Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2607.12140v1 Announce Type: cross Abstract: Stochastic differential equations (SDEs) are widely used to model continuous-time dynamical systems, but graphical causal models for them are not yet…
arXiv:2209.14125v3 Announce Type: replace Abstract: Diffusion models have proven to be a flexible and effective framework for modelling probability distributions on finite-dimensional spaces. However, many physical…
arXiv:2607.11997v1 Announce Type: cross Abstract: Multi-task model merging combines separately trained expert models into a single model that handles all tasks without co-training. Standard practice merges…
arXiv:2607.12830v1 Announce Type: cross Abstract: Conditional independence testing (CIT) is fundamental to modern statistical inference in areas related to causal discovery and variable selection. While marginal…
arXiv:2607.11983v1 Announce Type: cross Abstract: A specialist tolerates blind spots that a generalist does not. Usually this is treated as a cost to be minimized. We…
arXiv:2607.03999v2 Announce Type: replace-cross Abstract: Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty. Existing tree-based methods make an uneasy trade-off:…
arXiv:2607.12975v1 Announce Type: new Abstract: Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations. Many observation mechanisms, however, are many-to-one,…
arXiv:2603.27270v2 Announce Type: replace-cross Abstract: Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machine…
arXiv:2607.12095v1 Announce Type: new Abstract: Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models.…
arXiv:2607.12145v1 Announce Type: new Abstract: True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess…
arXiv:2607.12833v1 Announce Type: new Abstract: Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regression targets the conditional…
arXiv:2607.12832v1 Announce Type: new Abstract: We study the squared $2$-Wasserstein distance to the standard Gaussian as a non-Gaussianity criterion and use it for linear Independent Component…
arXiv:2607.12922v1 Announce Type: new Abstract: 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…
arXiv:2607.12902v1 Announce Type: new Abstract: We show the Randomized Hamiltonian Monte Carlo (RHMC) algorithm has accelerated mixing time guarantees for sampling from log-concave probability distributions. RHMC…
arXiv:2607.12938v1 Announce Type: cross Abstract: Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function…
arXiv:2607.12026v1 Announce Type: new Abstract: Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured,…
arXiv:2510.12311v2 Announce Type: replace Abstract: We develop interacting particle algorithms for learning latent variable models with energy-based priors. To do so, we leverage recent developments in…
arXiv:2502.15131v5 Announce Type: replace-cross Abstract: We study the fundamental problem of calibrating a linear binary classifier of the form $sigma(hat{w}^top x)$, where the feature vector $x$…
arXiv:2405.11667v2 Announce Type: replace-cross Abstract: Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this…
arXiv:2509.19242v2 Announce Type: replace-cross Abstract: We study multivariate linear regression under Gaussian covariates in two settings, where data may be erased or corrupted by an adversary…
arXiv:2607.11910v1 Announce Type: new Abstract: Optimizing composite stacking sequences to match continuous targets (e.g., Lamination or Buckling Parameters) with discrete manufacturing constraints represents a challenging combinatorial…
arXiv:2607.11913v1 Announce Type: new Abstract: Recent advancements in agentic AI have increasingly moved toward graph-based methods, driven by the demand for explainable, human-centered, and non-linear reasoning…
arXiv:2607.11914v1 Announce Type: new Abstract: A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial…
arXiv:2607.11916v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in…