Input Design for Nonlinear Model Discrimination via Affine Abstraction. - Kanishka Raj Singh; Yuhao Ding; Necmiye Ozay; Sze Zheng Yong (ADHS, 2018)

Optimal input design for affine model discrimination with applications in intention-aware vehicles. - Yuhao Ding; Farshad Harirchi; Sze Zheng Yong; Emil Jacobsen; Necmiye Ozay (ICCPS, 2018)

Analysis of cars' commuting behavior under license plate restriction policy: a case study in Hangzhou, China. - Wenbin Yao; Yuhao Ding; Fangming Xu; Sheng Jin 001 (ITSC, 2018)

FTDL: A Tailored FPGA-Overlay for Deep Learning with High Scalability. - Runbin Shi; Yuhao Ding; Xuechao Wei; He Li 008; Hang Liu 001; Hayden Kwok-Hay So; Caiwen Ding (DAC, 2020)

FTDL: An FPGA-tailored Architecture for Deep Learning Systems. - Runbin Shi; Yuhao Ding; Xuechao Wei; Hang Liu 001; Hayden Kwok-Hay So; Caiwen Ding (FPGA, 2020)

CSB-RNN: a faster-than-realtime RNN acceleration framework with compressed structured blocks. - Runbin Shi; Peiyan Dong; Tong Geng; Yuhao Ding; Xiaolong Ma; Hayden Kwok-Hay So; Martin C. Herbordt; Ang Li 006; Yanzhi Wang (ICS, 2020)

Analysis of Spurious Local Solutions of Optimal Control Problems: One-Shot Optimization Versus Dynamic Programming. - Yuhao Ding; Yingjie Bi; Javad Lavaei (ACC, 2021)

Escaping Spurious Local Minimum Trajectories in Online Time-varying Nonconvex Optimization. - Yuhao Ding; Javad Lavaei; Murat Arcak (ACC, 2021)

Structured Projection-free Online Convex Optimization with Multi-point Bandit Feedback. - Yuhao Ding; Javad Lavaei (CDC, 2021)

Ontology-Enhanced Slot Filling. - Yuhao Ding; Yik-Cheung Tam (CoRR, 2021)

On the Global Convergence of Momentum-based Policy Gradient. - Yuhao Ding; Junzi Zhang; Javad Lavaei (CoRR, 2021)

Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization. - Yuhao Ding; Junzi Zhang; Javad Lavaei (CoRR, 2021)

Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm. - Ping Lao; Qi Liu 038; Yuhao Ding; Yu Wang; Yuan Li; Meng Li (Remote. Sens., 2021)

On the Global Optimum Convergence of Momentum-based Policy Gradient. - Yuhao Ding; Junzi Zhang; Javad Lavaei (AISTATS, 2022)

A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization. - Donghao Ying; Yuhao Ding; Javad Lavaei (AISTATS, 2022)

Non-stationary Risk-Sensitive Reinforcement Learning: Near-Optimal Dynamic Regret, Adaptive Detection, and Separation Design. - Yuhao Ding; Ming Jin 002; Javad Lavaei (AAAI, 2023)

Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints. - Yuhao Ding; Javad Lavaei (AAAI, 2023)

Policy-Based Primal-Dual Methods for Convex Constrained Markov Decision Processes. - Donghao Ying; Mengzi Amy Guo; Yuhao Ding; Javad Lavaei; Zuo-Jun Max Shen (AAAI, 2023)

Scalable Multi-Agent Reinforcement Learning with General Utilities. - Donghao Ying; Yuhao Ding; Alec Koppel; Javad Lavaei (ACC, 2023)

Local Analysis of Entropy-Regularized Stochastic Soft-Max Policy Gradient Methods. - Yuhao Ding; Junzi Zhang; Javad Lavaei (ECC, 2023)

MSD: Mixing Signed Digit Representations for Hardware-efficient DNN Acceleration on FPGA with Heterogeneous Resources. - Jiajun Wu 006; Jiajun Zhou; Yizhao Gao 002; Yuhao Ding; Ngai Wong 001; Hayden Kwok-Hay So (FCCM, 2023)

Model-Platform Optimized Deep Neural Network Accelerator Generation through Mixed-Integer Geometric Programming. - Yuhao Ding; Jiajun Wu 006; Yizhao Gao 002; Maolin Wang 002; Hayden Kwok-Hay So (FCCM, 2023)

SqueezeBlock: A Transparent Weight Compression Scheme for Deep Neural Networks. - Mo Song; Jiajun Wu 006; Yuhao Ding; Hayden Kwok-Hay So (ICFPT, 2023)

A CMDP-within-online framework for Meta-Safe Reinforcement Learning. - Vanshaj Khattar; Yuhao Ding; Bilgehan Sel; Javad Lavaei; Ming Jin 002 (ICLR, 2023)

Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold. - Bilgehan Sel; Ahmad Al-Tawaha; Yuhao Ding; Ruoxi Jia 001; Bo Ji 001; Javad Lavaei; Ming Jin 002 (L4DC, 2023)

Tempo Adaptation in Non-stationary Reinforcement Learning. - Hyunin Lee; Yuhao Ding; Jongmin Lee; Ming Jin 002; Javad Lavaei; Somayeh Sojoudi (NeurIPS, 2023)

Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities. - Donghao Ying; Yunkai Zhang 002; Yuhao Ding; Alec Koppel; Javad Lavaei (NeurIPS, 2023)

Tempo Adaption in Non-stationary Reinforcement Learning. - Hyunin Lee; Yuhao Ding; Jongmin Lee; Ming Jin 002; Javad Lavaei; Somayeh Sojoudi (CoRR, 2023)

Time-Variation in Online Nonconvex Optimization Enables Escaping From Spurious Local Minima. - Yuhao Ding; Javad Lavaei; Murat Arcak (IEEE Trans. Autom. Control., 2023)

On the Absence of Spurious Local Trajectories in Time-Varying Nonconvex Optimization. - Salar Fattahi; Cédric Josz; Yuhao Ding; Reza Mohammadi-Ghazi; Javad Lavaei; Somayeh Sojoudi (IEEE Trans. Autom. Control., 2023)

Spatial Distributions of Cloud Occurrences in Terms of Volume Fraction as Inferred from CloudSat and CALIPSO. - Yuhao Ding; Qi Liu 038; Ping Lao; Meng Li; Yuan Li; Qun Zheng; Yanghui Peng (Remote. Sens., 2023)

Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation. - Shangding Gu; Bilgehan Sel; Yuhao Ding; Lu Wang 029; Qingwei Lin; Ming Jin 002; Alois Knoll (AAAI, 2024)

Multi-Modal Flexible Headband for Sleep Monitoring. - Zaihao Wang; Yuhao Ding; Hongyu Chen 002; Zihao Wang; Chen Chen 039; Wei Chen 015 (BSN, 2024)

A Composable Dynamic Sparse Dataflow Architecture for Efficient Event-based Vision Processing on FPGA. - Yizhao Gao 002; Baoheng Zhang; Yuhao Ding; Hayden Kwok-Hay So (FPGA, 2024)

Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation. - Shangding Gu; Laixi Shi; Yuhao Ding; Alois Knoll; Costas J. Spanos; Adam Wierman; Ming Jin 002 (NeurIPS, 2024)

DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference. - Jiajun Zhou; Jiajun Wu 006; Yizhao Gao 002; Yuhao Ding; Chaofan Tao; Boyu Li; Fengbin Tu; Kwang-Ting Cheng; Hayden Kwok-Hay So; Ngai Wong 001 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2024)

The Landscape of Deterministic and Stochastic Optimal Control Problems: One-Shot Optimization Versus Dynamic Programming. - Jihun Kim; Yuhao Ding; Yingjie Bi; Javad Lavaei (IEEE Trans. Autom. Control., 2024)

Policy-based Primal-Dual Methods for Concave CMDP with Variance Reduction. - Donghao Ying; Mengzi Amy Guo; Hyunin Lee; Yuhao Ding; Javad Lavaei; Zuo-Jun Max Shen (J. Artif. Intell. Res., 2025)

Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning. - Shangding Gu; Bilgehan Sel; Yuhao Ding; Lu Wang 029; Qingwei Lin; Alois Knoll; Ming Jin 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Solving the Vehicle Cooperation Problem at Signal-Free Intersection via an Asynchronous Deep Reinforcement Learning Approach. - Shuai Wang 009; Yuhao Ding; Xiaoqi Ding; Xiaojun Tan (IEEE Internet Things J., 2025)

Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods With Entropy Regularization. - Yuhao Ding; Junzi Zhang; Hyunin Lee; Javad Lavaei (IEEE Trans. Autom. Control., 2025)