Probability & Statistics · Mathematics & Statistics
Computation
The newest papers in Computation that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
95 papers, from 4 September 2026 to 3 November 2026: 95 arXiv preprints.
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Newest papers, page 4
Multifidelity Formulations for Triangular Transport
We develop multifidelity methods for constructing triangular transport maps from samples, when high-fidelity data are scarce but lower-fidelity data are more abundant.Rank-Aware Speculative Sampling for Diffusion Draft Trees
Speculative sampling accelerates diffusion generation by verifying inexpensive draft states in parallel while preserving the target law.Isotonic surrogate modeling for computer experiments with many input variables
Virtual simulators are widely used for studying complex physical phenomena, from particle collisions to rocket propulsion.One Proposal for Every Margin: Zero-Shot Amortized Sequential Importance Sampling for Binary Matrices
In ecology, psychometrics, and the analysis of social and financial networks, binary matrices are often analyzed conditional on their observed row and column sums, which restricts the problem to a…FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models
Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the pretrained distribution.Copula Active Subspaces I: A Score-Covariance Method for Reduced-Order Non-Gaussian Density Estimation
In Bayesian inference problems with non-Gaussian observation noise, the posterior is only as accurate as the noise density, and gradient-based samplers need that density and its gradient evaluable…Recovery-Directed Symbolic Distillation of Neural Likelihoods
Amortized neural likelihoods enable computationally expensive inference for models with analytically intractable or unspecified likelihoods, but their black-box nature limits interpretability.Sliced Orlicz-Wasserstein
We propose sliced Orlicz-Wasserstein (SOW) distance which is a generalization of sliced Wasserstein (SW) distance.MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization
We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) with non-Gaussian errors.TR-SSQP: A Trust-Region Method for Constrained Stochastic Optimization under Heavy-Tailed Noise
We consider stochastic nonlinear optimization problems with deterministic equality constraints.Ensembles of Exactly Solved Subsamples for Clusterwise Regression: Trimming Without a Trimming Level
Clusterwise least squares partitions regression data into K groups with separate linear fits.Equation discovery with Bayesian tree-adjoining grammars
Tree-Adjoining Grammars (TAGs) have recently been introduced to Nonlinear System Identification (NLSI) as a means of encoding an entire model class as a finite set of grammatical rules, from which…Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not capture the uncertainty remaining after an…Federated Targeted Maximum Likelihood Estimation
The evidence behind a scientific or operational decision is often held by hospitals, banks, or registries that cannot pool individual observations.Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler
Local geometric mixing localizes geometric mixing by requiring geometric convergence to equilibrium in total variation only over finitely many transitions.SuperPCA: subspace analysis and an efficient algorithm for high-dimensional PCA
Principal component analysis (PCA) is a fundamental tool to reduce the dimensionality of the data in many applications.Potential for Enhanced Learning in Machine Learning Classes by Using Wiki LLM Indexing
Large language models are increasingly deployed as course-specific tutors, but their usefulness depends on grounding in vetted instructional materials that are often revised mid-semester.Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experiments
Online controlled experiments, or A/B tests, are widely used to estimate causal effects on digital platforms.Causal Inference with Unobserved Confounding: A Mixture Learning Perspective
Unobserved confounding is a fundamental challenge in causal inference from observational data.Auditing Bayesian Graph Alignment: Diagnostic Comparisons and Reference Failure
Bayesian graph alignment estimates correspondence probabilities, but convergence of an alignment-score trace need not imply accurate correspondence marginals.