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Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects
Causal inference methods are widely applied in the fields of medicine, p...
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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
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DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
We present the preliminary high-level design and features of DynamicPPL....
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Resource-Efficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embed...
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Bayesian Learning of Sum-Product Networks
Sum-product networks (SPNs) are flexible density estimators and have rec...
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Efficient and Robust Machine Learning for Real-World Systems
While machine learning is traditionally a resource intensive task, embed...
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Probabilistic Meta-Representations Of Neural Networks
Existing Bayesian treatments of neural networks are typically characteri...
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Automatic Bayesian Density Analysis
Making sense of a dataset in an automatic and unsupervised fashion is a ...
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Handling Incomplete Heterogeneous Data using VAEs
Variational autoencoders (VAEs), as well as other generative models, hav...
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Variational Bayesian dropout: pitfalls and fixes
Dropout, a stochastic regularisation technique for training of neural ne...
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Antithetic and Monte Carlo kernel estimators for partial rankings
In the modern age, rankings data is ubiquitous and it is useful for a va...
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Probabilistic Deep Learning using Random Sum-Product Networks
Probabilistic deep learning currently receives an increased interest, as...
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Variational Measure Preserving Flows
Probabilistic modelling is a general and elegant framework to capture th...
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Gaussian Process Behaviour in Wide Deep Neural Networks
Whilst deep neural networks have shown great empirical success, there is...
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The Mirage of Action-Dependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of model-free reinforcem...
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Weakly supervised collective feature learning from curated media
The current state-of-the-art in feature learning relies on the supervise...
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Imitation networks: Few-shot learning of neural networks from scratch
In this paper, we propose imitation networks, a simple but effective met...
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Denotational validation of higher-order Bayesian inference
We present a modular semantic account of Bayesian inference algorithms f...
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Variational Gaussian Dropout is not Bayesian
Gaussian multiplicative noise is commonly used as a stochastic regularis...
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General Latent Feature Modeling for Data Exploration Tasks
This paper introduces a general Bayesian non- parametric latent feature ...
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Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes
We propose a simple method that combines neural networks and Gaussian pr...
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One-Shot Learning in Discriminative Neural Networks
We consider the task of one-shot learning of visual categories. In this ...
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Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
Deep neural networks (DNNs) have excellent representative power and are ...
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Lost Relatives of the Gumbel Trick
The Gumbel trick is a method to sample from a discrete probability distr...
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General Latent Feature Models for Heterogeneous Datasets
Latent feature modeling allows capturing the latent structure responsibl...
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Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Off-policy model-free deep reinforcement learning methods using previous...
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Deep Bayesian Active Learning with Image Data
Even though active learning forms an important pillar of machine learnin...
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Bayesian inference on random simple graphs with power law degree distributions
We present a model for random simple graphs with a degree distribution t...
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GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core c...
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A study of the effect of JPG compression on adversarial images
Neural network image classifiers are known to be vulnerable to adversari...
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Magnetic Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct...
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The Mondrian Kernel
We introduce the Mondrian kernel, a fast random feature approximation to...
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Distributed Flexible Nonlinear Tensor Factorization
Tensor factorization is a powerful tool to analyse multi-way data. Compa...
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A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Recurrent neural networks (RNNs) stand at the forefront of many recent d...
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A General Framework for Constrained Bayesian Optimization using Information-based Search
We present an information-theoretic framework for solving global black-b...
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Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
We develop parallel predictive entropy search (PPES), a novel algorithm ...
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Sandwiching the marginal likelihood using bidirectional Monte Carlo
Computing the marginal likelihood (ML) of a model requires marginalizing...
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Dirichlet Fragmentation Processes
Tree structures are ubiquitous in data across many domains, and many dat...
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Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Drawing a sample from a discrete distribution is one of the building com...
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An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Stochastic variational inference (SVI) is emerging as the most promising...
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MCMC for Variationally Sparse Gaussian Processes
Gaussian process (GP) models form a core part of probabilistic machine l...
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Neural Adaptive Sequential Monte Carlo
Sequential Monte Carlo (SMC), or particle filtering, is a popular class ...
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Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Convolutional neural networks (CNNs) work well on large datasets. But la...
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Dropout as a Bayesian Approximation: Appendix
We show that a neural network with arbitrary depth and non-linearities, ...
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Deep learning tools have gained tremendous attention in applied machine ...
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Training generative neural networks via Maximum Mean Discrepancy optimization
We consider training a deep neural network to generate samples from an u...
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A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models
Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric ...
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On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
The variational framework for learning inducing variables (Titsias, 2009...
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Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Multivariate categorical data occur in many applications of machine lear...
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Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive black-box optimizat...
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