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Multi-Objective GFlowNets
Moksh Jain · Sharath Chandra Raparthy · Alex Hernandez-Garcia · Jarrid Rector-Brooks · Yoshua Bengio · Santiago Miret · Emmanuel Bengio
Event URL: https://openreview.net/forum?id=aqe70kkpjf »

In many applications of machine learning, like drug discovery and material design, the goal is to generate candidates that simultaneously maximize a set of objectives. As these objectives are often conflicting, there is no single candidate that simultaneously maximizes all objectives, but rather a set of Pareto-optimal candidates where one objective cannot be improved without worsening another. Moreover, these objectives, when considered in practice are often under-specified, making diversity of candidates a key consideration. The existing multi-objective optimization methods focus predominantly on covering the Pareto front, failing to capture diversity in the space of candidates. Motivated by the success of GFlowNets for generation of diverse candidates in a single objective setting, in this paper we consider Multi-Objective GFlowNets (MOGFNs). MOGFNs consist of a Conditional GFlowNet which models a family of single-objective sub-problems derived by decomposing the multi-objective optimization problem. Our work is the first to empirically demonstrate conditional GFlowNets. Through a series of experiments on synthetic as well as practically relevant material design and drug discovery tasks, we empirically demonstrate that MOGFNs outperform existing methods in terms of hypervolume, R2-distance and candidate diversity. We also demonstrate the effectiveness of MOGFNs over existing methods in active learning settings.

Author Information

Moksh Jain (MILA / UdeM)

MSc Student at MILA interested in learning based approaches for global optimization.

Sharath Chandra Raparthy (Meta)
Alex Hernandez-Garcia (Mila - Quebec AI Institute)
Jarrid Rector-Brooks (Montreal Institute for Learning Algorithms, University of Montreal, University of Montreal)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

Santiago Miret (Intel AI Lab)
Emmanuel Bengio (Recursion)

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