Neural collapse with normalized features: A geometric analysis over the riemannian manifold C Yaras, P Wang, Z Zhu, L Balzano, Q Qu Advances in neural information processing systems 35, 11547-11560, 2022 | 35 | 2022 |
The emergence of reproducibility and consistency in diffusion models H Zhang, J Zhou, Y Lu, M Guo, P Wang, L Shen, Q Qu Forty-first International Conference on Machine Learning, 2023 | 32 | 2023 |
Optimal non-convex exact recovery in stochastic block model via projected power method P Wang, H Liu, Z Zhou, AMC So International Conference on Machine Learning, 10828-10838, 2021 | 21 | 2021 |
Linear Convergence Analysis of Neural Collapse with Unconstrained Features P Wang, H Liu, C Yaras, L Balzano, Q Qu OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 2022 | 14 | 2022 |
Globally convergent accelerated proximal alternating maximization method for l1-principal component analysis P Wang, H Liu, AMC So ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 14 | 2019 |
Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for -Norm Principal Component Analysis P Wang, H Liu, AMC So SIAM Journal on Optimization 33 (2), 684-712, 2023 | 12 | 2023 |
Non-convex exact community recovery in stochastic block model P Wang, Z Zhou, AMC So Mathematical Programming 195 (1), 1-37, 2022 | 11 | 2022 |
A nearly-linear time algorithm for exact community recovery in stochastic block model P Wang, Z Zhou, AMC So International Conference on Machine Learning, 10126-10135, 2020 | 10 | 2020 |
Understanding deep representation learning via layerwise feature compression and discrimination P Wang, X Li, C Yaras, Z Zhu, L Balzano, W Hu, Q Qu arXiv preprint arXiv:2311.02960, 2023 | 7 | 2023 |
The law of parsimony in gradient descent for learning deep linear networks C Yaras, P Wang, W Hu, Z Zhu, L Balzano, Q Qu arXiv preprint arXiv:2306.01154, 2023 | 7 | 2023 |
Exact community recovery over signed graphs X Wang, P Wang, AMC So International Conference on Artificial Intelligence and Statistics, 9686-9710, 2022 | 7 | 2022 |
Generalized neural collapse for a large number of classes J Jiang, J Zhou, P Wang, Q Qu, D Mixon, C You, Z Zhu arXiv preprint arXiv:2310.05351, 2023 | 6 | 2023 |
Convergence and recovery guarantees of the k-subspaces method for subspace clustering P Wang, H Liu, AMC So, L Balzano International Conference on Machine Learning, 22884-22918, 2022 | 6 | 2022 |
Projected tensor power method for hypergraph community recovery J Wang, YM Pun, X Wang, P Wang, AMC So International Conference on Machine Learning, 36285-36307, 2023 | 3 | 2023 |
Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Matrix Factorizations C Yaras, P Wang, W Hu, Z Zhu, L Balzano, Q Qu NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 2023 | 2 | 2023 |
Symmetric Matrix Completion with ReLU Sampling H Liu, P Wang, L Huang, Q Qu, L Balzano arXiv preprint arXiv:2406.05822, 2024 | 1 | 2024 |
A Global Geometric Analysis of Maximal Coding Rate Reduction P Wang, H Liu, D Pai, Y Yu, Z Zhu, Q Qu, Y Ma arXiv preprint arXiv:2406.01909, 2024 | 1 | 2024 |
Fast first-order methods for the massive robust multicast beamforming problem with interference temperature constraints H Liu, P Wang, AMC So ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 1 | 2019 |
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation C Yaras, P Wang, L Balzano, Q Qu arXiv preprint arXiv:2406.04112, 2024 | | 2024 |
Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Linear Networks C Yaras, P Wang, W Hu, Z Zhu, L Balzano, Q Qu Conference on Parsimony and Learning (Recent Spotlight Track), 2023 | | 2023 |