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 61
When do data mixtures improve scaling laws? Insights from high-dimensional regression
Modern machine learning systems are trained on mixtures of data from different domains, and choosing the right mixture can substantially improve downstream performance.Latent Inference-Time Guidance of Time Series Foundation Models
Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks.Traversing the solution space of neural networks with Hessian Null Space Continuation
On a single task, deep networks can learn many solutions, depending on their optimizer, training data, architecture, and hyperparameters.ReCIRC: Rectified Conformal Risk Control
Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or missed labels in multilabel classification.Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions
Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data.Acceleration of Diffusion Language Model through Discrete Average Generator
Discrete diffusion models and flow matching have emerged as powerful frameworks for generative modeling over discrete state spaces, yet efficient few-step generation remains a fundamental challenge.Lower Bounds for Linear-Oracle Online Learning
Can a constant number of linear minimizations per round improve on the $T^{3/4}$ regret rate of online Frank-Wolfe on general convex sets?Learning to Plan from Random Exploration
Random exploration reveals how an environment can be traversed before a goal is specified.PTED: A multi-dimensional two-sample test for scientific inference and generative machine learning
Two-sample tests are widely applicable in inference and generative modelling, yet users frequently fall back on heuristics and visual inspection due to lack of an accessible test that operates in…Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries
Whether quantum computation can reduce the amount of data sampled from an unknown probability distribution required to learn a prediction rule is a fundamental question in quantum machine learning.Local polynomial density ratio estimation
We propose a novel local-polynomial estimator of the ratio $r=f/g$ of two $d$-dimensional densities $f$ and $g$, from which independent samples are available.Grokking through the Lens of Minimum-Norm Interpolation
Grokking shows that fitting the training data and learning the underlying signal can occur at very different stages.ShamAN-Q: Shampoo Augmented NanoQuant for Sub-1-bit LLM Weights
We introduce ShamAN-Q, a sub-1-bit post-training quantization method that extends NanoQuant by replacing each its diagonal reconstruction geometry with a tractable dense curvature metric, using a…Generative sequence modeling for infinite memory processes via predictive states
We consider estimating the one-step-ahead conditional distribution of a multivariate stochastic process.Towards Universal Wasserstein Barycenters through Flow Matching
Defining a weighted mean over probability measures under probability metrics is a central tool in probabilistic machine learning.Learning-Enabled Estimation: Tight Characterizations under Sample Selection Biases
When can we learn from biased samples?Adaptive mixture variational inference for spike-and-slab regression
Correlated predictors can support competing sparse explanations with similar predictions, making joint uncertainty about variable inclusion difficult to capture with mean-field approximations.Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity
We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multiple correct answers.Understanding Off- vs On-Policy Distillation: A Tale of Distinct Training Objectives
On-policy distillation (OPD) learns from teacher feedback on student-generated responses and has shown promise in reducing forgetting relative to supervised fine-tuning (SFT).Singularities of Non-negative Matrix Factorization and their application to Bayesian inference
Non-negative matrix factorization (NMF) is a singular statistical model whose Bayesian asymptotics are governed by the real log canonical threshold (RLCT).