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07/06/2023
Quantum Solutions to the Privacy vs. Utility Tradeoff
In this work, we propose a novel architecture (and several variants ther...
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04/28/2023
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
A stochastic-gradient-based interior-point algorithm for minimizing a co...
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03/08/2023
A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond
Monte Carlo sampling is a powerful toolbox of algorithmic techniques wid...
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02/01/2023
Riemannian Stochastic Approximation for Minimizing Tame Nonsmooth Objective Functions
In many learning applications, the parameters in a model are structurall...
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11/02/2022
Jump-Diffusion Langevin Dynamics for Multimodal Posterior Sampling
Bayesian methods of sampling from a posterior distribution are becoming ...
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10/15/2022
Time-Varying Semidefinite Programming: Path Following a Burer–Monteiro Factorization
We present an online algorithm for time-varying semidefinite programs (T...
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10/13/2022
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence
The stochastic heavy ball method (SHB), also known as stochastic gradien...
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06/23/2022
Stochastic Langevin Differential Inclusions with Applications to Machine Learning
Stochastic differential equations of Langevin-diffusion form have receiv...
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03/13/2022
Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD
Powered by the simplicity of lock-free asynchrony, Hogwilld! is a go-to ...
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11/19/2021
Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice
Optimization problems with set submodular objective functions have many ...
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11/03/2021
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
Understanding the properties of neural networks trained via stochastic g...
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07/15/2021
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo
Federated learning performed by a decentralized networks of agents is be...
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05/19/2021
Trilevel and Multilevel Optimization using Monotone Operator Theory
We consider rather a general class of multi-level optimization problems,...
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03/06/2021
Decentralized Langevin Dynamics over a Directed Graph
The prevalence of technologies in the space of the Internet of Things an...
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06/12/2020
Stochastic Gradient Langevin with Delayed Gradients
Stochastic Gradient Langevin Dynamics (SGLD) ensures strong guarantees w...
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01/16/2020
Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent
Machine learning has made tremendous progress in recent years, with mode...
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01/02/2020
Stochastic Gradient Langevin Dynamics on a Distributed Network
Langevin MCMC gradient optimization is a class of increasingly popular m...
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03/07/2019
Lifted Weight Learning of Markov Logic Networks Revisited
We study lifted weight learning of Markov logic networks. We show that t...
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09/23/2018
Second-order Guarantees of Distributed Gradient Algorithms
We consider distributed smooth nonconvex unconstrained optimization over...
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06/30/2018
Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems
Machine Learning models incorporating multiple layered learning networks...
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06/30/2018