Data Science

Data analysis, statistics, and data engineering

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

Top This Week

Machine Learning

[D] Offering licensed Indian language speech datasets (with explicit contributor consent)

Hi everyone, I run a small data initiative where we collect speech datasets in multiple Indian languages directly from contributors who p...

Reddit - Machine Learning · 1 min ·
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 ·
Top 10 AI certifications and courses for 2026
Ai Startups

Top 10 AI certifications and courses for 2026

This article reviews the top 10 AI certifications and courses for 2026, highlighting their significance in a rapidly evolving field and t...

AI Events · 15 min ·

All Content

[2505.11111] FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
Machine Learning

[2505.11111] FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation

FairSHAP introduces a novel preprocessing framework that utilizes Shapley value attribution to enhance fairness in machine learning model...

arXiv - AI · 4 min ·
[2411.17411] Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond
Machine Learning

[2411.17411] Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond

This paper explores advancements in uncertain combinatorics through the integration of graphization, hyperization, and uncertainization, ...

arXiv - Machine Learning · 4 min ·
[2407.01875] Spatio-Temporal Graphical Counterfactuals: An Overview
Machine Learning

[2407.01875] Spatio-Temporal Graphical Counterfactuals: An Overview

This article provides an overview of spatio-temporal graphical counterfactuals, discussing various models and proposing a unified framewo...

arXiv - AI · 3 min ·
[2505.08783] CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
Llms

[2505.08783] CodePDE: An Inference Framework for LLM-driven PDE Solver Generation

The article presents CodePDE, an innovative framework leveraging large language models (LLMs) for generating solvers for partial differen...

arXiv - AI · 4 min ·
[2505.05295] Performance Estimation in Binary Classification Using Calibrated Confidence
Machine Learning

[2505.05295] Performance Estimation in Binary Classification Using Calibrated Confidence

This article presents a novel method, CBPE, for estimating binary classification metrics without requiring ground truth labels, enhancing...

arXiv - Machine Learning · 4 min ·
[2505.02515] FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization
Nlp

[2505.02515] FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization

The paper presents FedSDAF, a novel framework that enhances Federated Domain Generalization by leveraging source domain awareness, demons...

arXiv - Machine Learning · 4 min ·
[2504.19199] Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration
Machine Learning

[2504.19199] Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration

The paper presents HetGL2R, a novel framework for ranking critical road segments by integrating heterogeneous graphs and origin-destinati...

arXiv - Machine Learning · 4 min ·
[2504.15077] Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL
Llms

[2504.15077] Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL

The paper 'Think2SQL' explores enhancing reasoning capabilities in Text-to-SQL tasks using Reinforcement Learning with Verifiable Rewards...

arXiv - Machine Learning · 4 min ·
[2504.02996] Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization
Machine Learning

[2504.02996] Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization

This article presents a novel approach to Noise-Aware Generalization (NAG) in machine learning, addressing the challenges posed by label ...

arXiv - Machine Learning · 4 min ·
[2504.10917] Towards A Universal Graph Structural Encoder
Machine Learning

[2504.10917] Towards A Universal Graph Structural Encoder

The paper presents GFSE, a universal graph structural encoder designed to capture transferable structural patterns across various graph d...

arXiv - AI · 4 min ·
[2602.20135] KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration
Llms

[2602.20135] KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

The paper introduces KNIGHT, a framework for generating multiple-choice questions using knowledge graphs, enhancing efficiency and adapta...

arXiv - AI · 4 min ·
[2602.20134] Modeling Epidemiological Dynamics Under Adversarial Data and User Deception
Machine Learning

[2602.20134] Modeling Epidemiological Dynamics Under Adversarial Data and User Deception

This paper presents a game-theoretic model to analyze how adversarial data and user deception affect epidemiological dynamics, particular...

arXiv - AI · 4 min ·
[2602.20130] To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering
Llms

[2602.20130] To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

The paper presents Selective Chain-of-Thought (Selective CoT), a method to enhance medical question answering efficiency using large lang...

arXiv - AI · 4 min ·
[2502.09257] From Contextual Combinatorial Semi-Bandits to Bandit List Classification: Improved Sample Complexity with Sparse Rewards
Machine Learning

[2502.09257] From Contextual Combinatorial Semi-Bandits to Bandit List Classification: Improved Sample Complexity with Sparse Rewards

This paper explores contextual combinatorial semi-bandits, presenting an algorithm that improves sample complexity in sparse reward scena...

arXiv - AI · 4 min ·
[2501.15461] Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
Machine Learning

[2501.15461] Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space

The paper introduces MbaGCN, a novel graph convolutional network architecture designed to address the over-smoothing problem in deep GNNs...

arXiv - Machine Learning · 4 min ·
[2602.20100] Transcending the Annotation Bottleneck: AI-Powered Discovery in Biology and Medicine
Data Science

[2602.20100] Transcending the Annotation Bottleneck: AI-Powered Discovery in Biology and Medicine

This article discusses the shift from expert annotation to AI-driven unsupervised learning in biomedicine, highlighting its potential to ...

arXiv - AI · 3 min ·
[2501.10471] VillageNet: Graph-based, Easily-interpretable, Unsupervised Clustering for Broad Biomedical Applications
Data Science

[2501.10471] VillageNet: Graph-based, Easily-interpretable, Unsupervised Clustering for Broad Biomedical Applications

VillageNet introduces a novel unsupervised clustering algorithm designed for high-dimensional biomedical datasets, enhancing interpretabi...

arXiv - Machine Learning · 4 min ·
[2411.02770] A spectral mixture representation of isotropic kernels with application to random Fourier features
Machine Learning

[2411.02770] A spectral mixture representation of isotropic kernels with application to random Fourier features

This paper presents a spectral mixture representation of isotropic kernels, enhancing the application of Random Fourier Features (RFF) in...

arXiv - Machine Learning · 4 min ·
[2501.00773] Revisiting Graph Neural Networks for Graph-level Tasks: Taxonomy, Empirical Study, and Future Directions
Machine Learning

[2501.00773] Revisiting Graph Neural Networks for Graph-level Tasks: Taxonomy, Empirical Study, and Future Directions

This article presents a comprehensive study on Graph Neural Networks (GNNs) for graph-level tasks, categorizing them into five types and ...

arXiv - AI · 4 min ·
[2411.01685] Reducing Biases in Record Matching Through Scores Calibration
Machine Learning

[2411.01685] Reducing Biases in Record Matching Through Scores Calibration

This paper explores methods to reduce biases in record matching through score calibration, proposing two model-agnostic post-processing t...

arXiv - Machine Learning · 4 min ·
Previous Page 70 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