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 57
Awakening of the Buddha: Subspace Learning During Population-Loss Plateaus
Population loss can remain nearly constant while a neural network learns a substantially more predictive representation.Principal Component Regression Dominates all Monotone Spectral Filters for Linear Regression
We compare the instance-wise, finite-sample risks of monotone spectral filters for linear regression, a broad class of estimators including principal component regression (PCR), gradient descent…Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models
Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup that underfits heterogeneous regimes.Distributionally robust linear regression through the lens of adversarial training
Distributionally robust optimization (DRO) studies parameter estimation under uncertainty in the underlying probability distribution and has emerged as a principled framework for analyzing…CAMOS: Coupled Oscillatory State-Space Model for Multimodal Clinical Time-Series
Longitudinal clinical cohorts are multimodal, irregularly sampled and pervasively incomplete: in ADNI, positron emission tomography and cerebrospinal fluid assays are absent from roughly half of all…The Geometry of Randomized Smoothing on Feasible Sets
Randomized smoothing certifies the probability of a fixed output event as the center of Gaussian noise moves.Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervision
Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intractable statistical models and offers near…BAM! Bayesian Anything Model: a foundation model for generative computational imaging
Generative models are transforming Bayesian computational imaging, yet the field still lacks physics-aware foundation models.Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoods
We consider simulation-based Bayesian inference (SBI) for the parameters of models with intractable likelihoods but tractable forward simulation.Probabilistic Adversarial Training
Building on a probabilistic perspective in which adversarial examples arise from the overlap between a distance-based distribution $p_{\mathrm{dis}}$ and a victim-classifier-induced distribution…Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification
Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive.BayesNDE: Bayesian Generative Modeling for Neural Density Estimation
Density estimation is a fundamental problem in statistics and machine learning.Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure
We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure.Efficient Active Auditing of Multi-Group Fairness with Bias Probes
Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM) while controlling unfairness bias.Gromov-Wasserstein Distillation for Inductive Multi-View Embedding
Gromov-Wasserstein multidimensional scaling (GW-MDS) learns low-dimensional representations from relational data but remains transductive, providing no explicit mapping for unseen samples.Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding
Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured.Partial identification with entropy regularized optimal transport
In many statistical settings, the available data and maintained assumptions do not suffice to uniquely identify the model parameters of interest.Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves
Sampling several answers and keeping the one a verifier scores highest is one of the simplest ways to buy accuracy at test time.Distribution Matching Distillation for Continuous Diffusion Language Models
Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (NFEs).Signal Processing over Product DAGs: Causal Shifts and Filters
We develop a signal processing framework for signals indexed by the product of two directed acyclic graphs (DAGs) and described by a linear structural equation model (SEM).