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

March 4, 2022

Better Supervisory Signals by Observing Learning Paths
Yi Ren, Shangmin Guo, Danica J. Sutherland
Knowledge Distillation Supervised Learning Label Smoothing Label Refinement Supervision Signal Novel Knowledge Distillation Learning Path

March 3, 2022

A study on the distribution of social biases in self-supervised learning visual models
Kirill Sirotkin, Pablo Carballeira, Marcos Escudero-Viñolo
Self Supervised Learning Study Feature Supervised Learning Social Bias ImageNet Dataset Product Distribution Visual Model Systematic Bias

March 2, 2022

Learning Efficiently Function Approximation for Contextual MDP
Orin Levy, Yishay Mansour
LeArning Abstract Supervised Learning Function Approximation Contextual MDPs

March 1, 2022

An Information-Theoretic Framework for Supervised Learning
Hong Jun Jeon, Yifan Zhu, Benjamin Van Roy
Deep Learning Supervised Learning Sample Complexity Sample Complexity Bound Information Theoretic Framework

February 28, 2022

Active learning with binary models for real time data labelling
Ankush Deshmukh, Bhargava B C, A V Narasimhadhan
Deep Learning Machine Learning Active Learning Supervised Learning Machine Learning Algorithm Data Labeling Binary Model Label Extraction

February 24, 2022

All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh K. Srivastava
Reinforcement Learning Imitation Learning Supervised Learning Goal Conditioned Reinforcement Learning Human Like RL

February 23, 2022

Learning Relative Return Policies With Upside-Down Reinforcement Learning
Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava
Supervised Learning Based Policy Goal Conditioned Policy Tabular Reinforcement Learning

February 18, 2022

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
Mark Collier, Rodolphe Jenatton, Efi Kokiopoulou, Jesse Berent
Neural Network Supervised ImageNet Supervised Learning Label Noise Formality Transfer Privileged Information

February 9, 2022

The no-free-lunch theorems of supervised learning
Tom F. Sterkenburg, Peter D. Grünwald
Supervised Learning Inductive Bias Learning Algorithm Justification Theory Learning Theory

February 8, 2022

February 7, 2022

Adaptive Mixing of Auxiliary Losses in Supervised Learning
Durga Sivasubramanian, Ayush Maheshwari, Pradeep Shenoy, Prathosh AP, Ganesh Ramakrishnan
Knowledge Distillation Supervised Learning Weak Label Global Mixing Auxiliary Loss

February 5, 2022

January 31, 2022

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
Conrad M Albrecht, Fernando Marianno, Levente J Klein
Supervised Learning Remote Sensing Land Cover Human Labeled Label Generation Geospatial Machine Learning

January 23, 2022

Learning to Minimize the Remainder in Supervised Learning
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli, Qi Zhao
Deep Learning LeArning Abstract Image Classification Supervised Learning Residual Stream

January 21, 2022

Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
Qianben Chen, Richong Zhang, Yaowei Zheng, Yongyi Mao
Contrastive Learning Representation Learning Text Classification Supervised Learning Discriminative Representation Dual Contrastive Learning

January 17, 2022

Generalization in Supervised Learning Through Riemannian Contraction
Leo Kozachkov, Patrick M. Wensing, Jean-Jacques Slotine
Strong Generalization Gradient Descent Supervised Learning Generalization Bound Convex Loss Riemannian Geometry

January 13, 2022

Hyperparameter Importance for Machine Learning Algorithms
Honghe Jin
Supervised Learning Machine Learning Algorithm Related Hyperparameters New Hyperparameter Hyperparameter Importance

January 3, 2022

Supervised Learning based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study
Arslan Ahmad, Atif Bin Mansoor, Alcardo Alex Barakabitze, Andrew Hines, Luigi Atzori, Ray Walshe
Supervised Learning Tutorial Review Video Streaming Quality of Experience Future Network