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 16
The Information Geometry of Representation Formation
Most analyses of neural representations focus on trained endpoints, leaving the process by which internal structure forms comparatively unmeasured.Prior-Data Fitted Networks for In-Context Off-Policy Evaluation
Off-policy evaluation (OPE) is essential for assessing decision policies before deployment when online experimentation is costly or unsafe.Minimax Regret for Sobolev Kernel-Based Reinforcement Learning
We study the minimax regret rate for episodic kernel-based reinforcement learning, where the expected reward and transition density belong to a Sobolev reproducing kernel Hilbert space, with…Synthetic Data Augmentation for Functional Data: Wasserstein Risk Bounds and Diffusion-Based Conditional Generation
Functional data are costly to collect; samples of tens to hundreds of curves are common.MetaARIMA: Configuration-Level Transfer Learning for ARIMA Models
Time Series Foundation Models have demonstrated that pretraining on diverse datasets enables effective transfer to new forecasting tasks.Conformal Prediction with Stable Measurement Mechanisms
In applications such as air-quality monitoring, inexpensive sensor readings can help predict pollutant concentrations measured by a separate reference instrument.Inference for Causal Path-Specific Effects in Longitudinal Studies with Repeated Treatments and Multiple Mediators
In longitudinal studies, repeated treatments may be followed by different sequences of mediators and time-varying covariates.Intrinsic Gradient Barriers in Pushforward Deep Generative Models
Broad classes of deep generative models (DGMs) operate by learning to map a simple source distribution, such as a standard Gaussian, to a complex high-dimensional target distribution.Certifying Distributionally Robust Decisions from Projection Observations
A scalar measurement can determine a robust decision without identifying the latent covariance or every decision risk.The Price of Scale in Proactive Memory Recall
A long-lived assistant that brings up memories without being asked must decide at every turn whether anything in its store is worth an interruption.Ambiguous Fits: The Rashomon Effect and Challenges in Scaling Law Estimation
Estimating neural scaling laws involves an end-to-end pipeline: designing a scaling recipe, training models across scales, evaluating models on a target metric, and deploying an algorithm to fit a…Joint Independence Testing for Latent Position Random Graphs
Kernel techniques are among the most powerful methods in data science and statistics.HIFI: A Highly Efficient Framework for Statistical Inference with Foundation Models
Foundation models provide powerful representations for unstructured data, such as text and images, and parameter-efficient fine-tuning (PEFT) makes their adaptation to downstream tasks increasingly…How Much Graph Is Too Much? Graph Construction and Exposure in Spatio-Temporal Forecasting
Spatio-temporal graph neural networks combine a node's history with information from graph neighbors.Valid Early Detection of Memorization in Diffusion Models
Diffusion models can sometimes reproduce examples from their training data, a phenomenon known as memorization.Data-driven Discovery of Lie Symmetries and First Integrals with Latent ODEs
We present LatentLieODE, a novel neural ODE method to discover linear Lie symmetries and first integrals of the learned dynamics from timeseries observations such as video inputs.Faithful Federated Tabular Data Synthesis
Synthetic data is an important data source in the AI era.Online Covariance Estimation for Averaged Stochastic Gradient Descent on Riemannian Manifolds
We study online inference for averaged Riemannian stochastic gradient descent (RSGD) when the unknown parameter lies on a manifold.Causal Inference in Metric Spaces via Distance Profiles
Modern interventions increasingly produce object-valued outcomes, such as distributions, covariance matrices, networks, and trees.Affine-invariant Cubic Newton with Weak Learners
Modern gradient boosting algorithms are based on Newton's method, which excels empirically but lacks global convergence guarantees.