Category
3,686 papers
We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem. Despite extensive literature on the global convergence of SK and its variants, its local linear convergence behavior remains less understood. We address this gap by providing the first nonasymptotic local analysis of SK that matches the rate o...
Post-hoc conditioning of pretrained diffusion models can be addressed using Sequential Monte Carlo (SMC) methods. By evolving an interacting particle system, SMC-guided diffusion samplers combine unconditional reverse-diffusion dynamics with sequential reweighting to approximate conditional distributions. Nevertheless,...
Question-answering services built on retrieval-augmented generation (RAG), in which a language model answers from retrieved documents, are inspected continuously and upgraded repeatedly, so their reliability guarantee must survive both. We study federated conformal RAG: nodes holding private corpora score candidate ans...
The problem of predicting unobserved entries in a binary matrix, known as 1-bit matrix completion, has found diverse applications in fields such as recommendation systems. In this study, we develop an empirical Bayes method for 1-bit matrix completion motivated by the Efron--Morris estimator, a matrix generalization of...
Reach audiences
Advertise in front of researchers, engineers, and readers.
Partial Least Squares (PLS) is a widely used method for data integration, designed to extract latent components shared across paired high-dimensional datasets. Despite decades of practical success, a precise theoretical understanding of its behavior in high-dimensional regimes remains limited. In this paper, we study a...
This work forms cryo-EM reconstruction as a stochastic inverse problem (SIP) over probability measures, where the observed images are modeled as the push-forward of an unknown distribution over molecular structures via a random forward operator.
A central question in human immunology is how a patient's T cell receptors impacts disease. Here, we introduce a method to infer the causal effects of T cell receptor (TCR) sequences on patient outcomes using observational TCR sequencing data and clinical outcomes data. Our approach corrects for unobserved confounders,...
We ask whether the standard treatment of dropout as a static hyperparameter is optimal, or whether its utility can be improved by letting it vary over depth. We answer this by developing a mean-field theory of dropout near the edge of chaos, identifying distinct universality classes for smooth and kinked activations, t...
The theoretical understanding of differentially private stochastic gradient descent (DP-SGD) with temporally correlated noise remains limited, particularly for non-convex neural network training. As a first step, we study two-layer Kolmogorov-Arnold Networks (KANs), a recently introduced architecture with learnable spl...
A continuous relaxation of the acquisition process is introduced that enables non-myopic pathwise policy gradients (NM-PPG) through the full acquisition trajectory, avoiding the high variance of standard score-function policy gradients while allowing end-to-end optimization of the acquisition policy.
Diffusion models generate samples through a sequence of learned denoising steps, and recent work has studied how semantic structure appears along this sampling process. We study this question in deterministic samplers by measuring semantic accessibility: how much information about a final semantic property, such as an...
Permuting complete weight vectors between samples quantifies the same role-state dependence while preserving their empirical distribution in Persistent Tri-State Message Passing, which combines persistent additive, subtractive, and inactive roles with weights computed from each sample's endpoint states.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
GPT-Lab
· gpt-lab.eu
Nov 3, 2026
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
