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arXiv stat.ML Papers

Score Accuracy Along the Forward Diffusion Does Not Certify Numerical Stability in Diffusion Sampling

arXiv:2607.08757v1 Announce Type: new Abstract: Score matching controls average error under the forward marginals, but a discretized reverse-time sampler evaluates the learned score along its own…

arXiv stat.ML Papers

Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation

arXiv:2607.07767v1 Announce Type: new Abstract: Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that…

arXiv stat.ML Papers

Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables

arXiv:2604.11305v3 Announce Type: replace-cross Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality…

arXiv stat.ML Papers

A Stochastic–Geometric Theory of Scaling Laws in Grokking

arXiv:2606.30388v3 Announce Type: replace Abstract: Delayed generalization (ie~grokking) refers to the phenomenon in which a neural network fits its training data early in training but only…

arXiv stat.ML Papers

Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings

arXiv:2508.11847v4 Announce Type: replace Abstract: We propose a method for evaluating the robustness of widely used LLM ranking systems -- variants of a Bradley--Terry model --…

arXiv stat.ML Papers

Rotated Mean-Field Variational Inference and Iterative Gaussianization

arXiv:2510.07732v2 Announce Type: replace-cross Abstract: We propose an iterative Gaussianization method for sampling from unnormalized densities by repeatedly applying mean-field variational inference (MFVI) in rotated coordinate…

arXiv stat.ML Papers

Differentiability and Approximation of Probability Functions under Gaussian Mixture Models

arXiv:2411.02721v2 Announce Type: replace-cross Abstract: In this work, we study probability functions associated with Gaussian mixture models. Our primary focus is on extending the use of…

arXiv stat.ML Papers

Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery

arXiv:2511.18940v3 Announce Type: replace-cross Abstract: Cross-subject motor imagery decoding remains a fundamental challenge in EEG-based brain-computer interfaces due to substantial inter-subject variability. Recent approaches have leveraged…

arXiv cs.NE Papers

Sampling on Random Subspaces under Limited Data in the Context of Exploratory Landscape Analysis

arXiv:2607.07854v1 Announce Type: new Abstract: Classical space-filling designs often fail to provide reliable statistical results for Exploratory Landscape Analysis (ELA) when only limited evaluation budgets are…

arXiv cs.NE Papers

Mass Conservation as an Inductive Bias for Self-Organized Criticality in NCA Reservoirs

arXiv:2606.23115v2 Announce Type: replace Abstract: Self-organized criticality (SOC), a dynamical regime associated with maximal information processing, offers a promising foundation for reservoir computing. Recent work has…

arXiv cs.NE Papers

($theta_l, theta_u$)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces

arXiv:2503.08394v5 Announce Type: replace Abstract: Multi-task optimization is typically characterized by a fixed and finite set of tasks. The present paper relaxes this condition by considering…

arXiv cs.LG Papers

The Importance of Encoder Choice:A Tabular-Image Study

arXiv:2607.07756v1 Announce Type: new Abstract: Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using…

arXiv cs.LG Papers

Does Dimensionality Reduction via Random Projections Preserve Landscape Features?

arXiv:2604.13230v2 Announce Type: replace Abstract: Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsity effects,…

arXiv cs.LG Papers

Image classification via a quantum-inspired strategy involving a mixture of experts

arXiv:2607.07754v1 Announce Type: new Abstract: Pattern recognition problems arise in a variety of physical image processing situations, and convolutional neural networks are a popular scheme for…

arXiv cs.LG Papers

Towards Isolated Interventions via Almost Orthogonal Features in Language Models

arXiv:2602.04718v2 Announce Type: replace Abstract: A central premise in mechanistic interpretability is that meaningful concepts in language models are represented by linear features in activation space.…

arXiv cs.LG Papers

A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents

arXiv:2607.07753v1 Announce Type: new Abstract: Modelling psychological disorders in artificial agents offers both a testbed for computational psychiatry and a lens on the failure modes of…

arXiv cs.LG Papers

How Causal Abstraction Underpins Computational Explanation

arXiv:2508.11214v2 Announce Type: replace Abstract: Explanations of cognitive behavior often appeal to computations over representations. What does it take for a system to implement a given…

arXiv cs.LG Papers

Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation

arXiv:2607.07748v1 Announce Type: new Abstract: Large Language Models achieve strong code generation for high resource languages like Python and Java but suffer sharp performance drops on…

arXiv cs.LG Papers

Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing

arXiv:2607.08497v1 Announce Type: cross Abstract: Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing. However, they repeatedly feed all…

arXiv cs.LG Papers

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks

arXiv:2607.07745v1 Announce Type: new Abstract: While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy…

arXiv cs.LG Papers

Benchmark Evaluation of Feredated Learning on Multi-organ Images

arXiv:2607.08219v1 Announce Type: cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.…

arXiv cs.LG Papers

Architecture Generalization with MetaNCA

arXiv:2607.07743v1 Announce Type: new Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information. Biological neurons,…

arXiv cs.LG Papers

Governing Generative AI Across Financial Institutions: An SR 26-2-Compatible Framework for Generative AI Risk Control

arXiv:2607.04103v2 Announce Type: replace-cross Abstract: The release of SR 26-2 marks a significant modernization of U.S. model risk management by replacing SR 11-7 with a more…

arXiv cs.LG Papers

LLT: Local Linear Transformer for PDE Operator Learning

arXiv:2607.07718v1 Announce Type: new Abstract: Neural operators have become a common approach for learning PDE solution maps and accelerating numerical simulations. Transformer-based neural operators are of…