Android Malware Detection Based on Factorization Machine. - Chenglin Li; Keith G. Mills; Di Niu; Rui Zhu 007; Hongwen Zhang 002; Husam Kinawi (IEEE Access, 2019)

L2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. - Keith G. Mills; Fred X. Han; Mohammad Salameh; Seyed Saeed Changiz Rezaei; Linglong Kong; Wei Lu 023; Shuo Lian; Shangling Jui; Di Niu (CIKM, 2021)

Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search. - Keith G. Mills; Fred X. Han; Jialin Zhang; Seyed Saeed Changiz Rezaei; Fabian Chudak; Wei Lu 023; Shuo Lian; Shangling Jui; Di Niu (CIKM, 2021)

Generative Adversarial Neural Architecture Search. - Seyed Saeed Changiz Rezaei; Fred X. Han; Di Niu; Mohammad Salameh; Keith G. Mills; Shuo Lian; Wei Lu 023; Shangling Jui (IJCAI, 2021)

Exploring Neural Architecture Search Space via Deep Deterministic Sampling. - Keith G. Mills; Mohammad Salameh; Di Niu; Fred X. Han; Seyed Saeed Changiz Rezaei; Hengshuai Yao; Wei Lu 023; Shuo Lian; Shangling Jui (IEEE Access, 2021)

L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. - Keith G. Mills; Fred X. Han; Mohammad Salameh; Seyed Saeed Changiz Rezaei; Linglong Kong; Wei Lu 023; Shuo Lian; Shangling Jui; Di Niu (CoRR, 2021)

R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning. - Shengyao Lu; Bang Liu; Keith G. Mills; Shangling Jui; Di Niu (ICLR, 2022)

GENNAPE: Towards Generalized Neural Architecture Performance Estimators. - Keith G. Mills; Fred X. Han; Jialin Zhang; Fabian Chudak; Ali Safari Mamaghani; Mohammad Salameh; Wei Lu 023; Shangling Jui; Di Niu (AAAI, 2023)

AIO-P: Expanding Neural Performance Predictors beyond Image Classification. - Keith G. Mills; Di Niu; Mohammad Salameh; Weichen Qiu; Fred X. Han; Puyuan Liu; Jialin Zhang; Wei Lu 023; Shangling Jui (AAAI, 2023)

AutoGO: Automated Computation Graph Optimization for Neural Network Evolution. - Mohammad Salameh; Keith G. Mills; Negar Hassanpour; Fred X. Han; Shuting Zhang; Wei Lu 023; Shangling Jui; Chunhua Zhou; Fengyu Sun; Di Niu (NeurIPS, 2023)

A General-Purpose Transferable Predictor for Neural Architecture Search. - Fred X. Han; Keith G. Mills; Fabian Chudak; Parsa Riahi; Mohammad Salameh; Jialin Zhang; Wei Lu 023; Shangling Jui; Di Niu (SDM, 2023)

Building Optimal Neural Architectures Using Interpretable Knowledge. - Keith G. Mills; Fred X. Han; Mohammad Salameh; Shengyao Lu; Chunhua Zhou; Jiao He; Fengyu Sun; Di Niu (CVPR, 2024)

GOAt: Explaining Graph Neural Networks via Graph Output Attribution. - Shengyao Lu; Keith G. Mills; Jiao He; Bang Liu; Di Niu (ICLR, 2024)

EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time. - Shengyao Lu; Bang Liu; Keith G. Mills; Jiao He; Di Niu (ICML, 2024)

Qua2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models. - Keith G. Mills; Mohammad Salameh; Ruichen Chen; Negar Hassanpour; Wei Lu 023; Di Niu (CoRR, 2024)

Qua2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models. - Keith G. Mills; Mohammad Salameh; Ruichen Chen; Negar Hassanpour; Wei Lu 023; Di Niu (AAAI, 2025)

Applying Graph Explanation to Operator Fusion. - Keith G. Mills; Muhammad Fetrat Qharabagh; Weichen Qiu; Fred X. Han; Mohammad Salameh; Wei Lu 023; Shangling Jui; Di Niu (CoRR, 2025)

FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers. - Ruichen Chen; Keith G. Mills; Di Niu (CoRR, 2025)

Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence. - Amirhosein Ghasemabadi; Keith G. Mills; Baochun Li; Di Niu (CoRR, 2025)