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 26
Gaussian Process Bandits with Paired Auxiliary Observations
Bayesian optimization (BO) is widely used for expensive black-box optimization, yet observation noise makes each evaluation less informative and slows posterior learning.Martingale Posterior Cox Processes
Cox processes provide a principled Bayesian nonparametric framework for modeling event-based data through an underlying intensity function.Tilted Mondrian: Triage and Mitigation of Fine-grained Disparities in Conformal Prediction
Fairness in conformal prediction is often pursued through Mondrian calibration, which provides finite-sample coverage guarantees separately across groups.A Statistical Viewpoint on Adam, Shampoo and Muon
We provide new statistical insights into designing stochastic optimization methods.Scalable and Adaptive Distribution Regression via Random Features
Distribution regression seeks to learn mappings from probability distributions to response variables and has emerged as a unifying framework for kernel methods, multi-instance learning, and…Unit-level aggregation breaks coverage in causal foundation models
Causal foundation models (CFMs) show promising results for treatment-effect estimation, with unit-level estimates that are competitive on standard benchmarks.Conformal Policy Selection: When to Deploy AI Decisions for Targeted Intervention
Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet deploying them responsibly requires knowing not…Towards Hyperparameter Transfer for Differentially Private Optimization
Scaling laws and hyperparameter tuning remain central challenges in training large-scale models, particularly under differential privacy (DP), where optimization is further complicated by noise…Distinguishability and Recovery for a Doubly Sparse Spiked Wigner Model
We study distinguishability and recovery of a sparse rank-one signal in a spiked Wigner model with independently sparse noise through a variety of algorithms and parameter regimes.Information-Geometric Blind Spots of Conformal Test Martingales
Conformal test martingales (CTMs) are e-processes for sequentially testing exchangeability via p-values produced by a conformity function $A$.A Class of Rényi-based Concentration Metrics for Kolmogorov-Arnold Geometry in Multilayer Perceptrons
The interpretability of artificial intelligence models has become a central concern, especially for black-box models such as multilayer perceptrons (MLPs).The Loss or the Architecture? What Matters in Survival Analysis with Competing Risks
Often, \emph{eg} in healthcare and predictive maintenance, what matters is which of several competing causes will strike first, and with what probability: survival analysis with competing risks.Minimax-optimal Scaling Laws in Ridge Regression via Transfer Learning
Pretrained models are often reused across downstream tasks, motivating the question of whether this transfer learning procedure can provide a substantial performance improvement, namely, in terms of…Towards optimal selection schedules for online active learning
We consider the problem of online linear classification, where a stream of unlabeled samples is available, and annotation is costly.Adaptive conformal prediction on the Grassmannian
We introduce a conformal prediction framework for regression with Grassmann-valued responses.Causal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs
Learning causal structure from data requires assumptions about mechanisms, noises, observation schemes, and interventions.Beyond Expected Values: Risk-Sensitive Planning with Distributional Monte-Carlo Tree Search
Monte-Carlo Tree Search (MCTS) typically stores only scalar mean estimates, which makes it poorly suited for tail-risk objectives such as conditional value-at-risk (CVaR).A general recipe for data-adaptive anytime-valid envelopes for iterative algorithms
Most convergence guarantees for iterative stochastic algorithms are stated at a fixed iteration count and do not justify stopping at a data-dependent time.Boundary Detection for High-Dimensional Point Clouds
We introduce two new methods for boundary detection in high-dimensional point cloud data, intended to work in situations with non-uniform density, high ambient dimension, and substantial noise.No-Regret Language Generation
We introduce no-regret generation, which requires a generator to make a sublinear number of mistakes uniformly over all demonstration sequences and compatible target languages.