Learning Graphical Models
Supplementary Material of " Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks "
We provide a detailed proof for Theorem 1. We provide an alternative proof for identifiability of Poisson BN. I (G D), where the last equality holds because the integrand is the kernel of a beta distribution. The scRNA-seq experiments were performed on five mice with AhR knockout targeted to intestinal stem cells. On average each mouse contributed 6,000 cells.
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
Multivariate zero-inflated count data arise in a wide range of areas such as economics, social sciences, and biology.
Streaming Bayesian Inference for Crowdsourced Classification
Edoardo Manino, Long Tran-Thanh, Nicholas Jennings
Neural Information Processing Systems http://nips.cc/
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-Taylor
Neural Information Processing Systems http://nips.cc/
Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
In various experiments, our Bayesian SPNs often improve test likelihoods over greedy SPN learners.
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Achieving the full promise of the Thermodynamic V ariational Objective (TVO), a recently proposed variational lower bound on the log evidence involving a one-dimensional Riemann integral approximation, requires choosing a "schedule" of sorted discretization points. This paper introduces a bespoke Gaussian process bandit optimization method for automatically choosing these points. Our approach not only automates their one-time selection, but also dynamically adapts their positions over the course of optimization, leading to improved model learning and inference. We provide theoretical guarantees that our bandit optimization converges to the regret-minimizing choice of integration points. Empirical validation of our algorithm is provided in terms of improved learning and inference in V ariational Autoencoders and Sigmoid Belief Networks.
Learning Reward Machines for Partially Observable Reinforcement Learning
The use of neural networks for function approximation has led to many recent advances in Reinforcement Learning (RL) .
Learning Sample-Specific Models with Low-Rank Personalized Regression
Ben Lengerich, Bryon Aragam, Eric P. Xing
Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations.
Learning of Discrete Graphical Models with Neural Networks
Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a discrete graphical model given i.i.d samples from its joint distribution can be solved with near-optimal sample complexity using a convex optimization method known as Generalized Regularized Interaction Screening Estimator (GRISE). But the computational cost of GRISE becomes prohibitive when the energy function of the true graphical model has higher order terms. We introduce NeurISE, a neural net based algorithm for graphical model learning, to tackle this limitation of GRISE. We use neural nets as function approximators in an Interaction Screening objective function.
