Bayesian Learning
A Minimax Approach to Supervised Learning
Neural Information Processing Systems http://nips.cc/
Stochastic Gradient Geodesic MCMC Methods
Neural Information Processing Systems http://nips.cc/
Bayesian latent structure discovery from multi-neuron recordings
Scott Linderman, Ryan P. Adams, Jonathan W. Pillow
Neural Information Processing Systems http://nips.cc/
A Unified Approach for Learning the Parameters of Sum-Product Networks
Han Zhao, Pascal Poupart, Geoffrey J. Gordon
Neural Information Processing Systems http://nips.cc/
Fast ฮต-free Inference of Simulation Models with Bayesian Conditional Density Estimation
George Papamakarios, Iain Murray
Neural Information Processing Systems http://nips.cc/
Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain
Timothy Rubin, Oluwasanmi O. Koyejo, Michael N. Jones, Tal Yarkoni
This paper presents Generalized Correspondence-LDA (GC-LDA), a generalization of the Correspondence-LDA model that allows for variable spatial representations to be associated with topics, and increased flexibility in terms of the strength of the correspondence between data types induced by the model. We present three variants of GC-LDA, each of which associates topics with a different spatial representation, and apply them to a corpus of neuroimaging data. In the context of this dataset, each topic corresponds to a functional brain region, where the region's spatial extent is captured by a probability distribution over neural activity, and the region's cognitive function is captured by a probability distribution over linguistic terms. We illustrate the qualitative improvements offered by GC-LDA in terms of the types of topics extracted with alternative spatial representations, as well as the model's ability to incorporate a-priori knowledge from the neuroimaging literature. We furthermore demonstrate that the novel features of GC-LDA improve predictions for missing data.
Data Programming: Creating Large Training Sets, Quickly
Alexander J. Ratner, Christopher M. De Sa, Sen Wu, Daniel Selsam, Christopher Rรฉ
Neural Information Processing Systems http://nips.cc/
Tractable Operations for Arithmetic Circuits of Probabilistic Models
Yujia Shen, Arthur Choi, Adnan Darwiche
We consider tractable representations of probability distributions and the polytime operations they support. In particular, we consider a recently proposed arithmetic circuit representation, the Probabilistic Sentential Decision Diagram (PSDD). We show that PSDDs support a polytime multiplication operator, while they do not support a polytime operator for summing-out variables. A polytime multiplication operator makes PSDDs suitable for a broader class of applications compared to classes of arithmetic circuits that do not support multiplication. As one example, we show that PSDD multiplication leads to a very simple but effective compilation algorithm for probabilistic graphical models: represent each model factor as a PSDD, and then multiply them.
Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model
Zhen Xu, Wen Dong, Sargur N. Srihari
Neural Information Processing Systems http://nips.cc/
A Probabilistic Programming Approach To Probabilistic Data Analysis
Feras Saad, Vikash K. Mansinghka
Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include discriminative machine learning, hierarchical Bayesian models, multivariate kernel methods, clustering algorithms, and arbitrary probabilistic programs. We demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling definition language and structured query language. The practical value is illustrated in two ways. First, the paper describes an analysis on a database of Earth satellites, which identifies records that probably violate Kepler's Third Law by composing causal probabilistic programs with nonparametric Bayes in 50 lines of probabilistic code. Second, it reports the lines of code and accuracy of CGPMs compared with baseline solutions from standard machine learning libraries.