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Dinghuai Zhang

dinghuai.zhang@mila.quebec
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e
Aaron Courville
Co-superviseur⋅e
Yoshua Bengio
Site web

Publications

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Dinghuai Zhang
Hanjun Dai
Nikolay Malkin
Aaron Courville
Yoshua Bengio
Ling Pan
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to appl… (voir plus)y machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space. On the other hand, GFlowNets have recently emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially and have the potential to amortize such solution-searching processes in CO, as well as generate diverse solution candidates. In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space. Efficient training techniques are also developed to benefit long-range credit assignment. Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlowNet policies can efficiently find high-quality solutions. Our implementation is open-sourced at https://github.com/zdhNarsil/GFlowNet-CombOpt.
2023-05-26
ArXiv (prépublication)
doi.org
arxiv.org
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan
Nikolay Malkin
Dinghuai Zhang
Yoshua Bengio
2023-04-24
ICML.cc/2023/Conference (poster)
doi.org
openreview.net
Generative Augmented Flow Networks
Ling Pan
Dinghuai Zhang
Aaron Courville
Longbo Huang
Yoshua Bengio
The Generative Flow Network is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the prob… (voir plus)ability of generating an object is proportional to a given reward function. Its effectiveness has been shown in discovering high-quality and diverse solutions, compared to reward-maximizing reinforcement learning-based methods. Nonetheless, GFlowNets only learn from rewards of the terminal states, which can limit its applicability. Indeed, intermediate rewards play a critical role in learning, for example from intrinsic motivation to provide intermediate feedback even in particularly challenging sparse reward tasks. Inspired by this, we propose Generative Augmented Flow Networks (GAFlowNets), a novel learning framework to incorporate intermediate rewards into GFlowNets. We specify intermediate rewards by intrinsic motivation to tackle the exploration problem in sparse reward environments. GAFlowNets can leverage edge-based and state-based intrinsic rewards in a joint way to improve exploration. Based on extensive experiments on the GridWorld task, we demonstrate the effectiveness and efficiency of GAFlowNet in terms of convergence, performance, and diversity of solutions. We further show that GAFlowNet is scalable to a more complex and large-scale molecule generation domain, where it achieves consistent and significant performance improvement.
2023-02-01
ICLR.cc/2023/Conference (notable)
doi.org
openreview.net
GFlowNets and variational inference
Nikolay Malkin
Salem Lahlou
Tristan Deleu
Xu Ji
Edward J Hu
Katie E Everett
Dinghuai Zhang
Yoshua Bengio
This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically us… (voir plus)ed to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demonstrate that, in certain cases, VI algorithms are equivalent to special cases of GFlowNets in the sense of equality of expected gradients of their learning objectives. We then point out the differences between the two families and show how these differences emerge experimentally. Notably, GFlowNets, which borrow ideas from reinforcement learning, are more amenable than VI to off-policy training without the cost of high gradient variance induced by importance sampling. We argue that this property of GFlowNets can provide advantages for capturing diversity in multimodal target distributions.
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Latent State Marginalization as a Low-cost Approach for Improving Exploration
Dinghuai Zhang
Aaron Courville
Yoshua Bengio
Qinqing Zheng
Amy Zhang
Ricky T. Q. Chen
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
P REDICTIVE I NFERENCE WITH F EATURE C ONFORMAL P REDICTION
Jiaye Teng
Chuan Wen
Dinghuai Zhang
Yoshua Bengio
Yang Gao
Yang Yuan
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu
Moksh J. Jain
Bonaventure F. P. Dossou
Qianli Shen
Salem Lahlou
Anirudh Goyal
Nikolay Malkin
Chris Emezue
Dinghuai Zhang
Nadhir Hassen
Xu Ji
Kenji Kawaguchi
Yoshua Bengio
2023-01-01
ICML (publié)
doi.org
openreview.net
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu
Moksh J. Jain
Bonaventure F. P. Dossou
Qianli Shen
Salem Lahlou
Anirudh Goyal
Nikolay Malkin
Chris Emezue
Dinghuai Zhang
Nadhir Hassen
Xu Ji
Kenji Kawaguchi
Yoshua Bengio
2023-01-01
ICML (publié)
doi.org
openreview.net
Stochastic Generative Flow Networks
Ling Pan
Dinghuai Zhang
Moksh J. Jain
Longbo Huang
Yoshua Bengio
2023-01-01
UAI (publié)
doi.org
openreview.net
Stochastic Generative Flow Networks
Ling Pan
Dinghuai Zhang
Moksh J. Jain
Longbo Huang
Yoshua Bengio
Generative Flow Networks (or GFlowNets for short) are a family of probabilistic agents that learn to sample complex combinatorial structures… (voir plus) through the lens of ``inference as control''. They have shown great potential in generating high-quality and diverse candidates from a given energy landscape. However, existing GFlowNets can be applied only to deterministic environments, and fail in more general tasks with stochastic dynamics, which can limit their applicability. To overcome this challenge, this paper introduces Stochastic GFlowNets, a new algorithm that extends GFlowNets to stochastic environments. By decomposing state transitions into two steps, Stochastic GFlowNets isolate environmental stochasticity and learn a dynamics model to capture it. Extensive experimental results demonstrate that Stochastic GFlowNets offer significant advantages over standard GFlowNets as well as MCMC- and RL-based approaches, on a variety of standard benchmarks with stochastic dynamics.
2023-01-01
UAI (publié)
doi.org
openreview.net
A theory of continuous generative flow networks
Salem Lahlou
Tristan Deleu
Pablo Lemos
Dinghuai Zhang
Alexandra Volokhova
Alex Hernandez-Garcia
Lena Nehale Ezzine
Yoshua Bengio
Nikolay Malkin
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (voir plus)ributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.
2023-01-01
ICML (publié)
doi.org
openreview.net
A theory of continuous generative flow networks
Salem Lahlou
Tristan Deleu
Pablo Lemos
Dinghuai Zhang
Alexandra Volokhova
Alex Hernandez-Garcia
Lena Nehale Ezzine
Yoshua Bengio
Nikolay Malkin
2023-01-01
ICML (publié)
doi.org
openreview.net