IM 2017 - 2017 Workshop on Implicit Models

Probabilistic models are an important tool in machine learning. They form the basis for models that generate realistic data, uncover hidden structure, and make predictions. Traditionally, probabilistic models in machine learning have focused on prescribed models. Prescribed models specify a joint density over observed and hidden variables that can be easily evaluated. The requirement of a tractable density simplifies their learning but limits their flexibility --- several real world phenomena are better described by simulators that do not admit a tractable density. Probabilistic models defined only via the simulations they produce are called implicit models.
Arguably starting with generative adversarial networks, research on implicit models in machine learning has exploded in recent years. This workshop’s aim is to foster a discussion around the recent developments and future directions of implicit models.
Implicit models have many applications. They are used in ecology where models simulate animal populations over time; they are used in phylogeny, where simulations produce hypothetical ancestry trees; they are used in physics to generate particle simulations for high energy processes. Recently, implicit models have been used to improve the state-of-the-art in image and content generation. Part of the workshop’s focus is to discuss the commonalities among applications of implicit models.
Of particular interest at this workshop is to unite fields that work on implicit models. For example:
Generative adversarial networks (a NIPS 2016 workshop) are implicit models with an adversarial training scheme.
Recent advances in variational inference (a NIPS 2015 and 2016 workshop) have leveraged implicit models for more accurate approximations.
Approximate Bayesian computation (a NIPS 2015 workshop) focuses on posterior inference for models with implicit likelihoods.
Learning implicit models is deeply connected to two sample testing, density ratio and density difference estimation.
We hope to bring together these different views on implicit models, identifying their core challenges and combining their innovations.