1.9k citations · 6.5k across the 132 of their papers we have counts for
6 papers · 2 filters
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
Jialei Wang, Jason D. Lee, Mehrdad Mahdavi +2
Sketching techniques have become popular for scaling up machine learning algorithms by reducing the sample size or dimensionality of massive data sets, while still maintaining the…
Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Nathan Srebro
We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assu…
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov +1
We investigate the parameter-space geometry of recurrent neural networks (RNNs), and develop an adaptation of path-SGD optimization method, attuned to this geometry, that can learn…
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
Weiran Wang, Jialei Wang, Dan Garber +1
We study the stochastic optimization of canonical correlation analysis (CCA), whose objective is nonconvex and does not decouple over training samples. Although several stochastic…
Distributed Multi-Task Learning with Shared Representation
Jialei Wang, Mladen Kolar, Nathan Srebro
We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensio…
Reducing Runtime by Recycling Samples
Jialei Wang, Hai Wang, Nathan Srebro
Contrary to the situation with stochastic gradient descent, we argue that when using stochastic methods with variance reduction, such as SDCA, SAG or SVRG, as well as their variant…