538 citations · 2.3k across the 54 of their papers we have counts for
6 papers · 1 filter
Robust Asymmetric Learning in POMDPs
Andrew Warrington, J. Wilder Lavington, Adam Ścibior +2
Policies for partially observed Markov decision processes can be efficiently learned by imitating policies for the corresponding fully observed Markov decision processes. Unfortuna…
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent
Frederik Kunstner, Raunak Kumar, Mark Schmidt
Expectation maximization (EM) is the default algorithm for fitting probabilistic models with missing or latent variables, yet we lack a full understanding of its non-asymptotic con…
Variance-Reduced Methods for Machine Learning
Robert M. Gower, Mark Schmidt, Francis Bach +1
Stochastic optimization lies at the heart of machine learning, and its cornerstone is stochastic gradient descent (SGD), a method introduced over 60 years ago. The last 8 years hav…
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
Yihan Zhou, Victor S. Portella, Mark Schmidt +1
In online convex optimization (OCO), Lipschitz continuity of the functions is commonly assumed in order to obtain sublinear regret. Moreover, many algorithms have only logarithmic…
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)
Sharan Vaswani, Issam Laradji, Frederik Kunstner +3
Adaptive gradient methods are typically used for training over-parameterized models. To better understand their behaviour, we study a simplistic setting -- smooth, convex losses wi…
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
The Bayesian learning rule is a natural-gradient variational inference method, which not only contains many existing learning algorithms as special cases but also enables the desig…