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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 65
Cost-Aware Stopping for Freeze-Thaw Bayesian Optimization: When Does One-Step Lookahead Suffice?
Freeze-thaw Bayesian optimization (BO) evaluates candidates in resumable increments but usually stops at a budget or convergence test.Towards Understanding the Impact of Plasticity Loss on Reinforcement Learning in Stochastic Environments
Deep reinforcement learning (RL) is a powerful framework for sequential decision-making.Hyperbolic Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) seeks to infer the reward function underlying observed behavior, but its predictive accuracy and interpretability depend on how that reward is computed from the…Hard Constraint Projection in a Physics Informed Neural Network (Burgers)
Physics informed machine learning provides a framework to study and simulate fluid dynamics, potentially at a fraction of the computational cost of traditional numerical methods, with the goal of…Regret-Optimal Wasserstein-Robust Linear Regression
Distributionally robust optimization (DRO) minimizes worst-case risk under distributional uncertainty, but its decisions can be suboptimal in terms of their excess risk, or regret.SATFRONT: Detecting Atmospheric Fronts with Satellite Data using Machine Learning
Atmospheric fronts are associated with much of the high-impact weather in the mid-latitudes, yet their identification and classification remain largely manual and subjective.Fast Privacy Loss Accounting for Random Allocation via Multiscale FFT
In the random allocation sampling scheme each user's data is used in $k$ steps chosen randomly and uniformly from a sequence of $t$ steps.Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics
Understanding how gradient descent learns features remains a central challenge in neural network theory.Physiological Trajectories and Observation Patterns in ICU Mortality Prediction: Transportability Under Changing Measurement Conditions
To distinguish the transferable predictive value of physiological trajectories from that of observation patterns in ICU mortality prediction, and to assess whether measurement timing affects…Instance-dependent sequential limits as an exact information ledger
Instance-dependent limits for bandit regret, sequential tests, best-arm identification and Markov decision processes are proved by one chain.A Better Critic Matters for Distribution Matching Distillation
Distribution Matching Distillation (DMD) trains a few-step student using the difference between a teacher's score and an online critic's estimate of the student distribution's score.Counting on Dummies: Privacy Amplification with Observable Counts and Truncated Batches
In many practical deployments of Differentially Private Stochastic Gradient Descent (DP-SGD), the server processes data in batches.Latent Reasoning in Continuous Space for Unified Multimodal Models
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought…FlowFPR: Flow Matching Prior for Oversampled Fourier Phase Retrieval
Fourier phase retrieval reconstructs images from magnitude measurements without phase information, but noise and channel ambiguities make natural color-image recovery challenging.A Generalized Sparse Fuzzy Clustering Framework with Automated Cluster Cardinality Estimation Ability
Fuzzy clustering provides a robust framework for handling ambiguous and overlapping data boundaries, yet its traditional formulation generates entirely dense membership assignments that limit noise…Learning to bid and bundle: a posterior-sampling agent for online retail
An online retailer launching a perishable product bids for sponsored-search clicks, discounts a bundle with an always-stocked staple and displays its remaining stock, which raises willingness to pay.Interpretable structural temperature scaling for calibrating graph neural networks and point-cloud segmentation
Graph-aware calibrators make graph neural networks (GNNs) more trustworthy by predicting a temperature for each node, usually with auxiliary networks whose decisions are hard to inspect.The Prior-Force Family of Implicit Denoiser Priors and Its Decisions in Linear Inverse Problems
Implicit priors given by Gaussian denoisers successfully regularize a wide range of inverse problems in approaches such as plug-and-play (PnP), regularization by denoising (RED) and score-based…CAPER: Circuit-Aware Parameter-Efficient Reasoning in LLMs for Analog Verification
Large language models (LLMs) are increasingly used to generate analog circuits, yet every netlist must also be verified.OPINE: Orthogonal Projection in Invertible Nonlinear Embeddings for Turbulent Flows
Proper orthogonal decomposition (POD) represents physical fields with orthogonal modes ordered by variance, so that one decomposition gives reduced coordinates in every dimension, but it needs many…