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 20
A Jump-Modified Wasserstein Gradient Flow for Maximum Mean Discrepancy with Energy Kernels
We study a jump-modified Wasserstein gradient flow of the squared maximum mean discrepancy generated by the energy kernels $K(z)=-|z|^q$, $0<q<2$, in every dimension $d\ge2$ as well as the…Internal Guidance as a Sampling Method for Tilted Diffusion Models
Reward guidance methods sample an exponential tilt of a base density using a diffusion model trained with only samples from the base distribution.Curvature-Scaled Sharpness-Aware Minimization: Convergence Beyond Smoothness
Sharpness-Aware Minimization (SAM) has established itself as a widely used method for training deep neural networks.Schrödinger-Bass Bridge Barycenter Models for Measure-Valued Data
Barycenter models, based on stochastic and deterministic optimal transport bridges between probability measures, have proven to be useful for supervised and unsupervised learning for (probability)…Approximate Solomonoff Induction: On the Robustness of Learned Universal Predictors
Why large language models trained via log-loss minimization over massive and diverse datasets exhibit broad capabilities and generalizable skills is not yet fully understood.ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
Evolutionary coding agents such as AlphaEvolve are one of the notable successes in using Large Language Models (LLMs) to build AI Scientists.GaP-MoE : Explicit Expert Specialization in Mixture-of-Experts via Orthogonal Gradient Accumulation and Projection
Sparse Mixture-of-Experts (SMoE) models improve scalability by routing input tokens to only a subset of expert networks, but they often suffer from representation collapse, in which multiple experts…Cascading Bandits with Noisy Observations: Algorithms and Regret Bounds
We propose a novel stochastic cascading bandit setup where probing an ordered shortlist of $K$ out of $L$ arms incurs position-dependent costs, and the learner observes only noisy feedback regarding…The Predictive--Optimization Gap in GuanDan Self-Play
Outcome-predictive signals are often treated as promising optimization targets in reinforcement learning, but predictive validity and optimization utility are different claims.When Thoughts Become Memory: Unifying In-Context Learning and Fine-Tuning
We are the first to give a certified rule for whether unseen data belong in a pretrained model's context or its weights, built on an exact law for their information value.One Act, Many Masks: Effect-Marginal Policy Optimization for Long-Horizon Agents
Recent advances in multi-turn reinforcement learning (RL) have substantially improved the performance of LLM agents on complex, long-horizon interactive tasks.Density-Level Backward Error for Low-NFE Generative Flows
Low-NFE generative ODE sampling is constrained not only by solver order, but also by how a fixed budget of network evaluations is distributed along the flow.Generative Modeling via Nonlinear Schrödinger Bridges and Branching Diffusions
We propose a new generative modeling framework grounded in forward-backward partial differential equations (PDEs).A unified approach for estimation of unnormalized models via Stein operators
In this paper, we develop a unified framework connecting proper scoring rules and Stein's method for the estimation of unnormalized models.BRIDGE: An Acceptance Gate to Block False Improvements in Model Updates, with a Retrospective Clinical Evaluation
Models are updated repeatedly, and an update that looks better on validation data but not on new data is a false improvement that can misguide clinical decisions.Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression
Residual-based causal discovery assumes that each variable is generated from its parents and an independent noise source.Private Online Prediction from Experts with Small Losses
We consider online prediction from experts, a fundamental problem in machine learning, under differential privacy constraints.Fragment-Level Graph Encoding for Interpretable Molecular Property Prediction
Graph neural networks (GNNs) are among the standard tools for molecular property prediction, but the atom-level explanations of their predictions are hard for medicinal chemists to act on.Near Minimax Rate for Quantile Distributional Reinforcement Learning
In this paper, we study quantile distributional policy evaluation, where the return distribution under a given policy is approximated by the quantile Bellman fixed point $\bmη_m$.Lifting PCA in the Wasserstein Space
Principal Component Analysis (PCA) is a fundamental tool for statistical dimension reduction, yet no canonical extension to distribution-valued data has emerged within the framework of optimal…