Search Results for author: Johan Suykens

Found 12 papers, 1 papers with code

SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe

no code implementations23 May 2024 Joris Depoortere, Johan Driesen, Johan Suykens, Hussain Syed Kazmi

Deep learning models have gained increasing prominence in recent years in the field of solar pho-tovoltaic (PV) forecasting.

Transfer Learning

A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints

no code implementations25 Apr 2024 Bram De Cooman, Johan Suykens

In this work we try to unify these existing techniques and bridge the gap with classical optimization and control theory, using a generic primal-dual framework for value-based and actor-critic reinforcement learning methods.


Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems

no code implementations20 Oct 2023 Mihaly Novak, Rocco Langone, Carlos Alzate, Johan Suykens

This is altered by the modifications reported in this brief that drastically improve the computational characteristics.

Clustering Computational Efficiency +3

Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions

no code implementations28 Jun 2023 Joran Michiels, Maarten De Vos, Johan Suykens

Additionally we explore how a domain expert can provide feature attributions which are sufficiently correct to improve the model.

Active Learning

Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value

no code implementations19 Jun 2023 Joran Michiels, Maarten De Vos, Johan Suykens

In this paper we examine this question and claim that they represent two different explanations which are valid for different end-users: one that explains the model and one that explains the model combined with the feature dependencies in the data.


The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks

no code implementations28 Sep 2020 Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan Suykens

However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.

Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings

no code implementations9 Mar 2018 Hussain Kazmi, Johan Suykens, Johan Driesen

Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort.

Multi-agent Reinforcement Learning

Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning

no code implementations15 Oct 2014 Emanuele Frandi, Ricardo Nanculef, Johan Suykens

Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a suitable choice in a variety of applications.

BIG-bench Machine Learning

Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization

no code implementations14 Apr 2014 Andreas Argyriou, Marco Signoretto, Johan Suykens

We study a hybrid conditional gradient - smoothing algorithm (HCGS) for solving composite convex optimization problems which contain several terms over a bounded set.

EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines

1 code implementation4 Mar 2014 Marc Claesen, Frank De Smet, Johan Suykens, Bart De Moor

EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.

Ensemble Learning

A Risk Minimization Principle for a Class of Parzen Estimators

no code implementations NeurIPS 2007 Kristiaan Pelckmans, Johan Suykens, Bart D. Moor

This paper explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms.


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