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 23
Do Neural Models Prefer Their Submodels?
A neural model is the set of functions that can be expressed by a given neural network architecture.Binary Discrete Diffusion: Candidate-Set Diffusion for Logic and Planning Tasks
Discrete diffusion models iteratively denoise a noisy sequence of categorical data such as text, enabling any-order generation with self-correction and in-filling capabilities.Borrowing Strength Across Stores: Transfer Learning for Choice-Based Revenue Management
Large retail chains must tailor assortment and pricing decisions to local customer preferences, yet even retailers with extensive sales records often have limited data for estimating demand at the…A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners
Gold's theorem shows that no learner can identify an arbitrary language in a superfinite class from positive presentations alone.ReAPO: Retry-Aware Policy Optimization for Efficient Exploration in LLM Reasoning
RLVR improves language-model reasoning, but commonly relies on independent stochastic rollouts for exploration, which can repeatedly sample similar failed strategies.Depth Separation for Neural Networks under Gaussian Measure
We revisit the problem of depth separation for neural networks, as studied by Eldan and Shamir, Daniely, and others.Actually, All Learnable Distribution Classes Are Privately Learnable
We show that every class of distributions that is learnable up to arbitrarily small target error in total variation distance with finitely many samples is also learnable under ($\varepsilon…Backtracking Direct, Indirect and Total Effects
Natural direct and indirect effects are defined through cross-world interventional counterfactuals.Auditing Conformal Prediction for Language Models Under Inference-Time Model Shift
Conformal prediction (CP) for language models is typically evaluated under a matched protocol, in which calibration and deployment use the same post-training inference configuration, such as…Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study
In-context reinforcement learning (ICRL) exploits the impressive in-context learning ability of large language models to zero-shot generalize to new, unseen tasks at inference time without any…From Common Information to Canonical Correlation Analysis and Beyond: Bridging Information Theory, Signal Processing, and Machine Learning
Many problems in signal processing, machine learning, and neuroimaging provide several jointly observed measurements of one process, each mixing a component common to all of them with a component…Corrected Block Sparse Spectral Optimization: Exact Models, Finite Identification, and Local Dynamics
The block generalized-power method pairs threshold-based support selection with inexpensive Stiefel updates, but the standard block formulation conflates two nonequivalent objectives and leaves…Detection Augmented Learning In Non-Stationary Bandit Environments
We study the problem of non-stationary bandits without knowledge of the environmental changes.Fair Regression under Approximate Demographic Parity
Exact demographic parity requires the same prediction distribution across sensitive groups.Statistical Learning Theory for Direct Preference Optimization
Direct Preference Optimization (DPO) learns from pairwise preferences through a logistic objective whose score-difference parameterization follows the Bradley--Terry model.Keep the Lineages Alive: Diversity-Preserving SMC for Diffusion LLM Decoding
Inference-time sequential Monte Carlo (SMC) decoding can improve the reasoning accuracy of masked diffusion language models (diffusion LLMs) without post-training by resampling a population of…Federated Community Detection in Bipartite Networks
We study federated spectral estimation when a large matrix is horizontally partitioned across servers that cannot share raw data.Mediation and moderation in distance-based regression with triangular fuzzy data: simulation evidence and an application to academic performance
We study mediation and moderation in the distance-based regression model for triangular fuzzy responses, in which the center and transformed spreads of the response are linear in the components of…Towards instance-dependent regret optimality in Episodic MDPs with Posterior Sampling
We study regret minimization in finite-horizon episodic Markov Decision Processes (MDPs).Truncate or Not? A Scaling Study of Looped Transformers with Truncated Backpropagation
Looped transformers reuse a shared block of layers to increase computational depth without increasing the number of trainable parameters.