Composite convex minimization involving self-concordant-like cost functions

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In this work, we develop first-order (Hessian-free) and zeroth-order (derivative-free) implementations of the Cubically Regularized Newton Method for solving general non-convex optimization problems. For that, we employ finite difference approximations of ...

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This paper develops a fast algorithm for computing the equilibrium assignment with the perturbed utility route choice (PURC) model. Without compromise, this allows the significant advantages of the PURC model to be used in large-scale applications. We form ...

Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks, whether and when a model leaks information about the correlations between t ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...

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 ...

Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models c ...

We formulate computed-tomography reconstruction as a continuous-domain optimization problem with Hessian total variation (HTV) as the regularizer. HTV is a sparsity-promoting regularizer that favors continuous and piecewise-linear functions with few affine ...

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Orthogonal group synchronization is the problem of estimating n elements Z(1),& mldr;,Z(n) from the rxr orthogonal group given some relative measurements R-ij approximate to Z(i)Z(j)(-1). The least-squares formulation is nonconvex. To avoid its local minim ...

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This code is used for developing the project entitled “Study on conformal antennas, proof of concept prototype for a UAV”, from the aspects of theory, design, and implementation. This code aims to speed up the investigation of an arbitrary phased array ant ...

Composite convex minimization involving self-concordant-like cost functions

Chattez avec Graph Search

First and Zeroth-Order Implementations of the Regularized Newton Method with Lazy Approximated Hessians

PolyCLEAN: Atomic optimization for super-resolution imaging and uncertainty estimation in radio interferometry

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

GLoFool: Global Enhancements and Local Perturbations to Craft Adversarial Images

Lorentzian polynomials and the independence sequences of graphs

Perturbed Utility Stochastic Traffic Assignment

Correlation inference attacks against machine learning models

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

The effect of smooth parametrizations on nonconvex optimization landscapes

Distributed Adaptive Learning Under Communication Constraints

The complexity of quantum support vector machines

A Continuous-Domain Solution for Computed Tomography with Hessian Total-Variation Regularization

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Scalable constrained optimization

On the Generalization of Stochastic Gradient Descent with Momentum

Mesh-based topology, shape and sizing optimization of ribbed plates

On distributional autoregression and iterated transportation

BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Code for project entitled "study on conformal antennas, proof of concept prototype for a UAV"