# Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels

arXiv: https://arxiv.org/abs/2206.12569 · HTML: https://mlsanity.com/paper/2206.12569/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 51 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent](https://mlsanity.com/paper/1902.06720/) (2019)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [On Exact Computation with an Infinitely Wide Neural Net](https://mlsanity.com/paper/1904.11955/) (2019)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Gaussian Process Behaviour in Wide Deep Neural Networks](https://mlsanity.com/paper/1804.11271/) (2018)
- [Neural Tangents: Fast and Easy Infinite Neural Networks in Python](https://mlsanity.com/paper/1912.02803/) (2019)
- [Tensor Programs II: Neural Tangent Kernel for Any Architecture](https://mlsanity.com/paper/2006.14548/) (2020)
- [Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks](https://mlsanity.com/paper/2006.07322/) (2020)
- [More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize](https://mlsanity.com/paper/2203.06176/) (2022)
- [FALKON: An Optimal Large Scale Kernel Method](https://mlsanity.com/paper/1705.10958/) (2017)
- [Enhanced Convolutional Neural Tangent Kernels](https://mlsanity.com/paper/1911.00809/) (2019)
- [Bayesian Generative Active Deep Learning](https://mlsanity.com/paper/1904.11643/) (2019)
- [Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics](https://mlsanity.com/paper/2105.03703/) (2021)
- [Batch Active Learning Using Determinantal Point Processes](https://mlsanity.com/paper/1906.07975/) (2019)
- [Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels](https://mlsanity.com/paper/2103.01210/) (2021)
- [Epistemic Uncertainty Sampling](https://mlsanity.com/paper/1909.00218/) (2019)
- [Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes](https://mlsanity.com/paper/1612.06129/) (2016)
- [Semi-supervised Batch Active Learning via Bilevel Optimization](https://mlsanity.com/paper/2010.09654/) (2020)
- [Meta-Learning with Neural Tangent Kernels](https://mlsanity.com/paper/2102.03909/) (2021)

## Built on by (most cited first, 25 of 25)

- [Learning Dynamics of LLM Finetuning](https://mlsanity.com/paper/2407.10490/) (2024)
- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [PINNACLE: PINN Adaptive ColLocation and Experimental points selection](https://mlsanity.com/paper/2404.07662/) (2024)
- [Algorithm Selection for Deep Active Learning with Imbalanced Datasets](https://mlsanity.com/paper/2302.07317/) (2023)
- [Generalized Coverage for More Robust Low-Budget Active Learning](https://mlsanity.com/paper/2407.12212/) (2024)
- [Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation](https://mlsanity.com/paper/2303.01687/) (2023)
- [Uncertainty Herding: One Active Learning Method for All Label Budgets](https://mlsanity.com/paper/2412.20644/) (2024)
- [KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection](https://mlsanity.com/paper/2307.07942/) (2023)
- [Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation](https://mlsanity.com/paper/2312.09196/) (2023)
- [Querying Easily Flip-flopped Samples for Deep Active Learning](https://mlsanity.com/paper/2401.09787/) (2024)
- [AHA: Human-Assisted Out-of-Distribution Generalization and Detection](https://mlsanity.com/paper/2410.08000/) (2024)
- [Out-of-Distribution Learning with Human Feedback](https://mlsanity.com/paper/2408.07772/) (2024)
- [Training-Free Neural Active Learning with Initialization-Robustness Guarantees](https://mlsanity.com/paper/2306.04454/) (2023)
- [Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition](https://mlsanity.com/paper/2311.00906/) (2023)
- [Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples](https://mlsanity.com/paper/2406.03944/) (2024)
- [Deep Active Learning in the Open World](https://mlsanity.com/paper/2411.06353/) (2024)
- [Synaptic Weight Distributions Depend on the Geometry of Plasticity](https://mlsanity.com/paper/2305.19394/) (2023)
- [NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage](https://mlsanity.com/paper/2306.04099/) (2023)
- [Learning Dynamics in RL Post-Training for Language Models](https://mlsanity.com/paper/2601.04670/) (2026)
- [Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling](https://mlsanity.com/paper/2311.02879/) (2023)
- [Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs](https://mlsanity.com/paper/2507.21482/) (2025)
- [Accumulative SGD Influence Estimation for Data Attribution](https://mlsanity.com/paper/2510.26185/) (2025)
- [The Speed-up Factor: A Quantitative Multi-Iteration Active Learning Performance Metric](https://mlsanity.com/paper/2602.13359/) (2026)
- [ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation](https://mlsanity.com/paper/2506.04646/) (2025)
- [Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation](https://mlsanity.com/paper/2510.22229/) (2025)

## Abstract (arXiv)

We propose a new method for approximating active learning acquisition strategies that are based on retraining with hypothetically-labeled candidate data points. Although this is usually infeasible with deep networks, we use the neural tangent kernel to approximate the result of retraining, and prove that this approximation works asymptotically even in an active learning setup -- approximating "look-ahead" selection criteria with far less computation required. This also enables us to conduct sequential active learning, i.e. updating the model in a streaming regime, without needing to retrain the model with SGD after adding each new data point. Moreover, our querying strategy, which better understands how the model's predictions will change by adding new data points in comparison to the standard ("myopic") criteria, beats other look-ahead strategies by large margins, and achieves equal or better performance compared to state-of-the-art methods on several benchmark datasets in pool-based active learning.