- Tyler Maunu. First-Order Algorithms for Optimization over Graph Laplacians. Sampling Theory and Applications (SampTA). 2023.
- Tyler Maunu, Thibaut Le Gouic, and Philippe Rigollet. Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery. International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.
- Tyler Maunu and Gilad Lerman, Depth Descent Synchronization in SO(D). International Journal of Computer Vision. 2023.
- Tyler Maunu, Chenyu Yu, and Gilad Lerman. Stochastic and Private Nonconvex Outlier-Robust PCA. Mathematical and Scientific Machine Learning (2022).
- Max Daniels, Tyler Maunu and Paul Hand, Score-based Generative Neural Networks for Large-Scale Optimal Transport. Advances in Neural Information Processing Systems (NeurIPS). 2021.
- Yunpeng Shi, Shaohan Li, Tyler Maunu and Gilad Lerman, Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching. International Conference on 3D Vision. 2021.
- Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. Advances in Neural Information Processing Systems (NeurIPS). 2020.
- Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin Stromme, Exponential ergodicity of mirror Langevin diffusions. Advances in Neural Information Processing Systems (NeurIPS). 2020.
- Sinho Chewi, Tyler Maunu, Philippe Rigollet, and Austin Stromme, Gradient Descent Algorithms for Bures-Wasserstein Barycenters. Conference on Learning Theory (COLT). 2020.
- Tyler Maunu and Gilad Lerman, Robust Subspace Recovery with Adversarial Outliers. 2019.
- Tyler Maunu, Teng Zhang, and Gilad Lerman, A Well-Tempered Landscape for Non-convex Robust Subspace Recovery. Journal of Machine Learning Research, 2019.
- Gilad Lerman and Tyler Maunu, An Overview of Robust Subspace Recovery, Proceedings of the IEEE, 2018.
- Gilad Lerman and Tyler Maunu, Fast, Robust and Non-convex Subspace Recovery, Information and Inference: A Journal of the IMA, 2017.