Publications:

• Robust max statistics for high-dimensional inference
  M. Liu, M. E. Lopes
  Preprint, 2024 [arxiv]

• Testing elliptical models in high dimensions
  S. Wang, M. E. Lopes
  Preprint, 2024 [arxiv]

• Improved rates of bootstrap approximation for the operator norm: A coordinate-free approach
  M. E. Lopes
  to appear: Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, 2024+ [arxiv]

• Error estimation for random Fourier features
  J. Yao, N. B. Erichson, M. E. Lopes
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 [link] [arxiv]
  (included in oral presentations, top 1.9% of submissions)
  

• Randomized numerical linear algebra: A perspective on the field with an eye to software
  with R. Murray, J. Demmel, M. W. Mahoney, et al.
  Preprint, 2023 [arxiv]
  See also related video on the Mutual Information YouTube channel

• Rates of bootstrap approximation for eigenvalues in high-dimensional PCA
  J. Yao, M. E. Lopes
  Statistica Sinica, 2023 [arxiv]

• Bootstrapping the operator norm in high dimensions: Error estimation for covariance matrices and sketching
  M. E. Lopes, N. B. Erichson, M. W. Mahoney
  Bernoulli, 2023 [pdf] [link]

• High-dimensional MANOVA via bootstrapping and its application to functional data and sparse count data
  Z. Lin, M. E. Lopes, H.-G. Mueller
  Journal of the American Statistical Association, 2023 [link] [arxiv]

• A bootstrap method for spectral statistics in high-dimensional elliptical models
  S. Wang, M. E. Lopes
  Electronic Journal of Statistics, 2023 [pdf] [link]

• Central limit theorem and bootstrap approximation in high dimensions: Near 1/√n rates via implicit smoothing
  M. E. Lopes
  Annals of Statistics, 2022 [pdf] [link]

• A sharp lower-tail bound for Gaussian maxima with application to bootstrap methods in high dimensions
  M. E. Lopes, J. Yao
  Electronic Journal of Statistics, 2022 [pdf] [link]

• Randomized algorithms for scientific computing (RASC)
  with A. Buluç, T. G. Kolda, S. M. Wild, et al.
  Report from the DOE Advanced Scientific Computing Research Workshop, 2021 [link] [arxiv]

• Measuring the algorithmic convergence of randomized ensembles: The regression setting
  M. E. Lopes, S. Wu, T. C. M. Lee
  SIAM Journal on Mathematics of Data Science, 2020 [link] [arxiv]

• Error estimation for sketched SVD via the bootstrap
  M. E. Lopes, N. B. Erichson, M. W. Mahoney
  International Conference on Machine Learning (ICML), 2020 [link][arxiv]

• Estimating the error of randomized Newton methods: A bootstrap approach
  X. T. Chen, M. E. Lopes
  International Conference on Machine Learning (ICML), 2020 [link]

• Bootstrapping max statistics in high dimensions:
  Near-parametric rates under weak variance decay and application to functional and multinomial data
  M. E. Lopes, Z. Lin, H.-G. Mueller
  Annals of Statistics, 2020 [pdf] [link]

• Estimating a sharp convergence bound for randomized ensembles
  M. E. Lopes
  Journal of Statistical Planning and Inference, 2020 [link] [arxiv]

• Estimating the algorithmic variance of randomized ensembles via the bootstrap
  M. E. Lopes
  Annals of Statistics, 2019 [pdf] [link] [arxiv]

• Bootstrapping spectral statistics in high dimensions
  M. E. Lopes, A. Blandino, A. Aue
  Biometrika, 2019 [link] [arxiv]

• A bootstrap method for error estimation in randomized matrix multiplication
  M. E. Lopes, S. Wang, M. W. Mahoney
  Journal of Machine Learning Research (JMLR), 2019 [link] [arxiv]

• Error estimation for randomized least-squares algorithms via the bootstrap
  M. E. Lopes, S. Wang, M. W. Mahoney
  International Conference on Machine Learning (ICML), 2018 [link] [arxiv]
  (included in long talks, top 8.6% of submissions)

• Unknown sparsity in compressed sensing: denoising and inference
  M. E. Lopes
  IEEE Transactions on Information Theory, 2016 [link] [arxiv]

• A residual bootstrap for high-dimensional regression with near low-rank designs
  M. E. Lopes
  Advances in Neural Information Processing Systems (NIPS), 2014 [link] [arxiv]
  (included in spotlight presentations, top 4.8% of submissions)
  

• Estimating unknown sparsity in compressed sensing
  M. E. Lopes
  International Conference on Machine Learning (ICML), 2013 [link] [arxiv]
  

• A more powerful two-sample test in high dimensions using random projection
  M. E. Lopes, L. Jacob, and M. J. Wainwright
  Advances in Neural Information Processing Systems (NIPS), 2011 [link] [arxiv]

---