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)

Bansor: Improving Tensor Program Auto-Scheduling with Bandit Based Reinforcement Learning. - Chao Gao; Tong Mo; Taylor Zowtuk; Tanvir Sajed; Laiyuan Gong; Hanxuan Chen; Shangling Jui; Wei Lu 023 (ICTAI, 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)

Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability. - Yafei Wang; Bo Pan; Wei Tu; Peng Liu 048; Bei Jiang; Chao Gao; Wei Lu 023; Shangling Jui; Linglong Kong (AAAI, 2022)

A Memory-Bounded Best-First Beam Search and Its Application to Scheduling Halide Programs. - Chao Gao; Jingwei Chen; Tong Mo; Tanvir Sajed; Shangling Jui; Min Qin; Laiyuan Gong; Wei Lu 023 (SOCS, 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)

FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting. - Liyao Jiang; Negar Hassanpour; Mohammad Salameh; Mohan Sai Singamsetti; Fengyu Sun; 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 (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)