805 citations · 1.9k across the 55 of their papers we have counts for
7 papers · 1 filter
Discriminative Clustering with Representation Learning with any Ratio of Labeled to Unlabeled Data
Corinne Jones, Vincent Roulet, Zaid Harchaoui
We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
On the Convergence of the Iterative Linear Exponential Quadratic Gaussian Algorithm to Stationary Points
Vincent Roulet, Maryam Fazel, Siddhartha Srinivasa +1
A classical method for risk-sensitive nonlinear control is the iterative linear exponential quadratic Gaussian algorithm. We present its convergence analysis from a first-order opt…
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy +1
We examine popular gradient-based algorithms for nonlinear control in the light of the modern complexity analysis of first-order optimization algorithms. The examination reveals th…
A Statistical Investigation of Long Memory in Language and Music
Alexander Greaves-Tunnell, Zaid Harchaoui
Representation and learning of long-range dependencies is a central challenge confronted in modern applications of machine learning to sequence data. Yet despite the prominence of…
Kernel-based Translations of Convolutional Networks
Corinne Jones, Vincent Roulet, Zaid Harchaoui
Convolutional Neural Networks, as most artificial neural networks, are commonly viewed as methods different in essence from kernel-based methods. We provide a systematic translatio…
A Smoother Way to Train Structured Prediction Models
Krishna Pillutla, Vincent Roulet, Sham M. Kakade +1
We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon. Smoothing overcomes the non-smoothness inherent to…