Sparsified SGD with Memory

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Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the statistical guarantees on the excess risk achieved by early-stopped unconstrain ...

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

The problem of Byzantine resilience in distributed machine learning, a.k.a., Byzantine machine learning, consists in designing distributed algorithms that can train an accurate model despite the presence of Byzantine nodes, i.e., nodes with corrupt data or ...

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. To this end, the agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of infor ...

First-principles calculations of phonons are often based on the adiabatic approximation and on Brillouinzone samplings that might not always be sufficient to capture the subtleties of Kohn anomalies. These shortcomings can be addressed through corrections ...

This paper presents a new Matlab toolbox, aimed at facilitating the use of polynomial optimization for stability analysis of nonlinear systems. In the past decade several decisive contributions made it possible to recast this type of problems as convex opt ...

Reaction optimization is challenging and traditionally delegated to domain experts who iteratively pro-pose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence ...

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

There are various possibilities to realize coil winding designs for an inductive power transfer system. In order to achieve high power transfer efficiency and power density and explore trade-offs between the two, design optimization around the coil link is ...

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

Herringbone grooved journal bearings (HGJBs) are widely used in micro-turbocompressor applications due to their high load-carrying capacity, low friction, and oil-free solution. However, the performance of these bearings is sensitive to manufacturing devia ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

Sparsified SGD with Memory

Chattez avec Graph Search

The statistical complexity of early-stopped mirror descent

A ride time-oriented scheduling algorithm for dial-a-ride problems

Byzantine Machine Learning: A Primer

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Distributed Adaptive Learning Under Communication Constraints

Phonon Self-Energy Corrections: To Screen, or Not to Screen

SOStab: A Matlab toolbox for transient stability analysis

Bayesian Optimization for Chemical Reactions

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Robust optimization of control parameters for WEC arrays using stochastic methods

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Modeling and Design Optimization of Loosely Coupled PCB Spiral Coils in Inductive Power Transfer Systems

Deep Learning Generalization with Limited and Noisy Labels

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Robust Design of Herringbone Grooved Journal Bearings using Multi-Objective Optimization with Artificial Neural Networks

Robust Design of Herringbone Grooved Journal Bearings using Multi-Objective Optimization with Artificial Neural Networks

Robust Collaborative Learning with Linear Gradient Overhead

Universal and adaptive methods for robust stochastic optimization