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09/04/2023
Composite federated learning with heterogeneous data
We propose a novel algorithm for solving the composite Federated Learnin...
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08/02/2023
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives
This paper proposes a locally differentially private federated learning ...
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05/29/2023
Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
We present an efficient algorithm for regularized optimal transport. In ...
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11/01/2022
Over-the-Air Computation for Distributed Systems: Something Old and Something New
Facing the upcoming era of Internet-of-Things and connected intelligence...
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02/17/2022
Delay-adaptive step-sizes for asynchronous learning
In scalable machine learning systems, model training is often paralleliz...
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01/25/2022
On Uniform Boundedness Properties of SGD and its Momentum Variants
A theoretical, and potentially also practical, problem with stochastic g...
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10/22/2021
A Fast and Accurate Splitting Method for Optimal Transport: Analysis and Implementation
We develop a fast and reliable method for solving large-scale optimal tr...
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09/09/2021
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
We introduce novel convergence results for asynchronous iterations which...
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06/05/2021
Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization
Many popular learning-rate schedules for deep neural networks combine a ...
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02/18/2021
On the Convergence of Step Decay Step-Size for Stochastic Optimization
The convergence of stochastic gradient descent is highly dependent on th...
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02/12/2021
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness
Stochastic gradient algorithms are often unstable when applied to functi...
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06/24/2020
Advances in Asynchronous Parallel and Distributed Optimization
Motivated by large-scale optimization problems arising in the context of...
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03/13/2020
Communication Efficient Sparsification for Large Scale Machine Learning
The increasing scale of distributed learning problems necessitates the d...
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02/13/2020
Convergence of a Stochastic Gradient Method with Momentum for Nonsmooth Nonconvex Optimization
Stochastic gradient methods with momentum are widely used in application...
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10/18/2019
Anderson Acceleration of Proximal Gradient Methods
Anderson acceleration is a well-established and simple technique for spe...
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09/23/2019
Efficient Stochastic Programming in Julia
We present StochasticPrograms.jl, a user-friendly and powerful open-sour...
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03/20/2019
Noisy Accelerated Power Method for Eigenproblems with Applications
This paper introduces an efficient algorithm for finding the dominant ge...
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01/24/2019
Curvature-Exploiting Acceleration of Elastic Net Computations
This paper introduces an efficient second-order method for solving the e...
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01/10/2019
Harnessing the Power of Serverless Runtimes for Large-Scale Optimization
The event-driven and elastic nature of serverless runtimes makes them a ...
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10/08/2018
POLO: a POLicy-based Optimization library
We present POLO --- a C++ library for large-scale parallel optimization ...
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09/27/2018
The Convergence of Sparsified Gradient Methods
Distributed training of massive machine learning models, in particular d...
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06/18/2018
Distributed learning with compressed gradients
Asynchronous computation and gradient compression have emerged as two ke...
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06/08/2018
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
Motivated by the success of reinforcement learning (RL) for discrete-tim...
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10/18/2016
Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server
This paper presents an asynchronous incremental aggregated gradient algo...
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05/18/2015
An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization
Mini-batch optimization has proven to be a powerful paradigm for large-s...
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05/24/2011