Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning

Feature transformation is to derive a new feature set from original features to augment the AI power of data. In many science domains such as material performance screening, while feature transformation can model material formula interactions and compositions and discover performance drivers, supervised labels are collected from expensive and lengthy experiments. This issue motivates an Unsupervised Feature Transformation Learning (UFTL) problem. Prior literature, such as manual transformation, supervised feedback guided search, and PCA, either relies on domain knowledge or expensive supervised feedback, or suffers from large search space, or overlooks non-linear feature-feature interactions. UFTL imposes a major challenge on existing methods: how to design a new unsupervised paradigm that captures complex feature interactions and avoids large search space? To fill this gap, we connect graph, contrastive, and generative learning to develop a measurement-pretrain-finetune paradigm for UFTL. For unsupervised feature set utility measurement, we propose a feature value consistency preservation perspective and develop a mean discounted cumulative gain like unsupervised metric to evaluate feature set utility. For unsupervised feature set representation pretraining, we regard a feature set as a feature-feature interaction graph, and develop an unsupervised graph contrastive learning encoder to embed feature sets into vectors. For generative transformation finetuning, we regard a feature set as a feature cross sequence and feature transformation as sequential generation. We develop a deep generative feature transformation model that coordinates the pretrained feature set encoder and the gradient information extracted from a feature set utility evaluator to optimize a transformed feature generator.

Dual-stage Flows-based Generative Modeling for Traceable Urban Planning

Urban planning, which aims to design feasible land-use configurations for target areas, has become increasingly essential due to the high-speed urbanization process in the modern era. However, the traditional urban planning conducted by human designers can be a complex and onerous task. Thanks to the advancement of deep learning algorithms, researchers have started to develop automated planning techniques. While these models have exhibited promising results, they still grapple with a couple of unresolved limitations: 1) Ignoring the relationship between urban functional zones and configurations and failing to capture the relationship among different functional zones. 2) Less interpretable and stable generation process. To overcome these limitations, we propose a novel generative framework based on normalizing flows, namely Dual-stage Urban Flows (DSUF) framework. Specifically, the first stage is to utilize zone-level urban planning flows to generate urban functional zones based on given surrounding contexts and human guidance. Then we employ an Information Fusion Module to capture the relationship among functional zones and fuse the information of different aspects. The second stage is to use configuration-level urban planning flows to obtain land-use configurations derived from fused information. We design several experiments to indicate that our framework can outperform compared to other generative models for the urban planning task.