538 citations · 2.3k across the 54 of their papers we have counts for
8 papers · 1 filter
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Stein's method (Stein, 1973; 1981) is a powerful tool for statistical applications and has significantly impacted machine learning. Stein's lemma plays an essential role in Stein's…
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation
Si Yi Meng, Sharan Vaswani, Issam Laradji +2
We consider stochastic second-order methods for minimizing smooth and strongly-convex functions under an interpolation condition satisfied by over-parameterized models. Under this…
Where are the Masks: Instance Segmentation with Image-level Supervision
Issam H. Laradji, David Vazquez, Mark Schmidt
A major obstacle in instance segmentation is that existing methods often need many per-pixel labels in order to be effective. These labels require large human effort and for certai…
Instance Segmentation with Point Supervision
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro +2
Instance segmentation methods often require costly per-pixel labels. We propose a method that only requires point-level annotations. During training, the model only has access to a…
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Natural-gradient methods enable fast and simple algorithms for variational inference, but due to computational difficulties, their use is mostly limited to \emph{minimal} exponenti…
Efficient Deep Gaussian Process Models for Variable-Sized Input
Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic +1
Deep Gaussian processes (DGP) have appealing Bayesian properties, can handle variable-sized data, and learn deep features. Their limitation is that they do not scale well with the…
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Sharan Vaswani, Aaron Mishkin, Issam Laradji +3
Recent works have shown that stochastic gradient descent (SGD) achieves the fast convergence rates of full-batch gradient descent for over-parameterized models satisfying certain i…
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets
Mehrdad Ghadiri, Mark Schmidt
There are many problems in machine learning and data mining which are equivalent to selecting a non-redundant, high "quality" set of objects. Recommender systems, feature selection…