Data Science

Data analysis, statistics, and data engineering

This Week's Best | Monthly Best | Guide | Trending

Top This Week

UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
Accelerating science with AI and simulations
Machine Learning

Accelerating science with AI and simulations

MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...

AI News - General · 10 min ·
[2512.24420] Virasoro Symmetry in Neural Network Field Theories
Machine Learning

[2512.24420] Virasoro Symmetry in Neural Network Field Theories

Abstract page for arXiv paper 2512.24420: Virasoro Symmetry in Neural Network Field Theories

arXiv - Machine Learning · 3 min ·

All Content

[2602.21478] Efficient Inference after Directionally Stable Adaptive Experiments
Machine Learning

[2602.21478] Efficient Inference after Directionally Stable Adaptive Experiments

This paper explores efficient inference methods for adaptive experiments, introducing the concept of directional stability, which enhance...

arXiv - Machine Learning · 3 min ·
[2602.21446] ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding
Machine Learning

[2602.21446] ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding

The paper presents ConformalHDC, a framework that integrates uncertainty quantification into hyperdimensional computing for improved neur...

arXiv - Machine Learning · 4 min ·
[2602.21436] Efficient Uncoupled Learning Dynamics with $\tilde{O}\!\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
Machine Learning

[2602.21436] Efficient Uncoupled Learning Dynamics with $\tilde{O}\!\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback

This paper presents an efficient uncoupled learning algorithm for bilinear saddle-point problems, achieving last-iterate convergence with...

arXiv - Machine Learning · 3 min ·
[2602.21312] Precedence-Constrained Decision Trees and Coverings
Data Science

[2602.21312] Precedence-Constrained Decision Trees and Coverings

This paper explores optimization problems related to precedence-constrained decision trees and set coverings, presenting new approximatio...

arXiv - Machine Learning · 4 min ·
[2602.21357] Conditional neural control variates for variance reduction in Bayesian inverse problems
Machine Learning

[2602.21357] Conditional neural control variates for variance reduction in Bayesian inverse problems

This paper presents a novel approach using conditional neural control variates to reduce variance in Bayesian inverse problems, enhancing...

arXiv - Machine Learning · 4 min ·
[2602.21272] Counterdiabatic Hamiltonian Monte Carlo
Ai Safety

[2602.21272] Counterdiabatic Hamiltonian Monte Carlo

The paper introduces Counterdiabatic Hamiltonian Monte Carlo (CHMC), an advanced sampling method that improves the efficiency of Hamilton...

arXiv - Machine Learning · 3 min ·
[2602.21252] INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection
Machine Learning

[2602.21252] INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection

The paper introduces INTACT, a novel framework for detecting cryptographic traffic violations by modeling violations as conditional const...

arXiv - Machine Learning · 3 min ·
[2602.22136] SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
Machine Learning

[2602.22136] SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference

The paper introduces SigmaQuant, a hardware-aware heterogeneous quantization method for deep neural networks (DNNs) aimed at optimizing p...

arXiv - Machine Learning · 3 min ·
[2602.20376] Exploiting Low-Rank Structure in Max-K-Cut Problems
Data Science

[2602.20376] Exploiting Low-Rank Structure in Max-K-Cut Problems

The paper explores the Max-3-Cut problem by leveraging low-rank structures in complex-valued quadratic forms, proposing new algorithms th...

arXiv - Machine Learning · 3 min ·
[2602.22179] Learning and Naming Subgroups with Exceptional Survival Characteristics
Machine Learning

[2602.22179] Learning and Naming Subgroups with Exceptional Survival Characteristics

The paper presents Sysurv, a novel non-parametric method for identifying subpopulations with exceptional survival characteristics, enhanc...

arXiv - Machine Learning · 3 min ·
[2602.22130] Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination
Machine Learning

[2602.22130] Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination

This paper addresses the sample complexity of robust mean estimation under mean-shift contamination, providing new algorithms and bounds ...

arXiv - Machine Learning · 4 min ·
[2602.22018] Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
Machine Learning

[2602.22018] Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data

This article presents a novel disease progression model, Mixed-SuStaIn, which integrates both discrete and continuous data types to impro...

arXiv - Machine Learning · 4 min ·
[2602.22015] Function-Space Empirical Bayes Regularisation with Student's t Priors
Machine Learning

[2602.22015] Function-Space Empirical Bayes Regularisation with Student's t Priors

This paper presents a novel function-space empirical Bayes regularisation framework using heavy-tailed Student's t priors to improve Baye...

arXiv - Machine Learning · 3 min ·
[2602.22003] Neural solver for Wasserstein Geodesics and optimal transport dynamics
Machine Learning

[2602.22003] Neural solver for Wasserstein Geodesics and optimal transport dynamics

This article presents a novel neural solver for computing Wasserstein geodesics and optimal transport dynamics, enhancing the modeling of...

arXiv - Machine Learning · 3 min ·
[2602.21965] Compact Circulant Layers with Spectral Priors
Machine Learning

[2602.21965] Compact Circulant Layers with Spectral Priors

This paper explores compact circulant layers with spectral priors, focusing on their application in memory-efficient neural networks for ...

arXiv - Machine Learning · 3 min ·
[2602.21961] Robustness in sparse artificial neural networks trained with adaptive topology
Machine Learning

[2602.21961] Robustness in sparse artificial neural networks trained with adaptive topology

This paper explores the robustness of sparse artificial neural networks with adaptive topology, demonstrating their competitive performan...

arXiv - Machine Learning · 3 min ·
[2602.21959] Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions
Machine Learning

[2602.21959] Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions

This article reviews methods for estimating and optimizing ship fuel consumption, addressing challenges and proposing future research dir...

arXiv - Machine Learning · 4 min ·
[2602.21948] Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis
Machine Learning

[2602.21948] Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis

This paper introduces GACTGAN, a Bayesian Generative Adversarial Network that utilizes Gaussian approximation for synthesizing tabular da...

arXiv - Machine Learning · 3 min ·
[2602.21919] Learning in the Null Space: Small Singular Values for Continual Learning
Machine Learning

[2602.21919] Learning in the Null Space: Small Singular Values for Continual Learning

The paper presents NESS, a novel continual learning method that leverages small singular values to maintain orthogonality in weight updat...

arXiv - Machine Learning · 4 min ·
[2602.21928] Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems
Machine Learning

[2602.21928] Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems

This paper presents a novel federated learning methodology for decentralized root cause analysis in nonlinear dynamical systems, addressi...

arXiv - Machine Learning · 4 min ·
Previous Page 50 Next

Related Topics

Machine Learning Large Language Models Generative AI Natural Language Processing Computer Vision Robotics & Embodied AI

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Subscribe to Newsletter

Daily or weekly digest • Unsubscribe anytime