# Transductive Active Learning: Theory and Applications

arXiv: https://arxiv.org/abs/2402.15898 · HTML: https://mlsanity.com/paper/2402.15898/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2024.

## Built on by (most cited first, 22 of 22)

- [Post-hoc Probabilistic Vision-Language Models](https://mlsanity.com/paper/2412.06014/) (2024)
- [DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning](https://mlsanity.com/paper/2505.19850/) (2025)
- [Active Fine-Tuning of Multi-Task Policies](https://mlsanity.com/paper/2410.05026/) (2024)
- [Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging](https://mlsanity.com/paper/2505.14136/) (2025)
- [Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning](https://mlsanity.com/paper/2510.04786/) (2025)
- [Epistemically-guided forward-backward exploration](https://mlsanity.com/paper/2507.05477/) (2025)
- [Test-time Offline Reinforcement Learning on Goal-related Experience](https://mlsanity.com/paper/2507.18809/) (2025)
- [Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models](https://mlsanity.com/paper/2509.24510/) (2025)
- [LITE: Efficiently Estimating Gaussian Probability of Maximality](https://mlsanity.com/paper/2501.13535/) (2025)
- [Distributionally Robust Active Learning for Gaussian Process Regression](https://mlsanity.com/paper/2502.16870/) (2025)
- [Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation](https://mlsanity.com/paper/2509.21866/) (2025)
- [Loss-Driven Bayesian Active Learning](https://mlsanity.com/paper/2604.11995/) (2026)
- [ActiveCQ: Active Estimation of Causal Quantities](https://mlsanity.com/paper/2509.24293/) (2025)
- [Observationally Informed Adaptive Causal Experimental Design](https://mlsanity.com/paper/2603.03785/) (2026)
- [Simulation Priors for Data-Efficient Deep Learning](https://mlsanity.com/paper/2509.05732/) (2025)
- [Boosting Active Learning with Knowledge Transfer](https://mlsanity.com/paper/2509.15805/) (2025)
- [Calibrated Uncertainty Sampling for Active Learning](https://mlsanity.com/paper/2510.03162/) (2025)
- [Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights](https://mlsanity.com/paper/2605.10654/) (2026)
- [Sampling-Based Safe Reinforcement Learning](https://mlsanity.com/paper/2605.19469/) (2026)
- [Joint Model and Data Sparsification via the Marginal Likelihood](https://mlsanity.com/paper/2605.29908/) (2026)
- [Learning to Extrapolate to New Tasks: A Relational Approach to Task Extrapolation](https://mlsanity.com/paper/2605.30132/) (2026)
- [ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation](https://mlsanity.com/paper/2606.02247/) (2026)

## Abstract (arXiv)

We study a generalization of classical active learning to real-world settings with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region. We analyze a family of decision rules that sample adaptively to minimize uncertainty about prediction targets. We are the first to show, under general regularity assumptions, that such decision rules converge uniformly to the smallest possible uncertainty obtainable from the accessible data. We demonstrate their strong sample efficiency in two key applications: active fine-tuning of large neural networks and safe Bayesian optimization, where they achieve state-of-the-art performance.