Publications
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT.
Proceedings of the AAAI-20 Conference.
(2020). Parallel Local Graph Clustering.
Proceedings of the VLDB Endowment. 9(12),
(2016).
(2016).
(2015).
(2022).
Noisy Recurrent Neural Networks.
Advances in Neural Information Processing Systems Conference. 34,
(2021). 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). 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).
(2020).
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). Mining Large graphs.
Handbook of Big Data. 191-220.
(2016). Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression.
Proceedings of 2019 COLT.
(2019).
(2016).
(2018). Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data.
The Astrophysical Journal.
(2016). LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data.
Journal of Machine Learning Research. 23, 1-36.
(2022).
(2015). Lipschitz recurrent neural networks.
International Conference on Learning Representations.
(2021). Inefficiency of K-FAC for Large Batch Size Training.
Proceedings of the AAAI-20 Conference.
(2020).
(2015). Identifying Important Ions and Positions in Mass Spectrometry Imaging Data Using CUR Matrix Decompositions.
Analytical Chemistry. 87(9), 4658-4666.
(2015).
(2021). Hessian-based Analysis of Large Batch Training and Robustness to Adversaries.
Proceedings of the 2018 NeurIPS Conference. 4954-4964.
(2018). Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks.
Proceedings of 2020 SDM Conference.
(2020).
(2021).