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 63
Universality and Generalization of Causal Transformers Across Context Lengths
Long contexts are central to modern transformer systems, but most expressivity results choose a different network for each fixed sequence length.Propagate, Then Sharpen: Post-Hoc Refinement of Frozen Node Classifiers
We study post-hoc refinement of frozen node classifiers: given only the graph $G$ and class distributions $Q$ predicted by a frozen model, can we improve accuracy without access to node features…A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty
Modern probabilistic machine learning models increasingly produce multivariate outputs with complex dependence structure, from multi-step time-series forecasts to sample path predictions.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.Convex Optimization Is Free When Accuracy Is Expensive
This paper studies convex optimization when the gradient cannot be evaluated exactly, but only approximated by a hierarchy of algorithms whose compute grows like $δ^{-γ}$ in the accuracy $δ$.Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantification
Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven applications, including uncertainty…Universal Approximation of Measure-to-Measure Operators by Pushforwards
Many learning tasks map an input distribution to an output distribution.Simplex Diffusion Models
Diffusion models have revolutionized generative modeling for continuous data through the gradual refinement of a belief state.Learning Conditional Expectation Operators via Functional Newton Updates
We introduce the Functional Spectral-Newton Method (FSNM) for learning the leading singular structure of a conditional expectation operator without fixing a basis or reproducing kernel Hilbert space.Control-Geometry Straightening for Sampling-Based Latent Planning
Joint-embedding predictive architectures enable planning with latent world models, but accurate transition prediction alone does not ensure that the planning objective is easy to optimize.Elicitation and Decision Geometry in Single-Index Bandits
We study two-arm contextual bandits with arm-specific single indices and a shared unknown monotone link.Arbitrary-Accuracy Neural Approximation with Optimal Neuron Count and Near-Optimal Bit Complexity
We study the minimum number of hidden neurons required for arbitrary-accuracy approximation of multivariate Hölder-continuous functions on $[0,1]^d$ and the associated encoding complexity.Guided Uncertainty-Aware Robust Domain Transfer
Artificial intelligence models deployed as clinical decision support tools often suffer substantial performance degradation over time and across institutions due to covariate shift, concept drift…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.Intrinsic Associative Memory on Riemannian Manifolds: Curvature, Capacity, and Emergent Modes
Geometry does more than constrain an associative memory: curvature determines what it remembers and which states it creates.Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation
In spatial observational studies, treatment assignment and outcomes often exhibit spatial dependence patterns, and treatment effects may vary across space and subpopulations due to both measured and…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…One-Step Next-Latent Prediction Is Not a World Model
Next-latent prediction fits a map from the current embedding to the next one.Can Representation Learning Decouple from Loss Minimization? Polar Updates Have an Answer
Does representation learning stop when the training loss stops improving?The Signed Geometry of One-Shot Recourse: On-Path Validity and the Signed-Curvature Criterion
Closed-form recourse moves a rejected user along the unit gradient $\hat g$ of the classifier score $f$ by the promised distance $d_p=|f(x)|/\|\nabla f(x)\|$, at which the linearized score reaches…