arXiv AI June 16, 2026 By M. Berk Sahin, Ahmet Ege Tanriverdi, Behzad Sharif, Abolfazl Hashemi

Variance Reduction for Non-Log-Concave Sampling with Applications to Inverse Problems

arXiv:2606. 16257v1 Announce Type: cross Abstract: Sampling from high-dimensional, non-log-concave distributions with unnormalized densities is a fundamental challenge in machine learning, particularly when the exact gradient of the potential is unavailable and must be approximated via stochastic gradients that exhibit high variance under a fixed budget of gradient computations per iteration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 26

Stochastic Gradient Optimization with Model-Assisted Sampling

arXiv:2606. 27171v1 Announce Type: new Abstract: This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization.

By Jonne Pohjankukka, Jukka Heikkonen

benchmarks

arXiv Machine Learning

Jul 8

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems

arXiv:2607. 06252v1 Announce Type: cross Abstract: Many problems in science and engineering are difficult to model accurately, either due to unknown physical mechanisms, poorly quantified measurement uncertainty, or prohibitive computational costs of high-fidelity simulations.

By Fabian Schneider, Tapio Helin, Leila Taghizadeh

arXiv Machine Learning

Jul 7

A Gradient Flow Perspective on Minimum MMD Estimation

arXiv:2607. 03871v1 Announce Type: new Abstract: Minimum maximum mean discrepancy (MMD) estimation has emerged as a robust and likelihood-free alternative to maximum likelihood estimation for parameter estimation.

By Sophia Seulkee Kang, Louis Sharrock, Xiaoyuan Cheng, Fran\c{c}ois-Xavier Briol, Zonghao Chen

Hugging Face Trending Papers

Aug 6

The Tamed Subgradient Unadjusted Langevin Algorithm beyond Convexity

We study the problem of sampling from target distributions whose potentials are simultaneously non-smooth, subject to superlinear gradient growth, and non-convex. We introduce the Subgradient Tamed Unadjusted Langevin Algorithm (SG-TULA), a discretisation of the Langevin diffusion that operates directly on subgradients, without relying on computationally demanding smoothing procedures.

llms diffusion

arXiv Machine Learning

Aug 7

The Tamed Subgradient Unadjusted Langevin Algorithm beyond Convexity

arXiv:2608. 06283v1 Announce Type: new Abstract: We study the problem of sampling from target distributions whose potentials are simultaneously non-smooth, subject to superlinear gradient growth, and non-convex.

By Iosif Lytras, Nikolaos Makras, Sotirios Sabanis

llms diffusion

arXiv Machine Learning

Aug 31

On the Computational and Statistical Efficiency of the Empirical Maximum Entropy on the Mean Method

arXiv:2608.27705v1 Announce Type: cross Abstract: The Maximum Entropy on the Mean (MEM) method provides a flexible computational framework for solving inverse problems by combining data fidelity with...

By Matthew King-Roskamp, Gabriel Rioux, Rustum Choksi, Tim Hoheisel

One email a morning, machine-written

Related stories

Stochastic Gradient Optimization with Model-Assisted Sampling

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems

A Gradient Flow Perspective on Minimum MMD Estimation

The Tamed Subgradient Unadjusted Langevin Algorithm beyond Convexity

The Tamed Subgradient Unadjusted Langevin Algorithm beyond Convexity

On the Computational and Statistical Efficiency of the Empirical Maximum Entropy on the Mean Method