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 71
Matrix Aggregation Operators
Aggregation theory has traditionally focused on operators defined over vectors.SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predictions over extended future horizons.NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees
Large language models can interpret natural lan- guage, yet robust decisions remain challenging.Global Convergence of Third-Order Langevin Dynamics for Non-Convex Optimization via Simulated Annealing
We study global convergence guarantees of third-order Langevin dynamics for non-convex optimization via simulated annealing with fixed friction and decreasing noise.RLVR landscapes for iterated multiplications can be benign: Insights from spin-glass theory
Despite the importance of reinforcement learning with verifiable rewards (RLVR), the extent to which it can learn new reasoning capabilities remains debated.Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits
We study Bayesian linear bandits with an isotropic Gaussian parameter, independent Gaussian candidate arms, and Gaussian reward noise when the horizon is proportional to the dimension.Selective Inference for Deep Clustering in Latent Spaces
Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representation prior to clustering.Adaptive Random Matrices in Gaussian Bandits: Spectral Universality and Selection-Induced Outliers
Adaptive arm selection changes the distribution of the observations collected by a bandit algorithm, but it need not change their limiting empirical spectrum.Direct Optimization of Generators for Search in Automated Theorem Proving
Fine-tuned Large Language Models (LLMs) significantly advance Automated Theorem Proving (ATP), but are often deployed as guiding policies within tree search rather than for single-attempt generation.Scalable Minimum-Volume Simplex Estimation with Non-asymptotic Analysis
We study the estimation of a $K$-dimensional simplex from $N$ i.i.d.\ points sampled uniformly from its interior; the observations are convex combinations of $K+1$ unknown prototypes.Generalized Deep Regression for Repeated Measurements
In this paper, we study the estimation of a marginal regression function from independent units with repeated binary, count, or continuous responses using ReLU deep neural networks.Efficient Cost-Aware LLM Evaluation via Bayesian Bandit Gittins Indices
Exhaustively evaluating every candidate LLM configuration on every benchmark item to identify a high-performing one is costly.On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression
Federated learning (FL) is intrinsically heterogeneous: honest clients may have different data-generating models.Optimal Tradeoffs Between Network Size and Parameter Magnitude in Neural Approximation and Minimax Regression
The statistical accuracy of neural networks depends on both their approximation power and the complexity of the class fitted from data.Beyond Class Marginals: Bounding Rehearsal Gaps without Freezing Class Co-occurrence
Class-balanced replay controls class frequency but does not determine the interval between successive replay appearances of a class.Statistical Gains from Looped Estimation under Parameter Budgets
Memory constraints in artificial intelligence motivate accurate function approximation with fewer parameters.Beyond Scalar Sensitivity: Activation-Aware Mixed-Precision LLM Quantization with Cross-Layer Refinement
Mixed-precision weight quantization is commonly formulated as a Multiple-Choice Knapsack Problem (MCKP), yet existing solvers rely on scalar sensitivity proxies that collapse each weight matrix's…Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference
Causal inference guides operational and managerial decisions but remains challenging in high-dimensional panel or tensor settings, where decisions may depend on the joint conditional distribution of…xWhyL: Causal Interactive Learning
Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for learning about causality.Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining
Causal tabular foundation models amortize effect estimation across synthetic mechanisms, but latent-effect supervision rewards posterior shrinkage rather than encoding the repeated-sample response…