuga Hikida, Fran\c{c}ois-Xavier Briol. [statML]. Amortised and provably-robust simulation-based inference. arxiv.org/abs/2602.11325

Amortised and provably-robust simulation-based inference

Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outliers and other...

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Stat.CO Papers @StatCOupdates

Feb 5

Quanjun Lang, Xiong Wang, Fei Lu, Mauro Maggioni. [statML]. Learning Multi-type heterogeneous interacting particle systems. arxiv.org/abs/2602.03954

Learning Multi-type heterogeneous interacting particle systems

We propose a framework for the joint inference of network topology, multi-type interaction kernels, and latent type assignments in heterogeneous interacting particle systems from multi-trajectory...

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Stat.CO Papers @StatCOupdates

Feb 3

Alain Durmus, Maxence Noble, Thibaut Pellerin. [statML]. Sampling from multi-modal distributions on Riemannian manifolds with training-free stochastic interpolants. arxiv.org/abs/2602.00641

Sampling from multi-modal distributions on Riemannian manifolds...

In this paper, we propose a general methodology for sampling from un-normalized densities defined on Riemannian manifolds, with a particular focus on multi-modal targets that remain challenging...

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Stat.CO Papers @StatCOupdates

Jan 30

James Cuin, Davide Carbone, Yanbo Tang, O. Deniz Akyildiz. [statML]. Efficient Stochastic Optimisation via Sequential Monte Carlo. arxiv.org/abs/2601.22003

Efficient Stochastic Optimisation via Sequential Monte Carlo

The problem of optimising functions with intractable gradients frequently arise in machine learning and statistics, ranging from maximum marginal likelihood estimation procedures to fine-tuning of...

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Stat.CO Papers @StatCOupdates

Jan 30

James Matthew Young, Paula Cordero-Encinar, Sebastian Reich, Andrew Duncan, O. Deniz Akyildiz. [statML]. Diffusion Path Samplers via Sequential Monte Carlo. arxiv.org/abs/2601.21951

Diffusion Path Samplers via Sequential Monte Carlo

We develop diffusion-based samplers for target distributions known up to a normalising constant. To this end, we rely on the well-known diffusion path that smoothly interpolates between a simple...

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Stat.CO Papers @StatCOupdates

Jan 26

Niccolo Anceschi, Federico Ferrari, David B. Dunson, Himel Mallick. [statML]. Bayesian Joint Additive Factor Models for Multiview Learning. (Replacement). arxiv.org/abs/2406.00778

Bayesian Joint Additive Factor Models for Multiview Learning

It is increasingly common to collect data of multiple different types on the same set of samples. Our focus is on studying relationships between such multiview features and responses. A motivating...

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Stat.CO Papers @StatCOupdates

Jan 19

Hong Ye Tan, Stanley Osher, Wuchen Li. [statML]. Accelerated Regularized Wasserstein Proximal Sampling Algorithms. (Replacement). arxiv.org/abs/2601.09848

Accelerated Regularized Wasserstein Proximal Sampling Algorithms

We consider sampling from a Gibbs distribution by evolving a finite number of particles using a particular score estimator rather than Brownian motion. To accelerate the particles, we consider a...

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Stat.CO Papers @StatCOupdates

Jan 12

Qiao Liu, Wing Hung Wong. [statML]. A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference. arxiv.org/abs/2601.05355

An AI-powered Bayesian Generative Modeling Approach for Arbitrary...

Modern data analysis increasingly requires flexible conditional inference P(X_B | X_A) where (X_A, X_B) is an arbitrary partition of observed variable X. Existing approaches are either restricted...

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Stat.CO Papers @StatCOupdates

24 Dec 2025

Hedibert Lopes, Nick Polson, Vadim Sokolov. [statML]. Generative Bayesian Hyperparameter Tuning. arxiv.org/abs/2512.20051

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Stat.CO Papers @StatCOupdates

23 Dec 2025

Holden Lee, Matheau Santana-Gijzen. [statML]. Sampling from multimodal distributions with warm starts: Non-asymptotic bounds for the Reweighted Annealed Leap-Point Sampler. arxiv.org/abs/2512.17977

Sampling from multimodal distributions with warm starts:...

Sampling from multimodal distributions is a central challenge in Bayesian inference and machine learning. In light of hardness results for sampling -- classical MCMC methods, even with tempering,...

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Stat.CO Papers @StatCOupdates

18 Dec 2025

Hugo Lavenant, Giacomo Zanella. [statML]. Error Bounds and Optimal Schedules for Masked Diffusions with Factorized Approximations. (Replacement). arxiv.org/abs/2510.25544

Error Bounds and Optimal Schedules for Masked Diffusions with...

Recently proposed generative models for discrete data, such as Masked Diffusion Models (MDMs), exploit conditional independence approximations to reduce the computational cost of popular...

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Stat.CO Papers @StatCOupdates

18 Dec 2025

T\^am LeMinh, Julyan Arbel, Thomas M\"ollenhoff, Mohammad Emtiyaz Khan, Florence Forbes. [statML]. Natural Variational Annealing for Multimodal Optimization. (Replacement). arxiv.org/abs/2501.04667

Natural Variational Annealing for Multimodal Optimization

We introduce a new multimodal optimization approach called Natural Variational Annealing (NVA) that combines the strengths of three foundational concepts to simultaneously search for multiple...

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Stat.CO Papers @StatCOupdates

17 Dec 2025

\v{S}imon Kucharsk\'y, Aayush Mishra, Daniel Habermann, Stefan T. Radev, Paul-Christian B\"urkner. [statML]. Improving the Accuracy of Amortized Model Comparison with Self-Consistency. arxiv.org/abs/2512.14308

Improving the Accuracy of Amortized Model Comparison with Self-Consistency

Amortized Bayesian inference (ABI) offers fast, scalable approximations to posterior densities by training neural surrogates on data simulated from the statistical model. However, ABI methods are...

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