Timezone: »
Poster
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
Ali Eshragh · Fred Roosta · Asef Nazari · Michael Mahoney
We apply methods from randomized numerical linear algebra (RandNLA) to develop improved algorithms for the analysis of large-scale time series data. We first develop a new fast algorithm to estimate the leverage scores of an autoregressive (AR) model in big data regimes. We show that the accuracy of approximations lies within $(1+\mathcal{O}({\varepsilon}))$ of the true leverage scores with high probability. These theoretical results are subsequently exploited to develop an efficient algorithm, called LSAR, for fitting an appropriate AR model to big time series data. Our proposed algorithm is guaranteed, with high probability, to find the maximum likelihood estimates of the parameters of the underlying true AR model and has a worst case running time that significantly improves those of the state-of-the-art alternatives in big data regimes. Empirical results on large-scale synthetic as well as real data highly support the theoretical results and reveal the efficacy of this new approach.
Author Information
Ali Eshragh (University of Newcastle)
Fred Roosta (University of Queensland)
Asef Nazari
Michael Mahoney (UC Berkeley)
More from the Same Authors
-
2021 Spotlight: Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update »
Michal Derezinski · Jonathan Lacotte · Mert Pilanci · Michael Mahoney -
2022 Poster: A Fast Post-Training Pruning Framework for Transformers »
Woosuk Kwon · Sehoon Kim · Michael Mahoney · Joseph Hassoun · Kurt Keutzer · Amir Gholami -
2022 Poster: Squeezeformer: An Efficient Transformer for Automatic Speech Recognition »
Sehoon Kim · Amir Gholami · Albert Shaw · Nicholas Lee · Karttikeya Mangalam · Jitendra Malik · Michael Mahoney · Kurt Keutzer -
2021 : Q&A with Michael Mahoney »
Michael Mahoney -
2021 : Putting Randomized Matrix Algorithms in LAPACK, and Connections with Second-order Stochastic Optimization, Michael Mahoney »
Michael Mahoney -
2021 Poster: Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update »
Michal Derezinski · Jonathan Lacotte · Mert Pilanci · Michael Mahoney -
2021 Poster: Noisy Recurrent Neural Networks »
Soon Hoe Lim · N. Benjamin Erichson · Liam Hodgkinson · Michael Mahoney -
2021 Poster: Hessian Eigenspectra of More Realistic Nonlinear Models »
Zhenyu Liao · Michael Mahoney -
2021 Poster: Characterizing possible failure modes in physics-informed neural networks »
Aditi Krishnapriyan · Amir Gholami · Shandian Zhe · Robert Kirby · Michael Mahoney -
2021 Poster: Taxonomizing local versus global structure in neural network loss landscapes »
Yaoqing Yang · Liam Hodgkinson · Ryan Theisen · Joe Zou · Joseph Gonzalez · Kannan Ramchandran · Michael Mahoney -
2021 Poster: Stateful ODE-Nets using Basis Function Expansions »
Alejandro Queiruga · N. Benjamin Erichson · Liam Hodgkinson · Michael Mahoney -
2021 Oral: Hessian Eigenspectra of More Realistic Nonlinear Models »
Zhenyu Liao · Michael Mahoney -
2020 Poster: Boundary thickness and robustness in learning models »
Yaoqing Yang · Rajiv Khanna · Yaodong Yu · Amir Gholami · Kurt Keutzer · Joseph Gonzalez · Kannan Ramchandran · Michael Mahoney -
2020 Poster: Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization »
Michal Derezinski · Burak Bartan · Mert Pilanci · Michael Mahoney -
2020 Poster: Exact expressions for double descent and implicit regularization via surrogate random design »
Michal Derezinski · Feynman Liang · Michael Mahoney -
2020 Poster: Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method »
Michal Derezinski · Rajiv Khanna · Michael Mahoney -
2020 Poster: Precise expressions for random projections: Low-rank approximation and randomized Newton »
Michal Derezinski · Feynman Liang · Zhenyu Liao · Michael Mahoney -
2020 Oral: Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method »
Michal Derezinski · Rajiv Khanna · Michael Mahoney -
2020 Poster: A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent »
Zhenyu Liao · Romain Couillet · Michael Mahoney -
2020 Poster: A Statistical Framework for Low-bitwidth Training of Deep Neural Networks »
Jianfei Chen · Yu Gai · Zhewei Yao · Michael Mahoney · Joseph Gonzalez -
2019 : Final remarks »
Anastasios Kyrillidis · Albert Berahas · Fred Roosta · Michael Mahoney -
2019 Workshop: Beyond first order methods in machine learning systems »
Anastasios Kyrillidis · Albert Berahas · Fred Roosta · Michael Mahoney -
2019 : Opening Remarks »
Anastasios Kyrillidis · Albert Berahas · Fred Roosta · Michael Mahoney -
2019 Poster: ANODEV2: A Coupled Neural ODE Framework »
Tianjun Zhang · Zhewei Yao · Amir Gholami · Joseph Gonzalez · Kurt Keutzer · Michael Mahoney · George Biros -
2019 Poster: DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization »
Rixon Crane · Fred Roosta -
2019 Poster: Distributed estimation of the inverse Hessian by determinantal averaging »
Michal Derezinski · Michael Mahoney -
2018 Poster: GIANT: Globally Improved Approximate Newton Method for Distributed Optimization »
Shusen Wang · Fred Roosta · Peng Xu · Michael Mahoney -
2018 Poster: Hessian-based Analysis of Large Batch Training and Robustness to Adversaries »
Zhewei Yao · Amir Gholami · Qi Lei · Kurt Keutzer · Michael Mahoney -
2016 Poster: Feature-distributed sparse regression: a screen-and-clean approach »
Jiyan Yang · Michael Mahoney · Michael Saunders · Yuekai Sun -
2016 Poster: Sub-sampled Newton Methods with Non-uniform Sampling »
Peng Xu · Jiyan Yang · Farbod Roosta-Khorasani · Christopher RĂ© · Michael Mahoney -
2015 : Challenges in Multiresolution Methods for Graph-based Learning »
Michael Mahoney -
2015 : Using Local Spectral Methods in Theory and in Practice »
Michael Mahoney -
2015 Poster: Fast Randomized Kernel Ridge Regression with Statistical Guarantees »
Ahmed Alaoui · Michael Mahoney -
2013 Workshop: Large Scale Matrix Analysis and Inference »
Reza Zadeh · Gunnar Carlsson · Michael Mahoney · Manfred K. Warmuth · Wouter M Koolen · Nati Srebro · Satyen Kale · Malik Magdon-Ismail · Ashish Goel · Matei A Zaharia · David Woodruff · Ioannis Koutis · Benjamin Recht