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 7
Beyond least-favorable distribution pairs: asymptotically instance-optimal robust hypothesis testing
When testing a composite null $\mathcal{P}$ against a composite alternative $\mathcal{Q}$, it has been known since Huber and Strassen (1973) that if a least favorable distribution (LFD) pair exists…Multiclass Classification with Rare Useful Features: Fundamental Limits and the Optimality of Diversity Pursuit Higher Criticism Thresholding
We consider classification in high-dimensional feature spaces where only a vanishing fraction of features are useful and the number of classes may be large.Invertible Graph Aggregation Can Increase ReLU Readout Width
An invertible graph aggregation preserves the complete feature table, but can increase the size of a classifier that reads one node at a time.Near-Quadratic Error-Free Graph Matching above Otter's Threshold via Approximate Local Tree Correlation Testing
This paper presents a near-quadratic-time algorithm that correctly matches almost all nodes of two correlated Erdős-Rényi graphs without any incorrect matches.Minimal Superior Penalties: Characterising and Calibrating Decision-Aware Proper Scoring Rules
Training a classifier means letting a validation metric decide which model is kept and when training stops.RATSIFT: Resolution-Adaptive Transformer for Solar Irradiance Forecasting and Temporal Downscaling
Accurate forecasting and temporal downscaling of solar irradiance is critical for reliable renewable energy integration and grid management.Power-Optimal Risk Control via Standardized Margin Calibration
Machine learning can screen far more candidates than can be experimentally validated, creating a need for selection procedures that identify promising candidates while controlling risk before costly…Where Causal Discovery Breaks on Real Data: Measuring the Assumptions One Property at a Time
Causal discovery is usually evaluated on simulators or on systems whose ground truth follows from physics or design.Representative Learning for Distributed Support Vector Machines
Distributed support vector machine (SVM) learning is challenging under heterogeneous local data and limited communication.The Statistical Geometry of Latent Recovery in Hidden Markov Models
Hidden Markov models (HMMs) offer an important capability to interpret fitted latent states and transitions.ALDER: A Single-Loop Augmented-Lagrangian Damped Extragradient Method for Nonconvex--Concave Minimax Problems with Nonlinear Coupled Constraints
ALDER, a single-loop augmented-Lagrangian damped extragradient method, is proposed for smooth nonconvex--concave minimax problems with nonlinear coupled inequality constraints.Estimating Average Treatment Effects with Projected Instrumental Variables
In this paper, we develop a projected instrumental variable framework that relaxes conventional assumptions for causal identification with instrumental variables.Pricing Today, Shaping Tomorrow: Contextual Dynamic Pricing in Purchase-Driven Evolving Systems
Across diverse markets, it is important for firms to balance attracting customers with managing operational conditions, such as workloads and delivery times.Model-Based Learning of Latent Euclidean Embedding from Relational Data
Visualizing and interpreting the dependence structure in relational data often benefits from geometric representations via low-dimensional Euclidean embeddings.Sampling Design and Confidence Tubes for Integral Curves on Riemannian Manifolds
We estimate the integral curve of an unknown vector field from noisy tangent vector field on a known compact Riemannian manifold.Unbiased Low-Rank Approximation for Repeated Matrix-Vector Multiplication: Stability and Optimal Sampling
Replacing the matrix in an iteration with random low-rank matrices can reduce the cost of matrix-vector multiplication.Tight Stochastic Sequential Covers for General VC Classes with Unknown i.i.d. Features
We study hybrid online learning with stochastic features and adversarial labels when the feature distribution is unknown.Bootstrap Inference for Riemannian Optimization Problems
We develop an online multiplier bootstrap for manifold-valued parameters estimated by averaging the iterates of Riemannian stochastic approximation.Riemannian Newton Methods with Statistical Inference
We develop online statistical inference for Polyak-Ruppert averages of inexact Newton iterates on Riemannian manifolds.Optimal Learning in Fairness-Aware Contextual Bandits
Mitigating unfair disparities in sequential decision-making is a growing concern in machine learning.