Supervised Learning

Supervised learning, a core machine learning paradigm, aims to train models to predict outputs based on labeled input data. Current research emphasizes improving model efficiency and robustness, particularly in scenarios with limited or noisy data, exploring techniques like self-supervised pre-training, active learning for data selection, and ensemble methods to enhance accuracy and address class imbalances. These advancements are crucial for various applications, from medical image analysis and infrastructure inspection to natural language processing and targeted advertising, enabling more accurate and reliable predictions with less reliance on extensive labeled datasets.

Papers

April 14, 2022

CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
Rakesh R Menon, Sayan Ghosh, Shashank Srivastava
New Benchmark Supervised Learning Natural Language Explanation Language Supervision Superficial Clue Inductive Learning Learning Classifier

April 13, 2022

Label Augmentation with Reinforced Labeling for Weak Supervision
Gürkan Solmaz, Flavio Cirillo, Fabio Maresca, Anagha Gode Anil Kumar
Supervised Learning Weak Supervision Textual Label Labeling Function Label Augmentation Data Programming

April 12, 2022

April 6, 2022

Marrying Fairness and Explainability in Supervised Learning
Przemyslaw Grabowicz, Nicholas Perello, Aarshee Mishra
High Explainability Supervised Learning Causal Effect Shapley Additive Explanation Group DIscrimination Fairness Aware Machine Direct Discrimination

April 4, 2022

Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming
Sadhana Tiwari, Sonali Agarwal
Supervised Learning Feature Selection Empirical Analysis Disease Classification Apache Spark Unsupervised Model Selection Lifelog Data

April 1, 2022

Physics Informed Shallow Machine Learning for Wind Speed Prediction
Daniele Lagomarsino-Oneto, Giacomo Meanti, Nicolò Pagliana, Alessandro Verri, Andrea Mazzino, Lorenzo Rosasco, Agnese Seminara
Time Series Forecasting Supervised Learning Physic Informed Machine Learning Data Driven Approach Weather Forecasting Wind Speed Prediction Horizon

March 31, 2022

March 24, 2022

Open-set Recognition via Augmentation-based Similarity Learning
Sepideh Esmaeilpour, Lei Shu, Bing Liu
Supervised Learning Recognition Rate Open Set Recognition Unseen Data Class Sample

March 23, 2022

A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks
Miguel Romero, Jorge Finke, Camilo Rocha
Network Programming Supervised Learning Node Classification Hierarchical Multi Label Classification Top Down Supervised Learning Approach

March 17, 2022

March 16, 2022

Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography
John D. Miller, Vignesh A. Arasu, Albert X. Pu, Laurie R. Margolies, Weiva Sieh, Li Shen
Deep Learning Self Supervised Learning Self Supervised Supervised Learning Image Analysis Mammography Report Breast Cancer Detection Strong Augmentation

March 14, 2022

Supervised Learning

Papers

CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations

Label Augmentation with Reinforced Labeling for Weak Supervision

Hybrid Feature- and Similarity-Based Models for Joint Prediction and Interpretation

A Robust Learning Rule for Soft-Bounded Memristive Synapses Competitive with Supervised Learning in Standard Spiking Neural Networks

Positive Feature Values Prioritized Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Hierarchical Feature Spaces

TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning

Active Learning with Label Comparisons

Towards efficient representation identification in supervised learning

Marrying Fairness and Explainability in Supervised Learning

Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming

Physics Informed Shallow Machine Learning for Wind Speed Prediction

Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks

Conditional Autoregressors are Interpretable Classifiers

Open-set Recognition via Augmentation-based Similarity Learning

A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks

Continual Learning Based on OOD Detection and Task Masking

A Framework and Benchmark for Deep Batch Active Learning for Regression

Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography

Don't fear the unlabelled: safe semi-supervised learning via simple debiasing

A Supervised Learning Approach to Rankability