# Semi-supervised Batch Active Learning via Bilevel Optimization

arXiv: https://arxiv.org/abs/2010.09654 · HTML: https://mlsanity.com/paper/2010.09654/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)
- [DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning](https://mlsanity.com/paper/2204.08499/) (2022)
- [A Framework and Benchmark for Deep Batch Active Learning for Regression](https://mlsanity.com/paper/2203.09410/) (2022)
- [AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving](https://mlsanity.com/paper/2403.17373/) (2024)
- [Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels](https://mlsanity.com/paper/2206.12569/) (2022)
- [LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning](https://mlsanity.com/paper/2306.09910/) (2023)
- [Data Summarization via Bilevel Optimization](https://mlsanity.com/paper/2109.12534/) (2021)
- [On Sampling Collaborative Filtering Datasets](https://mlsanity.com/paper/2201.04768/) (2022)
- [Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification](https://mlsanity.com/paper/2209.01858/) (2022)
- [Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification](https://mlsanity.com/paper/2406.05677/) (2024)
- [Neural Active Learning on Heteroskedastic Distributions](https://mlsanity.com/paper/2211.00928/) (2022)
- [Black-box Coreset Variational Inference](https://mlsanity.com/paper/2211.02377/) (2022)
- [Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness: Theories and Applications](https://mlsanity.com/paper/2410.13743/) (2024)
- [NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage](https://mlsanity.com/paper/2306.04099/) (2023)
- [Farzi Data: Autoregressive Data Distillation](https://mlsanity.com/paper/2310.09983/) (2023)
- [Learning to Rank for Active Learning via Multi-Task Bilevel Optimization](https://mlsanity.com/paper/2310.17044/) (2023)

## Abstract (arXiv)

Active learning is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel batch acquisition strategy for active learning in the setting where the model training is performed in a semi-supervised manner. We formulate our approach as a data summarization problem via bilevel optimization, where the queried batch consists of the points that best summarize the unlabeled data pool. We show that our method is highly effective in keyword detection tasks in the regime when only few labeled samples are available.