Sampling Strategy

Sampling strategy, the method of selecting data subsets for analysis or model training, is crucial for efficient and accurate results across diverse scientific domains. Current research focuses on optimizing sampling techniques for various applications, including improving the efficiency of machine learning model explanations (e.g., Shapley values), enhancing the accuracy and speed of reduced-order models, and mitigating biases in generative models. Effective sampling strategies are essential for addressing computational limitations, improving model performance, and ensuring fairness and representativeness in data-driven analyses, impacting fields from machine learning and Bayesian inference to remote sensing and robotics.

Papers

August 31, 2022

Cluster-based Sampling in Hindsight Experience Replay for Robotic Tasks (Student Abstract)
Taeyoung Kim, Dongsoo Har
Sparse Reward Robotic Task Experience Replay Sampling Strategy Hindsight Experience Replay Multi Goal Reinforcement Learning Based Sampling

August 18, 2022

Monte Carlo is a good sampling strategy for polynomial approximation in high dimensions
Ben Adcock, Simone Brugiapaglia
High Dimensional Sample Complexity Least Square High Dimension Monte Carlo Sampling Strategy Polynomial Approximation Polynomial Basis

April 19, 2022

Sampling Strategies for Static Powergrid Models
Stephan Balduin, Eric MSP Veith, Sebastian Lehnhoff
Power Grid Dimensional Copula Power Flow Sampling Strategy Sampling Based Algorithm

February 9, 2022

Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data
Adrianna Janik, Kris Sankaran
Semantic Segmentation Raw Data Remote Sensing Semantic Segmentation Model Sampling Strategy Crisis Event Humanitarian Response Scarce Annotation Segmentation Refinement

February 1, 2022

Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning
Jin-Hyun Ahn, Kyungsang Kim, Jeongwan Koh, Quanzheng Li
Active Learning Federated Learning Annotation Strategy Efficient Annotation Sampling Strategy Federated Active Learning

January 13, 2022

On Sampling Collaborative Filtering Datasets
Noveen Sachdeva, Carole-Jean Wu, Julian McAuley
Recommender System Collaborative Filtering Recommendation Algorithm Sampling Strategy

December 8, 2021

Equity Promotion in Online Resource Allocation
Pan Xu, Yifan Xu
Sampling Strategy Online Resource Allocation Fair Allocation Equity Context

November 16, 2021

Sequential Community Mode Estimation
Shubham Anand Jain, Shreyas Goenka, Divyam Bapna, Nikhil Karamchandani, Jayakrishnan Nair
Practical Algorithm Information Theoretic Random Participation Sampling Strategy Random Sampling