stat.ML · 2026-07-15 · No. 54

Machine Learning, 2026-07-15.

7 new papers in stat.ML. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

7 entries
  1. 01

    Ensemble Controlled-Flow Filtering for Implicit Data Assimilation

    Zhuoyuan Li, Yue Zhao, Ming Li

    stat.ML · cs.LG · math.NA · math.OC

    Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations. Many observation mechanisms, however, are many-to-one, implicit, non-smooth, or accessible only through simulation, and need not provide the residual structures or likelihood guidance required by existing ensemble filters. We introduce implicit data assimilation, in which the analysis law is defined as an energy tilt of the forecast...

    arxiv.org/abs/2607.12975 · PDF

  2. 02

    LatentFlow: A General Framework for Conditioning Stochastic Processes

    Louis Sharrock, Lachlan Astfalck, Henry Moss

    stat.ML · cs.LG · stat.ME

    Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box information, and global constraints all induce intractable conditional laws, requiring bespoke, model-specific constructions. We introduce LatentFlow, a single framework for conditioning stochastic processes, with no learned neural approximations and no training. Our starting point is to write the...

    arxiv.org/abs/2607.12922 · PDF

  3. 03

    Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo

    Siddharth Mitra, Vishwak Srinivasan, Xiuyuan Wang, Andre Wibisono

    stat.ML · cs.DS · cs.LG · math.PR · math.ST · stat.CO

    We show the Randomized Hamiltonian Monte Carlo (RHMC) algorithm has accelerated mixing time guarantees for sampling from log-concave probability distributions. RHMC proceeds by repeatedly simulating the continuous-time Hamiltonian dynamics for some random integration times, and resetting the velocity to be an independent Gaussian random variable between each simulation. We show that when the target distribution is log-concave and satisfies an...

    arxiv.org/abs/2607.12902 · PDF

  4. 04

    ANGLE: Angular Neural Generative Learning via Engression

    Rajdeep Pathak, Archi Roy, Tanujit Chakraborty

    stat.ML · cs.LG · math.ST

    Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regression targets the conditional mean, which is often geometrically misleading for circular responses under multimodal, skewed, or asymmetric data structures. To address these limitations, a lightweight deep generative framework, namely ANGLE, is introduced for non-parametric distributional...

    arxiv.org/abs/2607.12833 · PDF

  5. 05

    Thompson Sampling Is 2-Competitive for Mistakes

    Mark Sellke, Gregory Valiant

    stat.ML · cs.LG

    We consider Bayesian bandit models and prove that Thompson sampling makes at most twice the expected number of mistakes (selections of a suboptimal arm) as any other policy. Our analysis applies as long as the latent arm processes are independent and each arm evolves only when played. For stochastic bandits with best arm defined via mean reward, this confirms a conjecture of Guha and Munagala from 2014, where the factor $2$ is already best...

    arxiv.org/abs/2607.12389 · PDF

  6. 06

    Falsifying Causal Graphs With Outlier Events

    William Roy Orchard, Philipp M. Faller, Dominik Janzing

    stat.ML · cs.LG

    True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess whether a given causal graph is good in the absence of a ground truth. We propose falsifying candidate causal graphs based on whether they can explain the propagation of an outlier event. Our approach leverages a key principle: weak outliers rarely cause strong ones. While this principle has previously been used...

    arxiv.org/abs/2607.12145 · PDF

  7. 07

    Dynamic Online Processor-Native Inference for State Estimation

    Orestis Kaparounakis

    stat.ML · cs.LG · eess.SP

    Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models. Yet the computation of the likelihood remains an essential bottleneck for accurate posteriors and performant inference. This paper presents a Bayesian filtering technique that uses processor-native uncertainty tracking for both uncertainty propagation and inference. The...

    arxiv.org/abs/2607.12095 · PDF

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