Supervised Learning Problem

Supervised learning aims to train models that accurately predict outputs based on given input data and corresponding labels. Current research emphasizes addressing challenges like limited or noisy labels, exploring techniques such as reinforcement learning integration, and developing robust algorithms for various data structures and prediction tasks, including those involving structured outputs and non-decomposable evaluation metrics. This field is crucial for advancing machine learning applications across diverse domains, from image classification and natural language processing to financial modeling and robotics, by improving model accuracy, efficiency, and generalization capabilities. Furthermore, a deeper understanding of the underlying geometry and stability of supervised learning problems is actively being pursued to enhance model robustness and mitigate the effects of data corruption.

Papers

October 14, 2024

A Human-Centered Approach for Improving Supervised Learning
Shubhi Bansal, Atharva Tendulkar, Nagendra Kumar
Machine Learning Supervised Learning Human Centric Supervised Machine Learning Stacking Ensemble Supervised Learning Problem

September 4, 2024

Introduction to Machine Learning
Laurent Younes
Machine Learning Unsupervised Learning Predictive Model Gentle Introduction Vector Machine Supervised Learning Problem

June 24, 2024

Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Zixia Jia, Junpeng Li, Shichuan Zhang, Anji Liu, Zilong Zheng
Reinforcement Learning Multi Label Classification Annotated Dataset Positive Unlabeled Learning Multi Label Image Classification Partial Label Supervised Learning Problem

June 18, 2024

Structured Prediction in Online Learning
Pierre Boudart, Alessandro Rudi, Pierre Gaillard
Online Learning Supervised Machine Learning Structured Prediction Optimal Algorithm Supervised Learning Problem

March 4, 2024

Geometry and Stability of Supervised Learning Problems
Facundo Mémoli, Brantley Vose, Robert C. Williamson
Optimal Transport Core Stability Geometric Analysis Classification Problem Supervised Learning Problem Safety Margin

December 15, 2023

Simple Weak Coresets for Non-Decomposable Classification Measures
Jayesh Malaviya, Anirban Dasgupta, Rachit Chhaya
Coreset Size Differentiable Objective Supervised Learning Problem Non Decomposable Performance Measure

July 17, 2023

Corruptions of Supervised Learning Problems: Typology and Mitigations
Laura Iacovissi, Nan Lu, Robert C. Williamson
Faulty Negative Mitigation Corruption Level Common Corruption Typological Exploration Corruption Robustness Supervised Learning Problem Label Corruption General Linear Corruption Process

July 14, 2023

Machine learning for option pricing: an empirical investigation of network architectures
Laurens Van Mieghem, Antonis Papapantoleon, Jonas Papazoglou-Hennig
Machine Learning New Machine Empirical Study Machine Learning Algorithm Neural Network Architecture Network Architecture Option Pricing Supervised Learning Problem

March 25, 2023

Federated Learning without Full Labels: A Survey
Yilun Jin, Yang Liu, Kai Chen, Qiang Yang
Machine Learning Timely Survey Self Supervised Learning Semi Supervised Learning Unlabeled Data Full Label Supervised Learning Problem

February 19, 2023

Weakly Supervised Label Learning Flows
You Lu, Wenzhuo Song, Chidubem Arachie, Bert Huang
Supervised Learning Normalizing Flow Weakly Supervised Learning Flow Based Supervised Learning Problem

January 30, 2023

Understanding Self-Distillation in the Presence of Label Noise
Rudrajit Das, Sujay Sanghavi
Loss Function Label Noise Speech Presence Cross Entropy Loss Self Distillation Supervised Learning Problem

June 20, 2022

Latent Variable Modelling Using Variational Autoencoders: A survey
Vasanth Kalingeri
Deep Learning Machine Learning Timely Survey Variational Autoencoders Latent Variable Model Probability Distribution Supervised Learning Problem

June 2, 2022

When does return-conditioned supervised learning work for offline reinforcement learning?
David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna
Offline Reinforcement Learning Optimal Policy Dynamic Programming Return Conditioned Supervised Learning Supervised Learning Problem

May 22, 2022

Chain of Thought Imitation with Procedure Cloning
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum
Feature Imitation Side Chain Expert Demonstration Expert Level Performance Supervised Learning Problem Expert Iteration Self Clone

February 8, 2022

Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer
Yutong Xie, Dufan Wu, Bin Dong, Quanzheng Li
Unsupervised Learning ImageNet Dataset Supervised Deep Learning Supervised Learning Problem Trained Model Competing Risk Conditional Stochastic Optimization