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Machine Learning
The newest papers in Machine Learning that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 244
Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features
We study the last iterate of stochastic gradient descent (SGD) on streaming least squares with discretely distributed features (Slock's model), which decouples the dynamics across eigendirections.Learning Reachability-Based Safety Representations for Real-Time Robot Navigation in Unknown and Dynamic Environments
Safe robot navigation in unknown, dynamic environments requires more than geometric collision checking: a robot still far from an obstacle may be unable to avoid it because of its speed…EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents
Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not only on the agent, but also on the user simulator and environment, which jointly…Reinforcement Learning with Hawkes-Structured Networks for Market Making
Market making under realistic order-flow dynamics is challenging due to the clustered, self-exciting nature of order arrivals, which are naturally modeled by non-Markovian Hawkes processes.Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards
Reinforcement learning with verifiable rewards (RLVR) is reshaping how large language models (LLMs) acquire reasoning capabilities, yet the optimizers used remain largely inherited from…Learning, Compression, and Meta-Complexity
The idea that learning is closely tied to compression has long served as both a powerful principle and, at times, an informal analogy across machine learning and learning theory.Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization
In a variety of multi-agent and physical environments, an element's update depends strongly on a few other elements and weakly on many, and both the identity and the number of the elements that…Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation
This work presents a hybrid numerical approach for solving linear systems arising from the discretization of the two-dimensional parametric Helmholtz equation.DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields
Basic probabilistic models form the basis of modern deep learning largely by defining the final activation layer of a network and allowing the networks outputs to be interpreted through a…Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork
Sentence representation learning is still an open challenge in Natural Language Processing.AI Discovery in Combinatorics: From Problems to Solutions and Back to Bigger Problems
We introduce $qt$Bench, a benchmark of 29 open problems in algebraic combinatorics.Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements
We present a scalable method for identifying nonlinear dynamics from partial measurements, supporting physics-based, neural-network, and gray-box models.Optimal Transport PAC-Bayes Bounds
PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in terms of the Kullback-Leibler (KL) divergence…The Language of Looking: Motion Tokens Reveal Domain-Dependent Local Structure in Gaze Sequences
Human gaze is the primary high-bandwidth interface through which we parse the semantics of our environment.From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction
Relevance prediction between user queries and items is a fundamental component of e-commerce search systems.$\texttt{NFDiag}$: High-dimensional MCMC Convergence Diagnosis with Normalizing Flows
Accurately assessing the convergence of Monte Carlo Markov Chains is crucial for ensuring their validity in Bayesian inference problems.Variational Filtering for Bayesian Last Layers with an Independent Binary Probit Surrogate
Modern deep classifiers trained by gradient descent need many passes over the data to become accurate, and even then are not always well calibrated; post-hoc temperature scaling corrects calibration…LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR
Code-switching (CS) speech, characterized by language alternation within a single utterance, is prevalent in multilingual communities and poses challenges to automatic speech recognition (ASR) due…Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models
Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated samples.OctMole: Multiscale 3D Molecular Generation with Hierarchical Spatial Flows
Generating accurate 3D molecules requires jointly determining a variable number of atoms, their identities, and geometry across spatial scales: global shape emerges coarsely, whereas chemical bonds…