# Improved Algorithms for Neural Active Learning

arXiv: https://arxiv.org/abs/2210.00423 · HTML: https://mlsanity.com/paper/2210.00423/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 58 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 28 of 28)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [A Convergence Theory for Deep Learning via Over-Parameterization](https://mlsanity.com/paper/1811.03962/) (2018)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Gradient Descent Finds Global Minima of Deep Neural Networks](https://mlsanity.com/paper/1811.03804/) (2018)
- [On Exact Computation with an Infinitely Wide Neural Net](https://mlsanity.com/paper/1904.11955/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks](https://mlsanity.com/paper/1905.13210/) (2019)
- [Finite-Time Analysis of Kernelised Contextual Bandits](https://mlsanity.com/paper/1309.6869/) (2013)
- [Neural Thompson Sampling](https://mlsanity.com/paper/2010.00827/) (2020)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [Confidence-Aware Learning for Deep Neural Networks](https://mlsanity.com/paper/2007.01458/) (2020)
- [Gone Fishing: Neural Active Learning with Fisher Embeddings](https://mlsanity.com/paper/2106.09675/) (2021)
- [A Survey of Active Learning for Text Classification using Deep Neural Networks](https://mlsanity.com/paper/2008.07267/) (2020)
- [EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits](https://mlsanity.com/paper/2110.03177/) (2021)
- [A Benchmark and Comparison of Active Learning for Logistic Regression](https://mlsanity.com/paper/1611.08618/) (2016)
- [Deep Active Learning by Leveraging Training Dynamics](https://mlsanity.com/paper/2110.08611/) (2021)
- [Efficient active learning of sparse halfspaces with arbitrary bounded noise](https://mlsanity.com/paper/2002.04840/) (2020)
- [Local Clustering in Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2103.00063/) (2021)
- [Neural Active Learning with Performance Guarantees](https://mlsanity.com/paper/2106.03243/) (2021)
- [Diversity Enhanced Active Learning with Strictly Proper Scoring Rules](https://mlsanity.com/paper/2110.14171/) (2021)
- [Multi-facet Contextual Bandits: A Neural Network Perspective](https://mlsanity.com/paper/2106.03039/) (2021)
- [Generic Outlier Detection in Multi-Armed Bandit](https://mlsanity.com/paper/2007.07293/) (2020)
- [Efficient active learning of sparse halfspaces](https://mlsanity.com/paper/1805.02350/) (2018)
- [Adaptivity to Noise Parameters in Nonparametric Active Learning](https://mlsanity.com/paper/1703.05841/) (2017)
- [Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss](https://mlsanity.com/paper/1812.01815/) (2018)
- [Neural Bandit with Arm Group Graph](https://mlsanity.com/paper/2206.03644/) (2022)
- [Neural Collaborative Filtering Bandits via Meta Learning](https://mlsanity.com/paper/2201.13395/) (2022)

## Built on by (most cited first, 11 of 11)

- [Streaming Active Learning with Deep Neural Networks](https://mlsanity.com/paper/2303.02535/) (2023)
- [Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization](https://mlsanity.com/paper/2506.17252/) (2025)
- [PageRank Bandits for Link Prediction](https://mlsanity.com/paper/2411.01410/) (2024)
- [Neural Contextual Bandits for Personalized Recommendation](https://mlsanity.com/paper/2312.14037/) (2023)
- [Neural Active Learning Beyond Bandits](https://mlsanity.com/paper/2404.12522/) (2024)
- [Neural Exploitation and Exploration of Contextual Bandits](https://mlsanity.com/paper/2305.03784/) (2023)
- [Meta Clustering of Neural Bandits](https://mlsanity.com/paper/2408.05586/) (2024)
- [Active Cost-aware Labeling of Streaming Data](https://mlsanity.com/paper/2304.06808/) (2023)
- [Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2312.01835/) (2023)
- [Neural Active Learning Meets the Partial Monitoring Framework](https://mlsanity.com/paper/2405.08921/) (2024)
- [Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity](https://mlsanity.com/paper/2506.12389/) (2025)

## Abstract (arXiv)

We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work. Then, the proposed algorithm leverages the powerful representation of NNs for both exploitation and exploration, has the query decision-maker tailored for k-class classification problems with the performance guarantee, utilizes the full feedback, and updates parameters in a more practical and efficient manner. These careful designs lead to an instance-dependent regret upper bound, roughly improving by a multiplicative factor O(log T) and removing the curse of input dimensionality. Furthermore, we show that the algorithm can achieve the same performance as the Bayes-optimal classifier in the long run under the hard-margin setting in classification problems. In the end, we use extensive experiments to evaluate the proposed algorithm and SOTA baselines, to show the improved empirical performance.