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 82
Exact Community Recovery in Bipartite Networks
Community detection in bipartite networks is a fundamental problem in modern data analysis, with applications in recommendation systems, biological networks, and social network analysis.Linear Exponential Quadratic Gaussian Covariance Steering
We formulate and analyze the linear exponential quadratic Gaussian (LEQG) covariance steering problem in continuous time over a given deadline (finite time horizon).A Splitting Method for SDE Terminal-Law Estimation
In many settings involving stochastic differential equations, including in diffusion based generative AI, our aim is to accurately generate samples from a terminal distribution.Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and Hölder Smoothness
Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both frequently violated in practice.Transfer Learning for Evolving Domains
Transfer learning explores how to leverage knowledge from various tasks or domains (sources) to enhance predictive performance in related tasks or domains (targets).Benign Loss Landscapes Can Coexist with Worst-Case Hardness
Deep neural networks are expressive enough to contain worst-case targets that can be evaluated in polynomial time but cannot be learned in polynomial time by gradient descent.A Ranking Approach for Measuring Calibration
When providing forecasted probabilities with a predictive model, the ideal model offers perfect calibration: the true probability of the outcome (i.e., the probability that $Y=1$) exactly matches…Stochastic Gradient Descent over P2
Stochastic gradient descent (SGD) admits diffusion approximations that replace the complicated randomness of stochastic gradients by Gaussian noise, providing a powerful tool for understanding its…Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data
Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across temporal and spatiotemporal forecasting…Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation
Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives.Certifiably Interpretable Training of ReLU-MLPs for Boolean Tasks with Guaranteed Truth-Table Generalization
As compute scales, models evolve, and training algorithms advance, our ability to explain the increasingly powerful AI systems they enable is eroding.Toward Optimal Switching Regret for Multi-Armed Bandits with Oblivious Adversary
We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most $S$ times.A pullback-corrected scalar auxiliary variable optimizer with momentum and adaptive mobility
Objectives in scientific machine learning are often prescribed as a sum of several terms, such as the residual, boundary, initial, and data losses of a physics-informed neural network.Synthetic Nearest Neighbors: Extending Synthetic Controls for Matrix Completion with Missing Not at Random Data
We develop a causal framework for matrix completion under missing not at random (MNAR) data.Assumption-Lean Inference for Spectral Differential Network Analysis of High-Dimensional Time Series
Network analysis for multivariate time series is popular in many fields, from neuroscience to seismology.AUC Maximization from Biased Positive-unlabeled Data with Confidence
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced binary classification.Phases in a class of associative memories via hidden neurons
Associative memory in the Hopfield network is attractor dynamics in a disordered many-body system, and higher-order and exponential extensions turn its retrieval update into softmax attention.Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
Unlabeled-unlabeled (UU) learning allows us to learn a binary classifier from two sets of unlabeled data with different class-priors.Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set
Data-driven distributionally robust optimization (DRO) typically treats the conditional outcome law as fixed and uses ambiguity sets to capture estimation error.How Wrong Can a Good Predictor Be? Diverging Updates with Vanishing Predictive KL
Accurate posterior prediction need not require accurate approximation of Bayesian updates.