Search Results for author:
Found 41 papers, 4 papers with code
Vehicle-to-Vehicle Charging: Model, Complexity, and Heuristics
The rapid adoption of Electric Vehicles (EVs) poses challenges for electricity grids to accommodate or mitigate peak demand.
Sample-Optimal Zero-Violation Safety For Continuous Control
In this paper, we study the problem of ensuring safety with a few shots of samples for partially unknown systems.
A Unified Framework for Gradient-based Clustering of Distributed Data
The proposed family, termed Distributed Gradient Clustering (DGC-$\mathcal{F}_\rho$), is parametrized by $\rho \geq 1$, controling the proximity of users' center estimates, with $\mathcal{F}$ determining the clustering loss.
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise.
Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights
We present a stochastic first-order optimization method specialized for deep neural networks (DNNs), ECCO-DNN.
Model-Free Learning and Optimal Policy Design in Multi-Agent MDPs Under Probabilistic Agent Dropout
The controller's objective is to find an optimal policy that maximizes the value of the expected system given a priori knowledge of the agents' dropout probabilities.
Corrected: On Confident Policy Evaluation for Factored Markov Decision Processes with Node Dropouts
In this work we investigate an importance sampling approach for evaluating policies for a structurally time-varying factored Markov decision process (MDP), i. e. the policy's value is estimated with a high-probability confidence interval.
ECCO: Equivalent Circuit Controlled Optimization
To find the value of the critical point, we propose a time step search routine for Forward Euler discretization that controls the local truncation error, a method adapted from circuit simulation ideas.
Large deviations rates for stochastic gradient descent with strongly convex functions
In this work we provide a formal framework for the study of general high probability bounds with SGD, based on the theory of large deviations.
Networked Signal and Information Processing
Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments
In the proposed setup, the grouping of users (based on the data distributions they sample), as well as the underlying statistical properties of the distributions, are apriori unknown.
Cluster-Based Control of Transition-Independent MDPs
To efficiently find a policy in this rapidly scaling space, we propose a clustered Bellman operator that optimizes over the action space for one cluster at any evaluation.
Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time
Linear approximations of the AC power flow equations are of great significance for the computational efficiency of large-scale optimal power flow (OPF) problems.
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise
We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails.
A Fully Decentralized Tuning-free Inexact Projection Method for P2P Energy Trading
Agent-based solutions lend themselves well to address privacy concerns and the computational scalability needs of future distributed electric grids and end-use energy exchanges.
Variance reduced stochastic optimization over directed graphs with row and column stochastic weights
This paper proposes AB-SAGA, a first-order distributed stochastic optimization method to minimize a finite-sum of smooth and strongly convex functions distributed over an arbitrary directed graph.
Personalized Federated Learning via Convex Clustering
The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized via a sum-of-norms penalty, weighted by a penalty parameter $\lambda$.
Gradient Based Clustering
We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluster center positions.
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
This paper considers decentralized stochastic optimization over a network of $n$ nodes, where each node possesses a smooth non-convex local cost function and the goal of the networked nodes is to find an $\epsilon$-accurate first-order stationary point of the sum of the local costs.
Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings
In this paper, we show efficient strategies that circumvent this problem by differentiating through the operations of a power flow solver that embeds the power flow equations into a holomorphic function.
A fast randomized incremental gradient method for decentralized non-convex optimization
For general smooth non-convex problems, we show the almost sure and mean-squared convergence of GT-SAGA to a first-order stationary point and further describe regimes of practical significance where it outperforms the existing approaches and achieves a network topology-independent iteration complexity respectively.
Fast decentralized non-convex finite-sum optimization with recursive variance reduction
We show that GT-SARAH, with appropriate algorithmic parameters, finds an $\epsilon$-accurate first-order stationary point with $O\big(\max\big\{N^{\frac{1}{2}}, n(1-\lambda)^{-2}, n^{\frac{2}{3}}m^{\frac{1}{3}}(1-\lambda)^{-1}\big\}L\epsilon^{-2}\big)$ gradient complexity, where ${(1-\lambda)\in(0, 1]}$ is the spectral gap of the network weight matrix and $L$ is the smoothness parameter of the cost functions.
Push-SAGA: A decentralized stochastic algorithm with variance reduction over directed graphs
In this paper, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes.
An improved convergence analysis for decentralized online stochastic non-convex optimization
In this paper, we study decentralized online stochastic non-convex optimization over a network of nodes.
S-ADDOPT: Decentralized stochastic first-order optimization over directed graphs
In this report, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed network of nodes.
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
We discuss local minima convergence guarantees and explore the simple but critical role of the stable-manifold theorem in analyzing saddle-point avoidance.
Optimization and Control
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
In centralized settings, it is well known that stochastic gradient descent (SGD) avoids saddle points and converges to local minima in nonconvex problems.
Optimization and Control
Gradient tracking and variance reduction for decentralized optimization and machine learning
Decentralized methods to solve finite-sum minimization problems are important in many signal processing and machine learning tasks where the data is distributed over a network of nodes and raw data sharing is not permitted due to privacy and/or resource constraints.
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking -- Part II: GT-SVRG
Decentralized stochastic optimization has recently benefited from gradient tracking methods \cite{DSGT_Pu, DSGT_Xin} providing efficient solutions for large-scale empirical risk minimization problems.
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking
In this paper, we study decentralized empirical risk minimization problems, where the goal to minimize a finite-sum of smooth and strongly-convex functions available over a network of nodes.
Optimization and Control
An introduction to decentralized stochastic optimization with gradient tracking
Decentralized solutions to finite-sum minimization are of significant importance in many signal processing, control, and machine learning applications.
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
This paper studies the problem of error-runtime trade-off, typically encountered in decentralized training based on stochastic gradient descent (SGD) using a given network.
Distributed stochastic optimization with gradient tracking over strongly-connected networks
In this paper, we study distributed stochastic optimization to minimize a sum of smooth and strongly-convex local cost functions over a network of agents, communicating over a strongly-connected graph.
Annealing for Distributed Global Optimization
The paper considers algorithms for optimizing the sum function.
Clustering with Distributed Data
By appropriate choice of $\rho$, the set of generalized minima may be brought arbitrarily close to the set of Lloyd's minima.
Towards Gradient Free and Projection Free Stochastic Optimization
This paper focuses on the problem of \emph{constrained} \emph{stochastic} optimization.
Coded Distributed Computing for Inverse Problems
Our experiments for personalized PageRank performed on real systems and real social networks show that this ratio can be as large as $10^4$.
Topology Adaptive Graph Convolutional Networks
Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss.
Convergence analysis of belief propagation for pairwise linear Gaussian models
Gaussian belief propagation (BP) has been widely used for distributed inference in large-scale networks such as the smart grid, sensor networks, and social networks, where local measurements/observations are scattered over a wide geographical area.
Convergence analysis of the information matrix in Gaussian belief propagation
Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local measurements/observations are scattered over a wide geographical area.
Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation
A necessary and sufficient convergence condition for the belief mean vector to converge to the optimal centralized estimator is provided under the assumption that the message information matrix is initialized as a positive semidefinite matrix.
