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data science

3,683 papers

#data science Open access Nov 2026

Crystal Engineering—Designing Complexity

Crystal Clear: Engineering Complexity combines Gautam R. Desiraju’s scientific autobiography with an account of the emergence of modern crystal engineering. Beginning with his childhood in Madras and Bombay, his education in chemistry and doctoral training in the United States, and his deliberate return to India, Desir...

G. Desiraju · 0 citations
#data science Open access Oct 2026

AI-generated language remains systematically distinguishable from human communication.

Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior and communication, yet validation methods for these applications remain limited. Existing evaluations often rely on human judgments of plausibility, a coarse, difficult-to-scale, and often unreliable benchmark for disti...

Nicolò Pagan, Petter Törnberg, Christopher A. Bail et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols. Retraining DNN models to address these distribution shifts is often impractical due to the high cost of acquiring and annotating new medical datasets. To add...

Jimut B. Pal, Suyash P. Awate · 0 citations
#machine learning Preprint Open access Oct 2026

Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage

Hospitals, companies, and research consortia want language models that learn from, and answer questions over, data that must stay where it is held, over links with limited bandwidth, such as the satellite links of remote clinics and ships. Averaging the sites' model parameters makes each message grow with the model and...

Prasanjit Dubey, Xiaoming Huo · 0 citations
#machine learning Preprint Open access Oct 2026

When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination

Trimming suspicious calibration points is a common response to contamination in conformal prediction. Its effect on clean-target coverage, however, is governed by the retained law induced by trimming, not by the contamination level alone. We analyse fixed-threshold trimming as conditioning rather than purification. It...

Congye Wang · 0 citations
#machine learning Preprint Open access Oct 2026

MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI

Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact removal, and segmentation. However, progress has been driven largely by public datasets focused on brain and knee imaging, shaping how models are trained and evaluated. As a result, careful studies of the reliability of thes...

Paula Arguello, Berk Tinaz, Mohammad Shahab Sepehri et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Nonparametric Kernel Clustering with Bandit Feedback

Clustering with bandit feedback refers to the problem of partitioning the arms of an $N$-armed stochastic bandit according to their underlying distributions, based on noisy observations collected sequentially and adaptively. This setting has attracted growing interest for its applicability in recommendation systems, cr...

Victor Thuot (MISTEA, UMPA-ENSL), Sebastian Vogt (LRR-TUM) et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Sampling from multimodal distributions with warm starts: Non-asymptotic bounds for the Reweighted Annealed Leap-Point Sampler

Sampling from multimodal distributions is a central challenge in Bayesian inference and machine learning. In light of hardness results for sampling -- classical MCMC methods, even with tempering, can suffer from exponential mixing times -- a natural question is how to leverage additional information, such as a warm sta...

Holden Lee, Matheau Santana-Gijzen · 0 citations
#machine learning Preprint Open access Oct 2026

Control Consistency Losses for Diffusion Bridges

Simulating the conditioned dynamics of diffusion processes, given their initial and terminal states, is an important but challenging problem in the sciences. The difficulty is particularly pronounced for rare events, for which the unconditioned dynamics rarely reach the terminal state. In this work, we propose a novel...

Samuel Howard, Nikolas N\"usken, Jakiw Pidstrigach · 0 citations
#machine learning Preprint Open access Oct 2026

Complexity Dependent Error Rates for Physics-informed Statistical Learning via the Small-ball Method

Physics-informed statistical learning (PISL) integrates empirical data with physical knowledge to enhance the statistical performance of estimators. While PISL methods are widely used in practice, a comprehensive theoretical understanding of how informed regularization affects statistical properties is still missing. S...

Diego Marcondes · 0 citations

Mode Collapse of Mean-Field Variational Inference

This work provides the first theoretical explanation of mode collapse in MFVI and proposes RoVI, which augments MFVI with a rotation matrix, which augments MFVI with a rotation matrix.

Shunan Sheng, Bohan Wu, Alberto González-Sanz · 1 citation
#machine learning Preprint Open access Oct 2026

Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices

Dynamic Voltage and Frequency Scaling is essential for enhancing energy efficiency in mobile platforms. However, traditional heuristic-based governors are increasingly inadequate for managing the complexity of heterogeneous System-on-Chip designs and diverse application workloads. Although reinforcement learning approa...

Jinqi Yan, Fang He, Qianlong Sang et al. · 0 citations

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GPT-Lab Nov 3, 2026

Sovereignty Is the Option to Leave

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.

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