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Goal-directed Generation of Discrete Structures with Conditional Generative Models
Amina Mollaysa · Brooks Paige · Alexandros Kalousis

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #803

Despite recent advances, goal-directed generation of structured discrete data remains challenging. For problems such as program synthesis (generating source code) and materials design (generating molecules), finding examples which satisfy desired constraints or exhibit desired properties is difficult. In practice, expensive heuristic search or reinforcement learning algorithms are often employed. In this paper, we investigate the use of conditional generative models which directly attack this inverse problem, by modeling the distribution of discrete structures given properties of interest. Unfortunately, the maximum likelihood training of such models often fails with the samples from the generative model inadequately respecting the input properties. To address this, we introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We avoid high-variance score-function estimators that would otherwise be required by sampling from an approximation to the normalized rewards, allowing simple Monte Carlo estimation of model gradients. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value. In both cases, we find improvements over maximum likelihood estimation and other baselines.

Author Information

Amina Mollaysa (University of Geneva,University of Applied Sciences Western Switzerland)
Brooks Paige (UCL)
Alexandros Kalousis (University of Applied Sciences, Western Switzerland)

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