Publications
(2015).
Inefficiency of K-FAC for Large Batch Size Training.
Proceedings of the AAAI-20 Conference.
(2020). Lipschitz recurrent neural networks.
International Conference on Learning Representations.
(2021).
(2015). LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data.
Journal of Machine Learning Research. 23, 1-36.
(2022). Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data.
The Astrophysical Journal.
(2016).
(2018).
(2016). Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression.
Proceedings of 2019 COLT.
(2019). Mining Large graphs.
Handbook of Big Data. 191-220.
(2016). A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark.
Proceedings of the 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics.
(2016).
(2020). A New Spin on an Old Algorithm: Technical Perspective on "Communication Costs of Strassen's Matrix Multiplication".
Communications of the ACM. 57(2), 106.
(2014). Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware.
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). 100-108.
(2021). Noisy Recurrent Neural Networks.
Advances in Neural Information Processing Systems Conference. 34,
(2021).
(2022).
(2015).
(2016).
Parallel Local Graph Clustering.
Proceedings of the VLDB Endowment. 9(12),
(2016). Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT.
Proceedings of the AAAI-20 Conference.
(2020).
(2014). RandNLA, Pythons, and the CUR for Your Data Problems: Reporting from G2S3 2015 in Delphi.
SIAM News.
(2016). RandNLA: Randomized Numerical Linear Algebra.
Communications of the ACM. 59, 80-90.
(2016).
(2014). Shallow neural networks for fluid flow reconstruction with limited sensors.
Proceedings of the Royal Society A. 476(2238),
(2020).