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Zichu Liu

Doctorat - UdeM
Superviseur⋅e principal⋅e
Gauthier Gidel
Co-supervisor
Ioannis Mitliagkas
Sujets de recherche
Apprentissage par renforcement
Optimisation
Systèmes dynamiques

Publications

Solving Hidden Monotone Variational Inequalities with Surrogate Losses
Ryan D'Orazio
Danilo Vucetic
Zichu Liu
Junhyung Lyle Kim
Ioannis Mitliagkas
Gauthier Gidel
Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minim… (voir plus)izing projected Bellman error and min-max optimization, cannot be modelled as minimizing a scalar loss function but instead correspond to solving a variational inequality (VI) problem. This difference in setting has caused many practical challenges as naive gradient-based approaches from supervised learning tend to diverge and cycle in the VI case. In this work, we propose a principled surrogate-based approach compatible with deep learning to solve VIs. We show that our surrogate-based approach has three main benefits: (1) under assumptions that are realistic in practice (when hidden monotone structure is present, interpolation, and sufficient optimization of the surrogates), it guarantees convergence, (2) it provides a unifying perspective of existing methods, and (3) is amenable to existing deep learning optimizers like ADAM. Experimentally, we demonstrate our surrogate-based approach is effective in min-max optimization and minimizing projected Bellman error. Furthermore, in the deep reinforcement learning case, we propose a novel variant of TD(0) which is more compute and sample efficient.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
Ryan D'Orazio
Danilo Vucetic
Zichu Liu
Junhyung Lyle Kim
Ioannis Mitliagkas
Gauthier Gidel
Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minim… (voir plus)izing projected Bellman error and min-max optimization, cannot be modelled as minimizing a scalar loss function but instead correspond to solving a variational inequality (VI) problem. This difference in setting has caused many practical challenges as naive gradient-based approaches from supervised learning tend to diverge and cycle in the VI case. In this work, we propose a principled surrogate-based approach compatible with deep learning to solve VIs. We show that our surrogate-based approach has three main benefits: (1) under assumptions that are realistic in practice (when hidden monotone structure is present, interpolation, and sufficient optimization of the surrogates), it guarantees convergence, (2) it provides a unifying perspective of existing methods, and (3) is amenable to existing deep learning optimizers like ADAM. Experimentally, we demonstrate our surrogate-based approach is effective in min-max optimization and minimizing projected Bellman error. Furthermore, in the deep reinforcement learning case, we propose a novel variant of TD(0) which is more compute and sample efficient.
2024-11-07
ArXiv (prépublication)
doi.org
arxiv.org