# On Tractable Computation of Expected Predictions

@inproceedings{Khosravi2019OnTC, title={On Tractable Computation of Expected Predictions}, author={Pasha Khosravi and YooJung Choi and Yitao Liang and Antonio Vergari and Guy Van den Broeck}, booktitle={NeurIPS}, year={2019} }

Computing expected predictions of discriminative models is a fundamental task in machine learning that appears in many interesting applications such as fairness, handling missing values, and data analysis. Unfortunately, computing expectations of a discriminative model with respect to a probability distribution defined by an arbitrary generative model has been proven to be hard in general. In fact, the task is intractable even for simple models such as logistic regression and a naive Bayes…

## 23 Citations

Tractable computation of expected kernels

- Computer ScienceUAI
- 2021

This work constructs a circuit representation for kernels and proposes an approach to such tractable computation, and demonstrates possible advancements for kernel embedding frameworks by exploiting tractable expected kernels to derive new algorithms.

Tractable Computation of Expected Kernels by Circuits

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- 2021

This work constructs a circuit representation for kernels and proposes an approach to tractable computation, and demonstrates possible advancements for kernel embedding frameworks by exploiting tractable expected kernels to derive new algorithms for two challenging scenarios.

Tractable Regularization of Probabilistic Circuits

- Computer ScienceArXiv
- 2021

This work re-think regularization for PCs and proposes two intuitive techniques, data softening and entropy regularization, that both take advantage of PCs’ tractability and still have an efficient implementation as a computation graph.

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- Computer ScienceECSQARU
- 2021

This work develops an efficient algorithm for assessing the robustness of classifications made by probabilistic circuits to imputations of the nonignorable portion of missing data at prediction time and shows that the algorithm is exact when the model satisfies certain constraints.

A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries

- Computer Science, MathematicsArXiv
- 2021

This paper characterize the tractability of a vocabulary of simple transformations in terms of sufficient structural constraints of the circuits they operate on, and derives a unified framework for reasoning about tractable models that generalizes several results in the literature and opens up novel tractable inference scenarios.

Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions

- Computer ScienceVLDB 2020
- 2020

This paper proposes the notion of “Certain Predictions” (CP) — a test data example can be certainly predicted (CP’ed) if all possible classifiers trained on top of all possible worlds induced by the incompleteness of data would yield the same prediction.

Handling Missing Data in Decision Trees: A Probabilistic Approach

- Computer Science, MathematicsArXiv
- 2020

This paper tackles the problem of handling missing data in decision trees by taking a probabilistic approach, and uses tractable density estimators to compute the "expected prediction" of the models of these models.

Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions

- Computer Science, MathematicsProc. VLDB Endow.
- 2020

It is shown that the proposed CPClean approach built based on CP can often significantly outperform existing techniques in terms of classification accuracy with mild manual cleaning effort.

A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference

- 2021

Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scenarios for these…

Probabilistic Circuits for Variational Inference in Discrete Graphical Models

- Computer ScienceNeurIPS
- 2020

This paper proposes a new approach that leverages the tractability of probabilistic circuit models, such as Sum Product Networks (SPN), to compute ELBO gradients exactly (without sampling) for a certain class of densities, and shows that selective-SPNs are suitable as an expressive variational distribution.

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