Portrait of Alex Hernandez-Garcia

Alex Hernandez-Garcia

Alumni
Website
Google Scholar

Publications

Multi-Objective GFlowNets
Moksh J. Jain
Sharath Chandra Raparthy
Alex Hernandez-Garcia
Jarrid Rector-Brooks
Yoshua Bengio
Santiago Miret
Emmanuel Bengio
We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learni… (see more)ng such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of some underlying property of interest, making it important to generate diverse candidates to have multiple options for expensive downstream evaluations. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC, which models a family of independent sub-problems defined by a scalarization function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a sequence of sub-problems defined by an acquisition function in an active learning loop. Our experiments on wide variety of synthetic and benchmark tasks demonstrate advantages of the proposed methods in terms of the Pareto performance and importantly, improved candidate diversity, which is the main contribution of this work.
2022-10-23
ArXiv (preprint)
doi.org
arxiv.org
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Siba Moussa
Michael Kilgour
Clara Jans
Alex Hernandez-Garcia
Miroslava Cuperlovic‐culf
Yoshua Bengio
Lena Simine
2022-08-10
ArXiv (preprint)
doi.org
arxiv.org
Biological Sequence Design with GFlowNets
Moksh J. Jain
Emmanuel Bengio
Alex Hernandez-Garcia
Jarrid Rector-Brooks
Bonaventure F. P. Dossou
Chanakya Ajit Ekbote
Jie Fu
Tianyu Zhang
Micheal Kilgour
Dinghuai Zhang
Lena Simine
Payel Das
Yoshua Bengio
2022-06-28
Proceedings of the 39th International Conference on Machine Learning (published)
doi.org
arxiv.org
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Victor Schmidt
Alexandra Luccioni
Mélisande Teng
Tianyu Zhang
Alexia Reynaud
Sunand Raghupathi
Gautier Cosne
Adrien Juraver
Vahe Vardanyan
Alex Hernandez-Garcia
Yoshua Bengio
Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both poli… (see more)cy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the potential consequences of extreme climate events such as flooding in familiar places can help make the abstract impacts of climate change more concrete and encourage action. As part of a larger initiative to build a website that projects extreme climate events onto user-chosen photos, we present our solution to simulate photo-realistic floods on authentic images. To address this complex task in the absence of suitable training data, we propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation. In this paper, we describe the details of our framework, thoroughly evaluate components of our architecture and demonstrate that our model is capable of robustly generating photo-realistic flooding.
2022-01-28
ICLR.cc/2022/Conference (poster)
openreview.net
openreview.net