Machine Learning

Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.

Papers

March 29, 2024

Functional Bilevel Optimization for Machine Learning
Ieva Petrulionyte, Julien Mairal, Michael Arbel
Neural Network Machine Learning Bilevel Optimization Prediction Function
Voice Signal Processing for Machine Learning. The Case of Speaker Isolation
Radan Ganchev
Machine Learning Case Relevance Speech Quality Signal Processing Speech Intelligibility Audio Processing Task
Designing Poisson Integrators Through Machine Learning
Miguel Vaquero, David Martín de Diego, Jorge Cortés
Machine Learning Lagrangian System Time Continuous Diffeomorphisms Poisson Integrator
Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview
Yingbai Hu, Fares J. Abu-Dakka, Fei Chen, Xiao Luo, Zheng Li, Alois Knoll, Weiping Ding
Reinforcement Learning Machine Learning Deep Reinforcement Learning Imitation Learning Autonomous Dynamical System

March 28, 2024

Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
Dimitris Bertsimas, Vassilis Digalakis, Yu Ma, Phevos Paschalidis
Machine Learning Machine Learning Model Mixed Integer Model Interpretability Optimal Model Length Sequence
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Pieter Gijsbers, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Jos van der Velde, Steffen Vogler, Carole-Jean Wu
Machine Learning State of the Art Datasets Data Format Machine Learning Tool
Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning
Kangming Xu, Huiming Zhou, Haotian Zheng, Mingwei Zhu, Qi Xin
Machine Learning Recommendation System Personalized Recommendation Product Classification Learning Based Classification
Evaluating Fair Feature Selection in Machine Learning for Healthcare
Md Rahat Shahriar Zawad, Peter Washington
Machine Learning Procedural Fairness Feature Selection Healthcare System Algorithmic Fairness Fairness Metric Fair Feature Selection
Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study
Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo, Weide Li, You-Gan Wang
Machine Learning System Performance Empirical Study Critical Lesson Behavior Analysis Behavioral Pattern ML Algorithm
Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approach
Pei Xi, Lin
Deep Learning Machine Learning Bayesian Network Bayesian Deep Learning Tumor Cell Bayesian Deep

March 27, 2024

Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions
Zainab Al-Zanbouri, Gauri Sharma, Shaina Raza
Machine Learning Machine Learning Model Healthcare System Fair Machine Learning Generalized Linear Model Health Disparity Fairness Analysis
Thelxino\"e: Recognizing Human Emotions Using Pupillometry and Machine Learning
Darlene Barker, Haim Levkowitz
Machine Learning Emotion Recognition Virtual Reality Human Emotion X Klaim Pupil Diameter
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning
Syed Mhamudul Hasan, Abdur R. Shahid, Ahmed Imteaj
Machine Learning Native Robustness Adversarial Robustness Adversarial Learning Model Robustness Carbon Footprint
Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch
Machine Learning Predictive Model Explainability Method Explainability Technique Milling Process
First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques
Enzo Rucci, Gonzalo Tittarelli, Franco Ronchetti, Jorge F. Elgart, Laura Lanzarini, Juan José Gagliardino
Machine Learning Machine Learning Model Person Identification Predictive Model Person Name Type 2 Diabetes Diabetes Prediction
DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Haitao Li, Qingyao Ai, Xinyan Han, Jia Chen, Qian Dong, Yiqun Liu, Chong Chen, Qi Tian
Machine Learning Pre Trained Language Model Jina Embeddings Semantic Textual Similarity Delta XAI Legal Case Retrieval Discriminative Encoder

March 26, 2024

Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
Hanzhe Li, Xiangxiang Wang, Yuan Feng, Yaqian Qi, Jingxiao Tian
Machine Learning External Control Edge Computing Cloud Computing IOT Technology Traditional Cloud IoT Monitoring
PerOS: Personalized Self-Adapting Operating Systems in the Cloud
Hongyu Hè
Machine Learning Potential Scalability Cloud Computing Scalable Architecture Personalized System
Particle identification with machine learning from incomplete data in the ALICE experiment
Maja Karwowska, Łukasz Graczykowski, Kamil Deja, Miłosz Kasak, Małgorzata Janik
Machine Learning New Machine Incomplete Data Active Learning With Contrastive Explanation Particle Identification Particle Picking
Application-Driven Innovation in Machine Learning
David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
Machine Learning Data Driven Machine Learning

Machine Learning

Papers

Functional Bilevel Optimization for Machine Learning

Voice Signal Processing for Machine Learning. The Case of Speaker Isolation

Designing Poisson Integrators Through Machine Learning

Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview

Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences

Croissant: A Metadata Format for ML-Ready Datasets

Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning

Evaluating Fair Feature Selection in Machine Learning for Healthcare

Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study

Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approach

Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

Thelxino\"e: Recognizing Human Emotions Using Pupillometry and Machine Learning

Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning

Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods

First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques

DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment

Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning

PerOS: Personalized Self-Adapting Operating Systems in the Cloud

Particle identification with machine learning from incomplete data in the ALICE experiment

Application-Driven Innovation in Machine Learning