| 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), 5563-5573, 2019 | 28 | 2019 |
| DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving W Deng, J Pan, T Zhou, D Kong, A Flores, G Lin The 14th International Conference on Web Search and Data Mining (WSDM'21), 2021 | 27* | 2021 |
| Non-convex Learning via Replica Exchange Stochastic Gradient MCMC W Deng, Q Feng, L Gao, F Liang, G Lin The 37th International Conference on Machine Learning (ICML'20), 2020 | 17 | 2020 |
| 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 | 8 | 2020 |
| Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications R Zhang, W Deng, MY Zhu The 9th Asian Conference on Machine Learning (ACML'17), 2017 | 7 | 2017 |
| Information Directed Sampling for Sparse Linear Bandits B Hao, T Lattimore, W Deng Advances in Neural Information Processing Systems (NeurIPS'21), 2021 | 5 | 2021 |
| Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction W Deng, Q Feng, G Karagiannis, G Lin, F Liang The 9th International Conference on Learning Representations (ICLR'21), 2021 | 4 | 2021 |
| Bayesian Sparse Learning with Preconditioned Stochastic Gradient MCMC and its Applications Y Wang, W Deng, G Lin Journal of Computational Physics, 2021 | 4 | 2021 |
| On Convergence of Federated Averaging Langevin Dynamics W Deng, Q Zhang, YA Ma, Z Song, G Lin http://arxiv.org/abs/2112.05120, 2022 | 3 | 2022 |
| An Adaptive Hessian Approximated Stochastic Gradient MCMC Method Y Wang, W Deng, G Lin Journal of Computational Physics, 110150, 2021 | 3 | 2021 |
| Interacting Contour Stochastic Gradient Langevin Dynamics W Deng, S Liang, B Hao, G Lin, F Liang The 10th International Conference on Learning Representations (ICLR'22), 2022 | | 2022 |
| 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 | | 2022 |
| Non-convex Bayesian Learning via Stochastic Gradient Markov Chain Monte Carlo W Deng Purdue University, 2021 | | 2021 |
| Non-reversible Parallel Tempering for Uncertainty Approximation in Deep Learning W Deng, Q Zhang, Q Feng, F Liang, G Lin Workshop on Distribution-Free Uncertainty Quantification at ICML 2021, 2021 | | 2021 |
guang linProfessor of Mathematics, Purdue UniversityVerified email at purdue.edu
