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Found 32 papers, 0 papers with code
A Moreau Envelope Approach for LQR Meta-Policy Estimation
We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear time-invariant uncertain dynamical systems.
Decentralized and Equitable Optimal Transport
Moreover, we propose the decentralized equitable optimal transport (DE-OT) problem.
PIDformer: Transformer Meets Control Theory
Motivated by this control framework, we derive a novel class of transformers, PID-controlled Transformer (PIDformer), aimed at improving robustness and mitigating the rank-collapse issue inherent in softmax transformers.
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations
In this work, we address the challenge of multi-task image generation with limited data for denoising diffusion probabilistic models (DDPM), a class of generative models that produce high-quality images by reversing a noisy diffusion process.
Frequentist Guarantees of Distributed (Non)-Bayesian Inference
Motivated by the need to analyze large, decentralized datasets, distributed Bayesian inference has become a critical research area across multiple fields, including statistics, electrical engineering, and economics.
Adaptive Federated Learning with Auto-Tuned Clients
Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data.
On First-Order Meta-Reinforcement Learning with Moreau Envelopes
We provide a detailed analysis of the MEMRL algorithm, where we show a sublinear convergence rate to a first-order stationary point for non-convex policy gradient optimization.
An energy management system model with power quality constraints for unbalanced multi-microgrids interacting in a local energy market
This paper proposes a convex optimization model of an energy management system with operational and power quality constraints and interactions in a Local Energy Market (LEM) for unbalanced microgrids (MGs).
On the Performance of Gradient Tracking with Local Updates
We study the decentralized optimization problem where a network of $n$ agents seeks to minimize the average of a set of heterogeneous non-convex cost functions distributedly.
A State Feedback Controller for Mitigation of Continuous-Time Networked SIS Epidemics
The upshot of devising such a strategy is that it allows health administration officials to ensure that there is sufficient capacity in the healthcare system to treat the most severe cases.
Unbounded Gradients in Federated Leaning with Buffered Asynchronous Aggregation
Synchronous updates may compromise the efficiency of cross-device federated learning once the number of active clients increases.
PersA-FL: Personalized Asynchronous Federated Learning
Our main technical contribution is a unified proof for asynchronous federated learning with bounded staleness that we apply to MAML and ME personalization frameworks.
Consensus ADMM-Based Distributed Simultaneous Imaging & Communication
This paper takes the first steps toward enabling wireless networks to perform both imaging and communication in a distributed manner.
On Arbitrary Compression for Decentralized Consensus and Stochastic Optimization over Directed Networks
We study the decentralized consensus and stochastic optimization problems with compressed communications over static directed graphs.
On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression
We study the acceleration of the Local Polynomial Interpolation-based Gradient Descent method (LPI-GD) recently proposed for the approximate solution of empirical risk minimization problems (ERM).
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix over a set of local machines.
The Role of Local Steps in Local SGD
Our main contribution is to characterize the convergence rate of Local SGD as a function of $\{H_i\}_{i=1}^R$ under various settings of strongly convex, convex, and nonconvex local functions, where $R$ is the total number of communication rounds.
Faster Convergence of Local SGD for Over-Parameterized Models
For general convex loss functions, we establish an error bound of $\O(1/T)$ under a mild data similarity assumption and an error bound of $\O(K/T)$ otherwise, where $K$ is the number of local steps.
Scalable Average Consensus with Compressed Communications
We propose a new decentralized average consensus algorithm with compressed communication that scales linearly with the network size n. We prove that the proposed method converges to the average of the initial values held locally by the agents of a network when agents are allowed to communicate with compressed messages.
Hyperfast Second-Order Local Solvers for Efficient Statistically Preconditioned Distributed Optimization
Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems.
Distributed Optimization
Optimization and Control
Communication-efficient Distributed Cooperative Learning with Compressed Beliefs
We study the problem of distributed cooperative learning, where a group of agents seeks to agree on a set of hypotheses that best describes a sequence of private observations.
Communication-efficient Decentralized Local SGD over Undirected Networks
Agents have access to $F$ through noisy gradients, and they can locally communicate with their neighbors a network.
Robust Asynchronous and Network-Independent Cooperative Learning
We consider the model of cooperative learning via distributed non-Bayesian learning, where a network of agents tries to jointly agree on a hypothesis that best described a sequence of locally available observations.
A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks
We propose a distributed, cubic-regularized Newton method for large-scale convex optimization over networks.
Generalized Self-concordant Hessian-barrier algorithms
Many problems in statistical learning, imaging, and computer vision involve the optimization of a non-convex objective function with singularities at the boundary of the feasible set.
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks
Then, we study distributed optimization algorithms for non-dual friendly functions, as well as a method to improve the dependency on the parameters of the functions involved.
Distributed Computation of Wasserstein Barycenters over Networks
We propose a new \cu{class-optimal} algorithm for the distributed computation of Wasserstein Barycenters over networks.
Optimal Algorithms for Distributed Optimization
In this paper, we study the optimal convergence rate for distributed convex optimization problems in networks.
Distributed Learning for Cooperative Inference
We study the problem of cooperative inference where a group of agents interact over a network and seek to estimate a joint parameter that best explains a set of observations.
Distributed Gaussian Learning over Time-varying Directed Graphs
We show a convergence rate of $O(1/k)$ with the constant term depending on the number of agents and the topology of the network.
A Tutorial on Distributed (Non-Bayesian) Learning: Problem, Algorithms and Results
We overview some results on distributed learning with focus on a family of recently proposed algorithms known as non-Bayesian social learning.
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs.
