| DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving W Deng, J Pan, T Zhou, D Kong, A Flores, G Lin ACM International Conference on Web Search and Data Mining. WSDM'21, 2021 | 122 | 2021 |
| An Adaptive Empirical Bayesian Method for Sparse Deep Learning W Deng, X Zhang, F Liang, G Lin Advances in Neural Information Processing Systems. NeurIPS'19, 2019 | 69 | 2019 |
| Non-convex Learning via Replica Exchange Stochastic Gradient MCMC W Deng, Q Feng, L Gao, F Liang, G Lin International Conference on Machine Learning. ICML'20, 2020 | 66 | 2020 |
| Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation Y Chen, W Deng, S Fang, F Li, NT Yang, Y Zhang, K Rasul, S Zhe, ... International Conference on Machine Learning. ICML'23, 2023 | 49 | 2023 |
| A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions W Deng, G Lin, F Liang Advances in Neural Information Processing Systems. NeurIPS'20, 2020 | 42 | 2020 |
| Information Directed Sampling for Sparse Linear Bandits B Hao, T Lattimore, W Deng Advances in Neural Information Processing Systems. NeurIPS'21, 2021 | 24 | 2021 |
| On Convergence of Federated Averaging Langevin Dynamics W Deng, Q Zhang, YA Ma, Z Song, G Lin The Conference on Uncertainty in Artificial Intelligence. UAI'24, 2024 | 22 | 2024 |
| An Adaptively Weighted Stochastic Gradient MCMC Algorithm for Monte Carlo Simulation and Global Optimization W Deng, G Lin, F Liang Statistics and Computing 32 (58), 1-24, 2022 | 21 | 2022 |
| Variational Schrödinger Diffusion Models W Deng, W Luo, Y Tan, M Biloš, Y Chen, Y Nevmyvaka, RTQ Chen International Conference on Machine Learning. ICML'24, 2024 | 17 | 2024 |
| Interacting Contour Stochastic Gradient Langevin Dynamics W Deng, S Liang, B Hao, G Lin, F Liang International Conference on Learning Representations. ICLR'22, 2022 | 16 | 2022 |
| Bayesian Sparse Learning with Preconditioned Stochastic Gradient MCMC and Its Applications Y Wang, W Deng, G Lin Journal of Computational Physics, 2021 | 14 | 2021 |
| Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo H Zheng, W Deng, C Moya, G Lin International Conference on Artificial Intelligence and Statistics. AISTATS'24, 2024 | 11 | 2024 |
| Reflected Schrödinger Bridge for Constrained Generative Modeling W Deng, Y Chen, NT Yang, H Du, Q Feng, RTQ Chen The Conference on Uncertainty in Artificial Intelligence. UAI'24 (Oral), 2024 | 11 | 2024 |
| Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction W Deng, Q Feng, G Karagiannis, G Lin, F Liang International Conference on Learning Representations. ICLR'21, 2021 | 11 | 2021 |
| Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications R Zhang, W Deng, MY Zhu Asian Conference on Machine Learning. ACML'17, 2017 | 7 | 2017 |
| Non-reversible Parallel Tempering for Deep Posterior Approximation W Deng, Q Zhang, Q Feng, F Liang, G Lin Thirty-Seventh AAAI Conference on Artificial Intelligence. AAAI'23, 2023 | 6 | 2023 |
| An Adaptive Hessian Approximated Stochastic Gradient MCMC Method Y Wang, W Deng, G Lin Journal of Computational Physics, 110150, 2021 | 6 | 2021 |
| Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization K Rojas, J Lin, K Rasul, A Schneider, Y Nevmyvaka, M Tao, W Deng arXiv preprint arXiv:2510.08554, 2025 | 4 | 2025 |
| Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory J Liang, Q Zhang, W Deng, Q Song, G Lin Journal of Computational and Graphical Statistics, 2025 | 3 | 2025 |
| Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics H Zheng, H Du, Q Feng, W Deng, G Lin International Conference on Machine Learning. ICML'24, 2024 | 3 | 2024 |
Guang LinAssociate Dean for Research, Moses Cobb Stevens Professor in Mathematics, Mech Eng Purdue UniversityVerified email at purdue.edu
