AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
The newest papers in Machine Learning (Statistics) that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 86
What Fixed-Rollout pass@k Evaluations Can Identify
Repeated-sampling evaluations increasingly extrapolate pass@k far beyond the number n of samples collected per problem.CAST: Canonical Approximate Schur Tree for Approximate Cholesky on Graphs
Graph-data workloads such as diffusion estimation, ranking, semi-supervised learning, and network optimization often solve many Laplacian or symmetric diagonally dominant M-matrix (SDDM) systems…Accountable and uncertainty-aware evaluation of sensor-based AI under distribution shift: devices, subjects, and nearly three years underground
Sensor-based AI systems are rarely operated under the conditions under which they were trained: devices, personnel and recording epochs change, and each change degrades performance in ways a random…Tensor Network Moral Graph Recovery of Discrete Probability Distributions
We present a method for recovering the moral graph of a causal DAG from a probability distribution over discrete variables, using fully connected tensor networks (FCTNs) with…Tensor-Train Weak SINDy: Identifying High-Dimensional Nonlinear Dynamics
In recent years, weak-form methods have made significant advances in data-driven discovery of dynamical systems.MiNCE: Nonparametric, Strongly Consistent Confidence Envelopes for Band-Limited Functions and their Smoothed Spectra
Minimum-norm confidence envelope strategies offer a nonparametric approach to constructing nonasymptotic, simultaneous confidence regions for band-limited functions, exploiting the theory of…Mode Coverage in Normalizing Flow Boltzmann Generators via Log-Ratio Variation
Normalizing flow Boltzmann generators retain a tractable pushforward density, but training with forward KL depends on target samples that may be biased or omit modes.Gaussian Approximation for Multivariate Martingale Sums from Uniformly Ergodic Markov Chains
We develop Gaussian approximation bounds in higher-order Wasserstein distance $W_p$, $p\geq2$, for sums of multivariate martingale differences generated by a uniformly ergodic Markov chain.Recovery Theory for Projected Power Iterations in Permutation Synchronization
We study the projected power method (PPM) for synchronizing \(n\) unknown permutations of \(m\) objects under a possibly sparse uniform corruption model.Oracle Complexity of Stochastic Fixed-Point Equations with Nonexpansive Maps
We study the oracle complexity of computing a point with small fixed-point residual $\|T(x)-x\| \leq ε$, for a general norm $\|\cdot\|$ and a self-map $T$ of a compact convex set.Black-Box Membership Inference via Word-Level Probability Estimation
Membership inference attacks (MIAs) have emerged as critical tools for auditing privacy risks in large language models (LLMs), aiming to determine whether a given text was included in a model's…Accelerated High-Accuracy Sampling from a Warm Start via the Proximal Bouncy Particle Sampler
We study the problem of sampling from $μ(\mathrm{d}x)\propto e^{-V(x)}\,\mathrm{d}x$ on $\mathbb{R}^d$, where $V$ is $α$-strongly convex and $β$-smooth, and write $κ:=β/α$.Efficient Learning and Symmetry Discovery under Exact Invariances
Learning with group invariances is central to many scientific and geometric learning problems, yet its computational foundations remain poorly understood.Adaptive Conformal Inference Under Delayed Feedback: Coverage Guarantees and a Delay-to-Memory Diagnostic
Adaptive Conformal Inference (ACI) extends conformal prediction to non-exchangeable settings by adjusting the nominal miscoverage level online in response to recent coverage errors.Dense Structural Compression of Transformers via Gauge-Correct Channel Removal
Inference energy per token drives the cost and carbon footprint of deployed transformers.Distributed Lag Neural Additive Models
We introduce Distributed Lag Neural Additive Models (DLNAMs), neural-additive analogues of Distributed Lag Non-linear Models (DLNMs) for learning nonlinear effects distributed over lags.Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts
Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challenging.SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws
We study the training dynamics of multiclass logistic regression on high-dimensional Gaussian mixture models with a large number of classes and establish precise scaling laws governing the…InfluenceField: A Differentiable Field with Interventionally Identifiable Causal Structure for Multimodal World Modeling
Multimodal large language models often capture visual-linguistic correlations but struggle to predict how local visual interventions propagate and affect downstream answers.The Role of Uncertainty in Assessing the Fairness of Machine Learning Models
Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure.