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Yicheng Wang

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6 papers

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EAAI Journal 2026 Journal Article

An automated and explainable machine learning framework for multi-objective energy performance evaluation and early-stage design support of indoor arenas

Yicheng Wang
Peijun Lu
Yimin Sun
Mauro Berta
Hao Wang

Indoor arenas, as large-span public buildings, entail high construction and operational costs, particularly in energy use. The pursuit of iconic forms often overshadows rational, performance-driven design, while their complex geometry and diverse functions make energy performance evaluation challenging at early design stages. This study develops an integrated framework that combines parametric simulation, a stacked-ensemble machine learning model, and SHAP (SHapley Additive exPlanations)-based interpretation to relate geometric configurations to the energy performance of indoor arenas. An automated performance evaluation pipeline integrates multiple evaluation modules, enabling automated data transfer, processing, and large-scale analysis. Using partially stratified and Latin Hypercube sampling, 9667 design scenarios were generated across three representative climate zones. Five indicators in three climate zones were evaluated, including energy use intensity (EUI) for cooling, heating, lighting, and total energy, and energy yield intensity (EYI). The ensemble models achieved high predictive accuracy (average R2 > 0. 95), providing a reliable basis for interpretation. SHAP analysis reveals the varying contributions of design parameters to each performance indicator across climates, as well as the nonlinear interaction mechanisms between complex roof morphology and overall energy behavior. These findings establish climate-based strategies for balancing energy consumption and generation, and demonstrate an integrated and interpretable control workflow for performance-informed design and analytical support for performance-oriented design decisions in complex-built systems.

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YNIMG Journal 2025 Journal Article

Olfactory sensation emotion regulation: The implicit emotion regulation function of positive olfactory stimuli during emotional picture processing

Jiaotao Cai
Xinran Wang
Jiayi Zhou
Ye di
Ziruo Shen
Shuo An
Bingyang Long
Yicheng Wang

Previous research has shown that olfactory stimuli can induce emotional states, physiological and neural responses related to emotions. It remains unclear whether positive olfactory stimuli could down-regulate individuals' negative emotions or up-regulate individuals' positive emotions in an unconscious way. The present study investigated the effects of emotional olfactory stimuli on the behavioral and electrophysiological responses to emotional pictures. Forty participants were exposed to different types of odor conditions (neutral, pleasant, unpleasant) and evaluated the emotional pictures' valence and arousal while electroencephalography was recorded. Behavioral results showed that participants reported more positive emotions in response to positive pictures in two types of pleasant odor conditions (especially the citrus) than in unpleasant or neutral odor conditions. However, the pleasant odor (lavender) increased the positive pictures' valence scores but decreased the positive pictures' arousal scores. The ERPs results showed that the pleasant odors reduced the amplitudes of N1 and EPN components in response to negative pictures, indicating that pleasant odors might down-regulate negative emotions through decreasing attentional capture for negative visual stimuli during the early stage. The pleasant odor (lavender) attenuated LPP amplitudes for emotional pictures, suggesting that the positive olfactory stimuli might be helpful to down-regulate the emotional arousal by reducing attentional deployment to negative visual stimuli during the late stage of emotional visual stimuli processing. These findings provided novel behavioral and neurophysiological evidence that positive olfactory stimuli modulated visual emotional processing across multiple stages, and suggested that olfactory sensation could function as a rapid and relatively effortless emotion regulation modality.

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JMLR Journal 2025 Journal Article

Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy

Chengli Tan
Jiangshe Zhang
Junmin Liu
Yicheng Wang
Yunda Hao

Recently, sharpness-aware minimization (SAM) has attracted much attention because of its surprising effectiveness in improving generalization performance. However, compared to stochastic gradient descent (SGD), it is more prone to getting stuck at the saddle points, which as a result may lead to performance degradation. To address this issue, we propose a simple renormalization strategy, dubbed Stable SAM (SSAM), so that the gradient norm of the descent step maintains the same as that of the ascent step. Our strategy is easy to implement and flexible enough to integrate with SAM and its variants, almost at no computational cost. With elementary tools from convex optimization and learning theory, we also conduct a theoretical analysis of sharpness-aware training, revealing that compared to SGD, the effectiveness of SAM is only assured in a limited regime of learning rate. In contrast, we show how SSAM extends this regime of learning rate and then it can consistently perform better than SAM with the minor modification. Finally, we demonstrate the improved performance of SSAM on several representative data sets and tasks. [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

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NeurIPS Conference 2023 Conference Paper

Fair Graph Distillation

Qizhang Feng
Zhimeng (Stephen) Jiang
Ruiquan Li
Yicheng Wang
Na Zou
Jiang Bian
Xia Hu

As graph neural networks (GNNs) struggle with large-scale graphs due to high computational demands, data distillation for graph data promises to alleviate this issue by distilling a large real graph into a smaller distilled graph while maintaining comparable prediction performance for GNNs trained on both graphs. However, we observe that GNNs trained on distilled graphs may exhibit more severe group fairness problems than those trained on real graphs. Motivated by this observation, we propose \textit{fair graph distillation} (\Algnameabbr), an approach for generating small distilled \textit{fair and informative} graphs based on the graph distillation method. The challenge lies in the deficiency of sensitive attributes for nodes in the distilled graph, making most debiasing methods (e. g. , regularization and adversarial debiasing) intractable for distilled graphs. We develop a simple yet effective bias metric, called coherence, for distilled graphs. Based on the proposed coherence metric, we introduce a framework for fair graph distillation using a bi-level optimization algorithm. Extensive experiments demonstrate that the proposed algorithm can achieve better prediction performance-fairness trade-offs across various datasets and GNN architectures.

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IJCAI Conference 2022 Conference Paper

AutoVideo: An Automated Video Action Recognition System

Daochen Zha
Zaid Pervaiz Bhat
Yi-Wei Chen
Yicheng Wang
Sirui Ding
Jiaben Chen
Kwei-Herng Lai
Mohammad Qazim Bhat

Action recognition is an important task for video understanding with broad applications. However, developing an effective action recognition solution often requires extensive engineering efforts in building and testing different combinations of the modules and their hyperparameters. In this demo, we present AutoVideo, a Python system for automated video action recognition. AutoVideo is featured for 1) highly modular and extendable infrastructure following the standard pipeline language, 2) an exhaustive list of primitives for pipeline construction, 3) data-driven tuners to save the efforts of pipeline tuning, and 4) easy-to-use Graphical User Interface (GUI). AutoVideo is released under MIT license at https: //github. com/datamllab/autovideo

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AAAI Conference 2019 Conference Paper

Analyzing Compositionality-Sensitivity of NLI Models

Yixin Nie
Yicheng Wang
Mohit Bansal

Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i. e. , on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e. g. , separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models’ compositional understanding.

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