
Randomized Automatic Differentiation
The successes of deep learning, variational inference, and many other fi...
read it

SketchGraphs: A LargeScale Dataset for Modeling Relational Geometry in ComputerAided Design
Parametric computeraided design (CAD) is the dominant paradigm in mecha...
read it

Learning Composable Energy Surrogates for PDE Order Reduction
Metamaterials are an important emerging class of engineered materials i...
read it

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Standard variational lower bounds used to train latent variable models p...
read it

On the Difficulty of WarmStarting Neural Network Training
In many realworld deployments of machine learning systems, data arrive ...
read it

Discrete Object Generation with Reversible Inductive Construction
The success of generative modeling in continuous domains has led to a su...
read it

A Theoretical Connection Between Statistical Physics and Reinforcement Learning
Sequential decision making in the presence of uncertainty and stochastic...
read it

SpArSe: Sparse Architecture Search for CNNs on ResourceConstrained Microcontrollers
The vast majority of processors in the world are actually microcontrolle...
read it

Efficient Optimization of Loops and Limits with Randomized Telescoping Sums
We consider optimization problems in which the objective requires an inn...
read it

Predicting ElectronIonization Mass Spectrometry using Neural Networks
When confronted with a substance of unknown identity, researchers often ...
read it

Motivating the Rules of the Game for Adversarial Example Research
Advances in machine learning have led to broad deployment of systems wit...
read it

Compressibility and Generalization in LargeScale Deep Learning
Modern neural networks are highly overparameterized, with capacity to su...
read it

Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical c...
read it

Estimating the Spectral Density of Large Implicit Matrices
Many important problems are characterized by the eigenvalues of a large ...
read it

PASSGLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Generalized linear models (GLMs)  such as logistic regression, Poisson...
read it

Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics an...
read it

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professionalgrade software applications are powerful but complicatedex...
read it

Variational Boosting: Iteratively Refining Posterior Approximations
We propose a blackbox variational inference method to approximate intra...
read it

Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball ...
read it

Bayesian latent structure discovery from multineuron recordings
Neural circuits contain heterogeneous groups of neurons that differ in t...
read it

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
A good clustering can help a data analyst to explore and understand a da...
read it

Composing graphical models with neural networks for structured representations and fast inference
We propose a general modeling and inference framework that composes prob...
read it

Patterns of Scalable Bayesian Inference
Datasets are growing not just in size but in complexity, creating a dema...
read it

A General Framework for Constrained Bayesian Optimization using Informationbased Search
We present an informationtheoretic framework for solving global blackb...
read it

Predictive Entropy Search for Multiobjective Bayesian Optimization
We present PESMO, a Bayesian method for identifying the Pareto set of mu...
read it

Sandwiching the marginal likelihood using bidirectional Monte Carlo
Computing the marginal likelihood (ML) of a model requires marginalizing...
read it

Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on gr...
read it

Dependent Multinomial Models Made Easy: Stick Breaking with the PólyaGamma Augmentation
Many practical modeling problems involve discrete data that are best rep...
read it

Spectral Representations for Convolutional Neural Networks
Discrete Fourier transforms provide a significant speedup in the computa...
read it

Early Stopping is Nonparametric Variational Inference
We show that unconverged stochastic gradient descent can be interpreted ...
read it

Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
read it

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Large multilayer neural networks trained with backpropagation have recen...
read it

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive blackbox optimizat...
read it

Gradientbased Hyperparameter Optimization through Reversible Learning
Tuning hyperparameters of learning algorithms is hard because gradients ...
read it

A framework for studying synaptic plasticity with neural spike train data
Learning and memory in the brain are implemented by complex, timevaryin...
read it

Accelerating MCMC via Parallel Predictive Prefetching
We present a general framework for accelerating a large class of widely ...
read it

Firefly Monte Carlo: Exact MCMC with Subsets of Data
Markov chain Monte Carlo (MCMC) is a popular and successful generalpurp...
read it

Bayesian Optimization with Unknown Constraints
Recent work on Bayesian optimization has shown its effectiveness in glob...
read it

Avoiding pathologies in very deep networks
Choosing appropriate architectures and regularization strategies for dee...
read it

Learning the Parameters of Determinantal Point Process Kernels
Determinantal point processes (DPPs) are wellsuited for modeling repuls...
read it

Input Warping for Bayesian Optimization of Nonstationary Functions
Bayesian optimization has proven to be a highly effective methodology fo...
read it

Learning Ordered Representations with Nested Dropout
In this paper, we study ordered representations of data in which differe...
read it

Discovering Latent Network Structure in Point Process Data
Networks play a central role in modern data analysis, enabling us to rea...
read it

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures
The Dirichlet process (DP) is a fundamental mathematical tool for Bayesi...
read it

Parallel MCMC with Generalized Elliptical Slice Sampling
Probabilistic models are conceptually powerful tools for finding structu...
read it

Practical Bayesian Optimization of Machine Learning Algorithms
Machine learning algorithms frequently require careful tuning of model h...
read it

Training Restricted Boltzmann Machines on Word Observations
The restricted Boltzmann machine (RBM) is a flexible tool for modeling c...
read it