A Framework and Benchmark for Deep Batch Active Learning for Regression

17 March 2022

Papers citing "A Framework and Benchmark for Deep Batch Active Learning for Regression"

9 / 9 papers shown

Title
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective Hechuan Wen Tong Chen Mingming Gong Li Kheng Chai S. Sadiq Hongzhi Yin CML 46 0 0 08 May 2025
Efficient Data Selection for Training Genomic Perturbation Models G. Panagopoulos J. Lutzeyer Sofiane Ennadir Michalis Vazirgiannis Jun Pang 80 0 0 18 Mar 2025
Active Learning for Neural PDE Solvers Daniel Musekamp Marimuthu Kalimuthu David Holzmüller Makoto Takamoto Carlos Fernandez AI4CE 41 4 0 02 Aug 2024
Gradient Aligned Regression via Pairwise Losses Dixian Zhu Tianbao Yang Livnat Jerby-Arnon 34 0 0 08 Feb 2024
Simulation Intelligence: Towards a New Generation of Scientific Methods Alexander Lavin D. Krakauer Hector Zenil Justin Emile Gottschlich Tim Mattson ... A. Hanuka Manuela Veloso Samuel A. Assefa Stephan Zheng Avi Pfeffer 40 102 0 06 Dec 2021
Deep Active Learning by Leveraging Training Dynamics Haonan Wang Wei Huang Ziwei Wu A. Margenot Hanghang Tong Jingrui He AI4CE 27 31 0 16 Oct 2021
Semi-supervised Batch Active Learning via Bilevel Optimization Zalan Borsos Marco Tagliasacchi Andreas Krause 29 23 0 19 Oct 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015