
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
While uncertainty estimation is a wellstudied topic in deep learning, m...
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DeltaSTN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
Hyperparameter optimization of neural networks can be elegantly formulat...
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A Unified Analysis of FirstOrder Methods for Smooth Games via Integral Quadratic Constraints
The theory of integral quadratic constraints (IQCs) allows the certifica...
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Evaluating Lossy Compression Rates of Deep Generative Models
The field of deep generative modeling has succeeded in producing astonis...
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Regularized linear autoencoders recover the principal components, eventually
Our understanding of learning inputoutput relationships with neural net...
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The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
In this work, we focus on an analogical reasoning task that contains ric...
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Learning Branching Heuristics for Propositional Model Counting
Propositional model counting or #SAT is the problem of computing the num...
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INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
In learningassisted theorem proving, one of the most critical challenge...
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When Does Preconditioning Help or Hurt Generalization?
While second order optimizers such as natural gradient descent (NGD) oft...
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Understanding and mitigating exploding inverses in invertible neural networks
Invertible neural networks (INNs) have been used to design generative mo...
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Picking Winning Tickets Before Training by Preserving Gradient Flow
Overparameterization has been shown to benefit both the optimization and...
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Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
Posterior collapse in Variational Autoencoders (VAEs) arises when the va...
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Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Lipschitz constraints under L2 norm on deep neural networks are useful f...
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Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Increasing the batch size is a popular way to speed up neural network tr...
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Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
Natural gradient descent has proven effective at mitigating the effects ...
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EigenDamage: Structured Pruning in the KroneckerFactored Eigenbasis
Reducing the test time resource requirements of a neural network while p...
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Functional Variational Bayesian Neural Networks
Variational Bayesian neural networks (BNNs) perform variational inferenc...
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SelfTuning Networks: Bilevel Optimization of Hyperparameters using Structured BestResponse Functions
Hyperparameter optimization can be formulated as a bilevel optimization ...
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Eigenvalue Corrected Noisy Natural Gradient
Variational Bayesian neural networks combine the flexibility of deep lea...
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Sorting out Lipschitz function approximation
Training neural networks subject to a Lipschitz constraint is useful for...
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Three Mechanisms of Weight Decay Regularization
Weight decay is one of the standard tricks in the neural network toolbox...
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Reversible Recurrent Neural Networks
Recurrent neural networks (RNNs) provide stateoftheart performance in...
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A CoordinateFree Construction of Scalable Natural Gradient
Most neural networks are trained using firstorder optimization methods,...
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Adversarial Distillation of Bayesian Neural Network Posteriors
Bayesian neural networks (BNNs) allow us to reason about uncertainty in ...
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Differentiable Compositional Kernel Learning for Gaussian Processes
The generalization properties of Gaussian processes depend heavily on th...
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Aggregated Momentum: Stability Through Passive Damping
Momentum is a simple and widely used trick which allows gradientbased o...
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Flipout: Efficient PseudoIndependent Weight Perturbations on MiniBatches
Stochastic neural net weights are used in a variety of contexts, includi...
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Understanding ShortHorizon Bias in Stochastic MetaOptimization
Careful tuning of the learning rate, or even schedules thereof, can be c...
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Isolating Sources of Disentanglement in Variational Autoencoders
We decompose the evidence lower bound to show the existence of a term me...
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Noisy Natural Gradient as Variational Inference
Combining the flexibility of deep learning with Bayesian uncertainty est...
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Scalable trustregion method for deep reinforcement learning using Kroneckerfactored approximation
In this work, we propose to apply trust region optimization to deep rein...
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On the Quantitative Analysis of DecoderBased Generative Models
The past several years have seen remarkable progress in generative model...
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A Kroneckerfactored approximate Fisher matrix for convolution layers
Secondorder optimization methods such as natural gradient descent have ...
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Learning WakeSleep Recurrent Attention Models
Despite their success, convolutional neural networks are computationally...
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Statistical Inference, Learning and Models in Big Data
The need for new methods to deal with big data is a common theme in most...
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Importance Weighted Autoencoders
The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently ...
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Optimizing Neural Networks with Kroneckerfactored Approximate Curvature
We propose an efficient method for approximating natural gradient descen...
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Automatic Construction and NaturalLanguage Description of Nonparametric Regression Models
This paper presents the beginnings of an automatic statistician, focusin...
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Structure Discovery in Nonparametric Regression through Compositional Kernel Search
Despite its importance, choosing the structural form of the kernel in no...
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Exploiting compositionality to explore a large space of model structures
The recent proliferation of richly structured probabilistic models raise...
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ShiftInvariance Sparse Coding for Audio Classification
Sparse coding is an unsupervised learning algorithm that learns a succin...
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Roger Grosse
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Assistant Professor of Computer Science at the University of Toronto, focusing on machine learning.