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 54
Co-Supervised Tree Synthesis for Interpretable Subgroup Identification and Honest Treatment Effect Inference in Randomized Trials
Identifying patient subgroups with heterogeneous treatment effects is central to precision medicine, yet existing approaches face a tension.Evolving LLM-Generated Features for Interpretable Classification
Large language models (LLMs) are increasingly used as classifiers, yet they operate as opaque systems whose decisions are difficult to interpret, which complicates their use in regulated domains…Latent Score-Based Bayesian Cramér-Rao Bound Estimation for High-Dimensional Imaging Systems
We propose a data-driven framework for estimating the Bayesian Cramér-Rao bound (CRB) in high-dimensional imaging systems with complex, analytically intractable priors.Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation
Limited training data diversity constrains generative modeling of 3D human bodies: conservative models remain close to observed examples, whereas exploratory models often violate basic body…Slaying the Hydra: Interaction-Aware Circuit Discovery in Language Models
Localizing behavior to individual components of a language model is a central goal of mechanistic interpretability.Adaptive Partitioning Schemes for Optimistic Optimization
Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically known and whose gradients are not available.Consideration Circuits: Depth Separation and Universality Beyond a Single Softmax
Most feature-based choice models, classical and deep, score items and apply a single softmax.Anchor Divergence for Semantic Geometry in Contrastive Learning
This paper concerns how semantic context determines geometry in learned vector representations.Learning Multiple Timescales for Goal-Conditioned Reinforcement Learning
Existing approaches to offline goal-conditioned reinforcement learning (GCRL) struggle with long-horizon tasks.Block Optimism for Nonstationary Bandits with Latent Linear Dynamics
We study an endogenous nonstationary stochastic bandit problem with latent linear dynamics, where actions affect both immediate rewards and the future evolution of an unobserved latent state.Platonic Task Arithmetic
Distinct pre-trained models specialized for the same task converge to closely similar behavior, yet the parameter updates that produce it share no common coordinate system.Joint Branch-Space Transform Coding for Diffusion Activation Quantization with Classifier-Free Guidance
Post-training quantization for diffusion models increasingly exploits timestep, feature, and layer structure.The Price of Correlated Tests: How Strict Should a Model Release Gate Be?
Before a machine learning model ships, it often has to pass a suite of automated tests.Tolerance-Based Fairness Auditing: Violation Certification and Sensitivity Screening
As artificial intelligence is increasingly deployed, algorithmic unfairness has raised growing concerns and intensified demands for transparent fairness auditing.Posterior sampling by source-space MCMC via prior-based few-step transport maps
Bayesian inference increasingly uses informative but implicit priors represented only by samples, such as historical ensembles, simulator outputs, and pretrained generative models.Gradient-Guided Density Peak Clustering
Density peak clustering (DPC) connects each observation to its nearest neighbor of higher density and identifies cluster centers as high-density observations with unusually large nearest neighbor…Counterfactual Generation via Flow Matching: Coupling-Sensitive End-to-End Rates
Counterfactual generation seeks to sample outcomes under a hypothetical intervention or decision using observational data collected under the factual assignment mechanism.IQS-BO: In-Context Query Selection for Bayesian Optimisation
Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogate and maximising an acquisition function at every…Clifford Sheaf Neural Networks
We introduce the Clifford Sheaf Neural Network (CSNN), an equivariant sheaf neural network for geometric graphs that places a Clifford algebra on each stalk of a cellular sheaf and transports…Optimal Transport Meets Reinforcement Learning: A Survey
Reinforcement learning (RL) algorithms frequently compare probability distributions, such as state visitation distributions induced by policies and experts, action distributions from learned…