3k citations · 24.2k across the 299 of their papers we have counts for
9 papers · 2 filters
Distributed Optimization with Arbitrary Local Solvers
Chenxin Ma, Jakub Konečný, Martin Jaggi +4
With the growth of data and necessity for distributed optimization methods, solvers that work well on a single machine must be re-designed to leverage distributed computation. Rece…
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
Virginia Smith, Simone Forte, Michael I. Jordan +1
Despite the importance of sparsity in many large-scale applications, there are few methods for distributed optimization of sparsity-inducing objectives. In this paper, we present a…
Learning Halfspaces and Neural Networks with Random Initialization
Yuchen Zhang, Jason D. Lee, Martin J. Wainwright +1
We study non-convex empirical risk minimization for learning halfspaces and neural networks. For loss functions that are -Lipschitz continuous, we present algorithms to learn ha…
-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang, Jason D. Lee, Michael I. Jordan
We study the improper learning of multi-layer neural networks. Suppose that the neural network to be learned has hidden layers and that the -norm of the incoming weight…
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
Yuchen Zhang, Michael I. Jordan
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing…
High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman, Philipp Moritz, Sergey Levine +2
Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear fu…
Trust Region Policy Optimization
John Schulman, Sergey Levine, Philipp Moritz +2
We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, we dev…
Adding vs. Averaging in Distributed Primal-Dual Optimization
Chenxin Ma, Virginia Smith, Martin Jaggi +3
Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accu…
Learning Transferable Features with Deep Adaptation Networks
Mingsheng Long, Yue Cao, Jianmin Wang +1
Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually tr…