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Mingzhou Fan

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4 papers

2 author rows

ICML Conference 2024 Conference Paper

GFlowNet Training by Policy Gradients

Puhua Niu
Shili Wu
Mingzhou Fan
Xiaoning Qian

Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties. We here propose a new GFlowNet training framework, with policy-dependent rewards, that bridges keeping flow balance of GFlowNets to optimizing the expected accumulated reward in traditional Reinforcement-Learning (RL). This enables the derivation of new policy-based GFlowNet training methods, in contrast to existing ones resembling value-based RL. It is known that the design of backward policies in GFlowNet training affects efficiency. We further develop a coupled training strategy that jointly solves GFlowNet forward policy training and backward policy design. Performance analysis is provided with a theoretical guarantee of our policy-based GFlowNet training. Experiments on both simulated and real-world datasets verify that our policy-based strategies provide advanced RL perspectives for robust gradient estimation to improve GFlowNet performance. Our code is available at: github. com/niupuhua1234/GFN-PG.

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UAI Conference 2024 Conference Paper

Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity

Mingzhou Fan
Byung-Jun Yoon
Edward R. Dougherty
Nathan M. Urban
Francis J. Alexander
Raymundo Arróyave
Xiaoning Qian

By querying approximate surrogate models of different fidelity as available information sources, Multi-Fidelity Bayesian Optimization (MFBO) aims at optimizing unknown functions that are costly if not infeasible to evaluate. Existing MFBO methods often assume that approximate surrogates have consistently high/low fidelity across the input domain. However, approximate evaluations from the same surrogate can have different fidelity at different input regions due to data availability and model constraints, especially when considering machine learning surrogates. In this work, we investigate MFBO when multi-fidelity approximations have input-dependent fidelity. By explicitly capturing input dependency for multi-fidelity queries in Gaussian Process (GP), our new input-dependent MFBO (iMFBO) with learnable noise models better captures the fidelity of each information source in an intuitive way. We further design a new acquisition function for iMFBO and prove that the queries selected by iMFBO have higher quality than those by naive MFBO methods, with the derived sub-linear regret bound. Experiments on both synthetic and real-world data demonstrate its superior empirical performance.

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ICML Conference 2024 Conference Paper

Path-Guided Particle-based Sampling

Mingzhou Fan
Ruida Zhou
Chao Tian 0002
Xiaoning Qian

Particle-based Bayesian inference methods by sampling from a partition-free target (posterior) distribution, e. g. , Stein variational gradient descent (SVGD), have attracted significant attention. We propose a path-guided particle-based sampling (PGPS) method based on a novel Log-weighted Shrinkage (LwS) density path linking an initial distribution to the target distribution. We propose to utilize a Neural network to learn a vector field motivated by the Fokker-Planck equation of the designed density path. Particles, initiated from the initial distribution, evolve according to the ordinary differential equation defined by the vector field. The distribution of these particles is guided along a density path from the initial distribution to the target distribution. The proposed LwS density path allows for an efficient search of modes of the target distribution while canonical methods fail. We theoretically analyze the Wasserstein distance of the distribution of the PGPS-generated samples and the target distribution due to approximation and discretization errors. Practically, the proposed PGPS-LwS method demonstrates higher Bayesian inference accuracy and better calibration ability in experiments conducted on both synthetic and real-world Bayesian learning tasks, compared to baselines, such as SVGD and Langevin dynamics, etc.

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AAAI Conference 2021 Conference Paper

Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science

Ziyu Xiang
Mingzhou Fan
Guillermo Vázquez Tovar
William Trehern
Byung-Jun Yoon
Xiaofeng Qian
Raymundo Arroyave
Xiaoning Qian

Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks. Here, we develop AFE to extract dependency relationships that can be interpreted with functional formulas to discover physics meaning or new hypotheses for the problems of interest. We focus on materials science applications, where interpretable predictive modeling may provide principled understanding of materials systems and guide new materials discovery. It is often computationally prohibitive to exhaust all the potential relationships to construct and search the whole feature space to identify interpretable and predictive features. We develop and evaluate new AFE strategies by exploring a feature generation tree (FGT) with deep Qnetwork (DQN) for scalable and efficient exploration policies. The developed DQN-based AFE strategies are benchmarked with the existing AFE methods on several materials science datasets.

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