atlas

Research map / Learning theory and algorithms / Prediction and decision losses

Bayesian Batch Active Learning as Sparse Subset Approximation

NeurIPS 2019 · Learning theory and algorithms › Prediction and decision losses · approach: Active learning and subset selection · 66 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
use ACS-FW batch construction instead of random selectionneural linear model · yachttest RMSE: 1.272 -> 1.031Helped
use ACS-FW instead of naive top-b MAXENT batchesneural linear model · energytest RMSE: 1.443 -> 0.855Helped
use ACS-FW instead of naive top-b MAXENT batchesneural linear model · yachttest RMSE: 0.923 -> 1.031Hurt
use ACS-FW instead of naive top-b MAXENT batchesneural linear model · yeartest RMSE: 13.030 -> 12.194Helped
use sequential greedy MAXENT-SG instead of ACS-FW batchesneural linear model · bostontest RMSE: 3.799 -> 3.458Helped
use ACS-FW instead of RANDOM on large classification tasksResNet18 neural linear model · cifar10, SVHN, Fashion MNISTaccuracyHelped
use ACS-FW instead of K-Center/BALD/MaxEntResNet18 neural linear model · cifar10, SVHN, Fashion MNISTaccuracyNo clear effect
use ACS-FW instead of MAXENT on batch construction runtimeneural linear model · powertotal runtime (s): 617.0 -> 179.8Helped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy