
The Case for Bayesian Deep Learning
The key distinguishing property of a Bayesian approach is marginalizatio...
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Bayesian Deep Learning and a Probabilistic Perspective of Generalization
The key distinguishing property of a Bayesian approach is marginalizatio...
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Exact Gaussian Processes on a Million Data Points
Gaussian processes (GPs) are flexible models with stateoftheart perfo...
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BoTorch: Programmable Bayesian Optimization in PyTorch
Bayesian optimization provides sampleefficient global optimization for ...
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Randomly Projected Additive Gaussian Processes for Regression
Gaussian processes (GPs) provide flexible distributions over functions, ...
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A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWAGaussian (SWAG), a simple, scalable, and general purpose ...
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Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Neural networks appear to have mysterious generalization properties when...
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SemiSupervised Learning with Normalizing Flows
Normalizing flows transform a latent distribution through an invertible ...
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Practical Multifidelity Bayesian Optimization for Hyperparameter Tuning
Bayesian optimization is popular for optimizing timeconsuming blackbox...
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Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
The translation equivariance of convolutional layers enables convolution...
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SWALP : Stochastic Weight Averaging in LowPrecision Training
Low precision operations can provide scalability, memory savings, portab...
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FunctionSpace Distributions over Kernels
Gaussian processes are flexible function approximators, with inductive b...
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Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
Identifying changes in model parameters is fundamental in machine learni...
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Simple Blackbox Adversarial Attacks
We propose an intriguingly simple method for the construction of adversa...
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Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many application...
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Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
The posteriors over neural network weights are high dimensional and mult...
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GPyTorch: Blackbox MatrixMatrix Gaussian Process Inference with GPU Acceleration
Despite advances in scalable models, the inference tools used for Gaussi...
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Subspace Inference for Bayesian Deep Learning
Bayesian inference was once a gold standard for learning with neural net...
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Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine ...
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Scalable Log Determinants for Gaussian Process Kernel Learning
For applications as varied as Bayesian neural networks, determinantal po...
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Bayesian Optimization with Gradients
Bayesian optimization has been successful at global optimization of expe...
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Bayesian GAN
Generative adversarial networks (GANs) can implicitly learn rich distrib...
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Stochastic Variational Deep Kernel Learning
Deep kernel learning combines the nonparametric flexibility of kernel m...
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Learning Scalable Deep Kernels with Recurrent Structure
Many applications in speech, robotics, finance, and biology deal with se...
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Deep Kernel Learning
We introduce scalable deep kernels, which combine the structural propert...
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Thoughts on Massively Scalable Gaussian Processes
We introduce a framework and early results for massively scalable Gaussi...
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The Human Kernel
Bayesian nonparametric models, such as Gaussian processes, provide a com...
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Kernel Interpolation for Scalable Structured Gaussian Processes (KISSGP)
We introduce a new structured kernel interpolation (SKI) framework, whic...
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A la Carte  Learning Fast Kernels
Kernel methods have great promise for learning rich statistical represen...
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Studentt Processes as Alternatives to Gaussian Processes
We investigate the Studentt process as an alternative to the Gaussian p...
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Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification
Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic prop...
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Gaussian Process Kernels for Pattern Discovery and Extrapolation
Gaussian processes are rich distributions over functions, which provide ...
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Gaussian Process Regression Networks
We introduce a new regression framework, Gaussian process regression net...
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Generalised Wishart Processes
We introduce a stochastic process with Wishart marginals: the generalise...
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Multimodal Word Distributions
Word embeddings provide point representations of words containing useful...
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Scalable Lévy Process Priors for Spectral Kernel Learning
Gaussian processes are rich distributions over functions, with generaliz...
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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
The loss functions of deep neural networks are complex and their geometr...
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Product Kernel Interpolation for Scalable Gaussian Processes
Recent work shows that inference for Gaussian processes can be performed...
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ConstantTime Predictive Distributions for Gaussian Processes
One of the most compelling features of Gaussian process (GP) regression ...
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Averaging Weights Leads to Wider Optima and Better Generalization
Deep neural networks are typically trained by optimizing a loss function...
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Gaussian Process Subset Scanning for Anomalous Pattern Detection in Noniid Data
Identifying anomalous patterns in realworld data is essential for under...
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Hierarchical Density Order Embeddings
By representing words with probability densities rather than point vecto...
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Improving ConsistencyBased SemiSupervised Learning with Weight Averaging
Recent advances in deep unsupervised learning have renewed interest in s...
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Probabilistic FastText for MultiSense Word Embeddings
We introduce Probabilistic FastText, a new model for word embeddings tha...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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