# Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs

arXiv: https://arxiv.org/abs/2202.08566 · HTML: https://mlsanity.com/paper/2202.08566/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Limitations of the Empirical Fisher Approximation for Natural Gradient Descent](https://mlsanity.com/paper/1905.12558/) (2019)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Making deep neural networks right for the right scientific reasons by interacting with their explanations](https://mlsanity.com/paper/2001.05371/) (2020)
- [Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits](https://mlsanity.com/paper/2004.06231/) (2020)
- [Coherent Hierarchical Multi-Label Classification Networks](https://mlsanity.com/paper/2010.10151/) (2020)
- [Interactive Label Cleaning with Example-based Explanations](https://mlsanity.com/paper/2106.03922/) (2021)
- [Active Learning for Cost-Sensitive Classification](https://mlsanity.com/paper/1703.01014/) (2017)
- [Active Learning with Partial Feedback](https://mlsanity.com/paper/1802.07427/) (2018)
- [Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures](https://mlsanity.com/paper/1905.08550/) (2019)
- [Probabilistic Circuits for Variational Inference in Discrete Graphical Models](https://mlsanity.com/paper/2010.11446/) (2020)
- [Learning in the Wild with Incremental Skeptical Gaussian Processes](https://mlsanity.com/paper/2011.00928/) (2020)
- [Solving Marginal MAP Exactly by Probabilistic Circuit Transformations](https://mlsanity.com/paper/2111.04833/) (2021)
- [A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries](https://mlsanity.com/paper/2102.06137/) (2021)
- [Solving Marginal MAP Problems with NP Oracles and Parity Constraints](https://mlsanity.com/paper/1610.02591/) (2016)
- [Partial Or Complete, That's The Question](https://mlsanity.com/paper/1906.04937/) (2019)
- [Active Learning for Structured Prediction from Partially Labeled Data](https://mlsanity.com/paper/1706.02342/) (2017)
- [Active Learning amidst Logical Knowledge](https://mlsanity.com/paper/1709.08850/) (2017)

## Built on by (most cited first, 2 of 2)

- [Semantic Probabilistic Layers for Neuro-Symbolic Learning](https://mlsanity.com/paper/2206.00426/) (2022)
- [Lifelong Personal Context Recognition](https://mlsanity.com/paper/2205.10123/) (2022)

## Abstract (arXiv)

In this position paper, we study interactive learning for structured output spaces, with a focus on active learning, in which labels are unknown and must be acquired, and on skeptical learning, in which the labels are noisy and may need relabeling. These scenarios require expressive models that guarantee reliable and efficient computation of probabilistic quantities to measure uncertainty. We identify conditions under which a class of probabilistic models -- which we denote CRISPs -- meet all of these conditions, thus delivering tractable computation of the above quantities while preserving expressiveness. Building on prior work on tractable probabilistic circuits, we illustrate how CRISPs enable robust and efficient active and skeptical learning in large structured output spaces.