数学与创造，可变思考
math and creativity, variable thinking
About Me
Yaoliang Yu (于耀亮) is currently a faculty in the David R. Cheriton School of Computer Science at University of Waterloo, as well as at the Vector institute. His main research interests include generative models, convex and nonconvex optimization, trustworthy machine learning, and applications in computer vision and natural language processing.
Interests
- Machine Learning
- Optimization
Education
-
PhD (Statistical Machine Learning)
University of Alberta
Some Publications
(2025).
Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets.
International Conference on Machine Learning (ICML).
(2025).
Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization.
International Conference on Learning Representations (ICLR).
(2024).
Faster Approximation of Probabilistic and Distributional Values via Least Squares.
International Conference on Learning Representations (ICLR).
(2023).
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks.
International Conference on Machine Learning (ICML).
(2021).
Demystifying and Generalizing BinaryConnect.
Advances in Neural Information Processing Systems (NeurIPS).
(2019).
Multivariate Triangular Quantile Maps for Novelty Detection.
Advances in Neural Information Processing Systems (NeurIPS).
(2019).
Sum-of-squares Polynomial Flow.
International Conference on Machine Learning (ICML).
(2018).
Deep Homogeneous Mixture Models: Representation, Separation and Approximation.
Advances in Neural Information Processing Systems (NeurIPS).
(2017).
Generalized Conditional Gradient for Sparse Estimation.
Journal of Machine Learning Research (JMLR).
(2015).
Minimizing Nonconvex Non-Separable Functions.
International Conference on Artificial Intelligence and Statistics (AISTATS).
(2013).
Characterizing the Representer Theorem.
International Conference on Machine Learning (ICML).
(2013).
On Decomposing the Proximal Map.
Advances in Neural Information Processing Systems (NeurIPS).
(2011).
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering.
Conference on Uncertainty in Artificial Intelligence (UAI).
(2009).
A General Projection Property for Distribution Families.
Advances in Neural Information Processing Systems (NeurIPS).
Recent Courses
Recent Readings