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
Post-stratified importance sampling
Importance sampling is a building block of modern statistical computing, but the weighted estimate is prone to high or infinite variance.Shrinkage prior for implicit generative models
Implicit generative models based on deep neural networks have revolutionized many scientific fields, from weather research to language generation.Transport along Local Annealing Paths: Wasserstein Geometry and Multimodal Sampling
Variational approaches to learning transport maps for multimodal sampling can suffer from mode collapse.SynForecast: Controlled Synthetic Time Series for Forecasting Research
Synthetic time series constitute a substantial share of the training corpora of several recent forecasting foundation models.Sketching in Regression: Sketchy in Practice?
The Johnson-Lindenstrauss (JL) lemma and its extensions provide tools for solving the $L_2$ regression problem in high-dimensional settings with $n>d$, by transforming a subspace of $\mathbb{R}^{n}$…Bagel: A Fast Bayesian Online Changepoint Detection Algorithm for Linear Models via Posterior Merging
Bayesian online changepoint detection provides a natural framework for quantifying uncertainty about whether and when a change has occurred.Poisson Super Learning For Survival And Competing Risks On A Discretized Time Scale
We introduce the Poisson Super Learner, a scalable ensemble method for survival and competing risks prediction with right-censored data.Hypergraph cycle enumeration for efficient inference of planted k-XOR with smooth trade-off
We introduce hypergraph cycle enumeration (HCE), a simple spectral framework for detecting and recovering planted signals in noisy hypergraph inference problems.Quantum amplification of improbable futures
Some consequential futures consist of entirely plausible steps arranged into an unlikely sequence, such as ordinary weather anomalies combining into an extreme event or local equipment failures…Metropolized backward sampling for conditional sequential Monte Carlo
Particle Gibbs samplers with backward sampling are widely used for sampling the smoothing distribution of state-space models, but, with few particles, the new trajectory often coincides with the…Modular Bayesian Foundation Models: Bayesian time series encoders for OOD detection, routing and forecasting
Industrial systems continuously generate time series that vary in sampling frequency, scale, dynamics, and operating regime.Intrinsic Information Rates of Summaries in Simulation-Based Inference
Simulation-based inference often encodes observations $Y$ into a fixed-dimensional summary $S$, with the posterior conditioning only on $S$.A knowledge-driven nonconvex square-root regularization framework for joint group sparsity and structured smoothness
Structured sparse learning is a leading paradigm for high-dimensional data modeling, as it enables rigorous integration of domain-validated structural priors into the learning process.KAPPA: Checking the Proofs in Statistics Papers with Lean 4 and Language Model Agents
Papers in statistical theory rest on long proofs that few readers check line by line.Acquisition for Spherical-Linear Bayesian Optimization on the Cube
Recent work demonstrated that executing Bayesian Optimization (BO) using simply a linear surrogate with a clever transformation is enough to approximate the performance of the standard…Sparse Bayesian Neural Networks via Sticky Piecewise Deterministic Markov Processes
There is growing evidence that the performance of a neural network depends largely on a small subset of its weights, which are typically identified by training a dense network and pruning it…Multichoice Modeling: Exploring Decision Configuration Space under Uncertainty
Decision support has become sophisticated at exploring uncertainty, but much less so at representing the architecture of the decision itself.Prediction Uncertainty Controlled Computer Model Calibration
Computer-model calibration is usually undertaken in service of prediction, yet neither Bayesian Kennedy-O'Hagan (KOH) calibration nor amortized simulation-based inference is built to control the…Dimension-free regimes for unadjusted Langevin in ridge-separable models: A subspace robust Wasserstein analysis
The Unadjusted Langevin Algorithm (ULA) is widely used for high-dimensional Bayesian inference.Asynchronous, Generation-Free ABC: Streaming AMIS Reweighting for Heterogeneous Simulator Workloads on HPC
Approximate Bayesian computation (ABC) enables Bayesian inference for models with intractable likelihoods by estimating parameter posterior distributions from simulated data.