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

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

December 28, 2021

To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
Bingqing Song, Haoran Sun, Wenqiang Pu, Sijia Liu, Mingyi Hong
Supervised Learning Unsupervised Learning Interference Management

December 20, 2021

RvS: What is Essential for Offline RL via Supervised Learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine
Reinforcement Learning Supervised Learning Two Layer Neural Network Temporal Difference Learning

December 16, 2021

High Fidelity Visualization of What Your Self-Supervised Representation Knows About
Florian Bordes, Randall Balestriero, Pascal Vincent
Generative Model Self Supervised Learning Supervised Learning Conditional Diffusion Model Self Supervised Model Self Supervised Representation

Supervised Learning

Papers

Active learning with binary models for real time data labelling

All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL

Learning Relative Return Policies With Upside-Down Reinforcement Learning

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

The no-free-lunch theorems of supervised learning

Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification

Addressing Data Scarcity in Multimodal User State Recognition by Combining Semi-Supervised and Supervised Learning

Adaptive Mixing of Auxiliary Losses in Supervised Learning

A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification

A Survey on Poisoning Attacks Against Supervised Machine Learning

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning

Learning to Minimize the Remainder in Supervised Learning

Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation

Generalization in Supervised Learning Through Riemannian Contraction

Hyperparameter Importance for Machine Learning Algorithms

Supervised Learning based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study

'Moving On' -- Investigating Inventors' Ethnic Origins Using Supervised Learning

To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?

RvS: What is Essential for Offline RL via Supervised Learning?

High Fidelity Visualization of What Your Self-Supervised Representation Knows About