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 80
Data Attribution via Sketched Metadifferentiation
Data attribution seeks to quantify how individual training examples shape a model's predictions and underpins problems including data valuation, machine unlearning, and model interpretability.Structured Features Overfit Where Random Features Grok
Xu, Vardi and Safran (ICML 2026) prove that over-parameterized ridge regression over an unstructured random Gaussian feature map groks, with the delay between memorization and generalization growing…Bandits with Probing: Optimal Regret and the Limits of Winner Feedback
A learner probes at most $k$ of $n$ arms each round, receives the maximum of their rewards in $[0,1]$, and competes with the best fixed arm.Conformal Individual Treatment Effect Estimation under Networked Interference
Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treatment effects under the no-interference assumption.Approximating Smooth Functionals with ReLU Networks
We study the uniform approximation of smooth scalar-valued functionals on an infinite-dimensional separable Hilbert space by ReLU neural networks.Graph Matching Relaxations and Amortization for Supervised Graph Prediction
End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node orderings.On Detecting Multiple Simultaneous Change-points in High Dimensional Non-Stationary Time Series
This paper studies the detection of multiple simultaneous (systematic) change points for high-dimensional nonstantionary economic and financial time series data.Predictive Likelihood Ratios for Language Model Watermark Detection
Keyed watermark detection tests dependence between observed tokens and pseudorandom variables reconstructed from a secret key.Learning under Target Shift: Optimal Density Ratio Estimation and Importance-Weighted Regression
We study density ratio estimation and importance-weighted regression under target shift with continuous outputs.Sharp Rates and a One-Line Correction for Spectral Representation Learning
A self-supervised encoder is trained once, frozen, and reused through lightweight probes on tasks nobody named at training time; the practitioner's question is when the off-the-shelf features are…Quenched Ensemble Sampling
Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states changes abruptly and many sampling algorithms stall.Skeletal Prototypes on Iterative Nerve Expansions
Prototype reduction replaces a training set with a smaller representation, and the established methods return a finite set of points.Copula Adapted Directed Acyclic Graph for Cluster Representation of Biomedical Data
Diagnostic errors and mislabeling are common in biomedicine, which compromise the reliability of predictive models and data-driven outcomes.Compute-Optimal Pretrain--Fine-tune in Ridge Gradient Descent
Pretraining followed by fine-tuning introduces a compute-allocation problem: under a fixed training budget, compute spent improving the upstream objective reduces the compute available for…Mini-batch Sampling Strategies for Long-Tailed Image Classification: An Empirical Study on CIFAR-100-LT
Real-world datasets often exhibit long-tailed class distributions, where a few head classes contain a large number of training samples while a large number of tail classes have only a few.Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels
Class-balanced learning and label noise create a coupled failure mode: frequency correction prevents majority classes from dominating the decision rule, but can amplify incorrectly labeled minority…Learned Look-Ahead Splitting Rule for CART
Classification and regression trees are typically constructed using a greedy splitting rule that maximizes the immediate reduction in prediction error at each node.Stability-Constrained Approximation in Spline KANs: Exact Layer Balancing and Budget-Compatible Saturation
Deep spline superposition networks face a tension between approximation order and stability across depth.Nonparametric Variance-Penalized Actor-Critic: Statistical Inference for Risk-Sensitive Reinforcement Learning
Variance penalization is a principled approach to risk-sensitive reinforcement learning (RL) that explicitly trades expected return for policy stability.Multi-source conformal prediction: leveraging heterogeneity via localization
Many modern prediction tasks involve data from multiple heterogeneous sources, while the test distribution may differ substantially from any individual source.