Expectation–maximization algorithm

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A Variational Inference Approach to Learning Multivariate Wold Processes

Patrick Thiran, Matthias Grossglauser, Negar Kiyavash, Seyed Jalal Etesami, William Trouleau

Temporal point-processes are often used for mathematical modeling of sequences of discrete events with asynchronous timestamps. We focus on a class of temporal point-process models called multivariate Wold processes (MWP). These processes are well suited t ...

MICROTOME PUBLISHING2021

Context-Aware Learning for Generative Models

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Robert Leeb, Serafeim Perdikis

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum- ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Giuseppe Carleo, Julien Sebastian Gacon, Stefan Woerner

The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computat ...

VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF2021

Coordinating strategic aggregators in an active distribution network for providing operational flexibility

Sidi-Rachid Cherkaoui, Mokhtar Bozorg, Mohammad Rayati

The support of distributed energy resources (DERs) for providing operational flexibility can be beneficial from the transmission system operators' (TSOs') viewpoint. Despite all opportunities, using DERs within an active distribution network (ADN) for prov ...

Elsevier2020

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Negar Kiyavash

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as the gradient descent ascent (GDA) are the common practice for solving these ...

2020

Robust Identification of Controlled Hawkes Processes

Thomas Alois Weber, Michael Mark

The identification of Hawkes-like processes can pose significant challenges. Despite substantial amounts of data, standard estimation methods show significant bias or fail to converge. To overcome these issues, we propose an alternative approach based on a ...

2020

A Catalyst Framework for Minimax Optimization

Negar Kiyavash

We introduce a generic two-loop scheme for smooth minimax optimization with strongly-convex-concave objectives. Our approach applies the accelerated proximal point framework (or Catalyst) to the associated dual problem and takes full advantage of existing ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2020

CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI

Martin Vetterli, Paul Hurley, Matthieu Martin Jean-André Simeoni, Adrien Georges Jean Besson

Finite rate of innovation (FRI) is a powerful reconstruction framework enabling the recovery of sparse Dirac streams from uniform low-pass filtered samples. An extension of this framework, called generalised FRI (genFRI), has been recently proposed for han ...

2020

Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

Volkan Cevher, Alp Yurtsever

We propose a class of novel variance-reduced stochastic conditional gradient methods. By adopting the recent stochastic path-integrated differential estimator technique (SPIDER) of Fang et al. (2018) for the classical Frank-Wolfe (FW) method, we introduce ...

2019

Learning Hawkes Processes from a Handful of Events

Patrick Thiran, Matthias Grossglauser, William Trouleau, Farnood Salehi

Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve the problem in the presence of long observation sequences. However, when only ...

2019

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