# Variational f-divergence Minimization

@article{Zhang2019VariationalFM, title={Variational f-divergence Minimization}, author={Mingtian Zhang and Thomas Bird and Raza Habib and Tianlin Xu and David Barber}, journal={ArXiv}, year={2019}, volume={abs/1907.11891} }

Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution. In light of recent successes in training Generative Adversarial Networks, alternative non-likelihood training criteria have been proposed. Whilst not necessarily statistically efficient, these alternatives may better match user requirements such as sharp image generation. A general variational method for training probabilistic latent…

## 15 Citations

### Spread Divergences

- Computer ScienceArXiv
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This work defines a spread divergence on modified p and q and describes sufficient conditions for the existence of such a divergence and demonstrates how to maximize the discriminatory power of a given divergence by parameterizing and learning the spread.

### Imitation Learning as f-Divergence Minimization

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This work proposes a general imitation learning framework for estimating and minimizing any f-Divergence, and shows that the approximate I-projection technique is able to imitate multi-modal behaviors more reliably than GAIL and behavior cloning.

### Posterior-Aided Regularization for Likelihood-Free Inference

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This paper proposes a universally applicable regularization technique, called Posterior-Aided Regularization (PAR), which is applicable to learning the density estimator, regardless of the model structure, and theoretically proves the asymptotic convergence of the regularized optimal solution to the unregularized optimal solutions as the regularization magnitude converges to zero.

### Low-Discrepancy Points via Energetic Variational Inference

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- 2021

A deterministic variational inference approach and generate lowdiscrepancy points by minimizing the kernel discrepancy, also known as the Maximum Mean Discrepancy or MMD, is proposed and the resulting algorithm is named EVI-MMD.

### f-Divergence Variational Inference

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The $f$-VI framework not only unifies a number of existing VI methods, but offers a standardized toolkit for VI subject to arbitrary divergences from the f-divergence family, and provides a sandwich estimate of marginal likelihood (or evidence).

### GFlowNets and variational inference

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This paper builds bridges between two families of probabilistic algorithms: (hi-erarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and…

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The skew-geometric Jensen-Shannon divergence ( JSα ) allows for an intuitive interpolation between forward and reverse Kullback-Leibler (KL) divergence based on the skew parameter α. While the…

### Neural Posterior Regularization for Likelihood-Free Inference

- Computer Science
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This paper introduces a regularization technique, namely Neural Posterior Regularization (NPR), which enforces the model to explore the input parameter space e ﬀ ectively and empirically validate that NPR attains the statistically signiﬁcant gain on benchmark performances for diverse simulation tasks.

### Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation

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### Constraining Variational Inference with Geometric Jensen-Shannon Divergence

- Computer ScienceNeurIPS
- 2020

This work presents a regularisation mechanism based on the skew geometric-Jensen-Shannon divergence, motivated by limiting cases, which leads to an intuitive interpolation between forward and reverse KL in the space of both distributions and divergences.

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