33 citations · 113 across the 8 of their papers we have counts for
6 papers · 1 filter
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen +2
Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distributio…
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron
Sharan Vaswani, Francis Bach, Mark Schmidt
Modern machine learning focuses on highly expressive models that are able to fit or interpolate the data completely, resulting in zero training loss. For such models, we show that…
Combining Bayesian Optimization and Lipschitz Optimization
Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt
Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the fun…
A Less Biased Evaluation of Out-of-distribution Sample Detectors
Alireza Shafaei, Mark Schmidt, James J. Little
In the real world, a learning system could receive an input that is unlike anything it has seen during training. Unfortunately, out-of-distribution samples can lead to unpredictabl…
Where are the Blobs: Counting by Localization with Point Supervision
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro +2
Object counting is an important task in computer vision due to its growing demand in applications such as surveillance, traffic monitoring, and counting everyday objects. State-of-…
New Insights into Bootstrapping for Bandits
Sharan Vaswani, Branislav Kveton, Zheng Wen +3
We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and est…