publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2023

  1. Simple Disentanglement of Style and Content in Visual Representations
    Lilian Ngweta, Subha Maity, Alex Gittens, and 2 more authors
    In International Conference on Machine Learning, ICML 2023, 2023
  2. Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning
    Deniz Koyuncu, Alex Gittens, Bülent Yener, and 1 more author
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, 2023
  3. Word Sense Induction with Knowledge Distillation from BERT
    Anik Saha, Alex Gittens, and Bülent Yener
    CoRR, 2023
  4. Reduced Label Complexity For Tight \ell_2 Regression
    Alex Gittens, and Malik Magdon-Ismail
    CoRR, 2023
  5. A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction
    Anik Saha, Oktie Hassanzadeh, Alex Gittens, and 3 more authors
    CoRR, 2023
  6. Improving Neural Ranking Models with Traditional IR Methods
    Anik Saha, Oktie Hassanzadeh, Alex Gittens, and 3 more authors
    CoRR, 2023

2022

  1. An Adversarial Perspective on Accuracy, Robustness, Fairness, and Privacy: Multilateral-Tradeoffs in Trustworthy ML
    Alex Gittens, Bülent Yener, and Moti Yung
    IEEE Access, 2022
  2. SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models
    Anik Saha, Alex Gittens, Jian Ni, and 3 more authors
    In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, CASE@EMNLP 2022, 2022
  3. TINKER: A framework for Open source Cyberthreat Intelligence
    Nidhi Rastogi, Sharmishtha Dutta, Alex Gittens, and 2 more authors
    In IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022, 2022

2021

  1. Sparse Graph Based Sketching for Fast Numerical Linear Algebra
    Dong Hu, Shashanka Ubaru, Alex Gittens, and 3 more authors
    In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, 2021
  2. Learning Fair Canonical Polyadical Decompositions using a Kernel Independence Criterion
    Kevin Kim, and Alex Gittens
    CoRR, 2021
  3. Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation
    Gabriel Orlanski, and Alex Gittens
    CoRR, 2021
  4. Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models
    Daniel Park, Haidar Khan, Azer Khan, and 2 more authors
    CoRR, 2021

2020

  1. Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition
    Alex Gittens, Kareem S. Aggour, and Bülent Yener
    In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 2020
  2. NoisyCUR: An Algorithm for Two-Cost Budgeted Matrix Completion
    Dong Hu, Alex Gittens, and Malik Magdon-Ismail
    In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, 2020
  3. MALOnt: An Ontology for Malware Threat Intelligence
    Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, and 2 more authors
    CoRR, 2020

2019

  1. Alchemist: An Apache Spark \(⇔\) MPI interface
    Alex Gittens, Kai Rothauge, Shusen Wang, and 6 more authors
    Concurr. Comput. Pract. Exp., 2019
  2. Group Collaborative Representation for Image Set Classification
    Bo Liu, Liping Jing, Jia Li, and 3 more authors
    Int. J. Comput. Vis., 2019
  3. Scalable Kernel K-Means Clustering with Nystr}"om Approximation: Relative-Error Bounds
    Shusen Wang, Alex Gittens, and Michael W. Mahoney
    J. Mach. Learn. Res., 2019
  4. Fast Fixed Dimension L2-Subspace Embeddings of Arbitrary Accuracy, With Application to L1 and L2 Tasks
    Malik Magdon-Ismail, and Alex Gittens
    CoRR, 2019

2018

  1. Accelerating a Distributed CPD Algorithm for Large Dense, Skewed Tensors
    Kareem S. Aggour, Alex Gittens, and Bülent Yener
    In IEEE International Conference on Big Data (IEEE BigData 2018), Seattle, WA, USA, December 10-13, 2018, 2018
  2. Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist
    Alex Gittens, Kai Rothauge, Shusen Wang, and 6 more authors
    In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018, 2018
  3. Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist
    Alex Gittens, Kai Rothauge, Shusen Wang, and 6 more authors
    CoRR, 2018
  4. Alchemist: An Apache Spark MPI Interface
    Alex Gittens, Kai Rothauge, Shusen Wang, and 6 more authors
    CoRR, 2018

2017

  1. Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
    Shusen Wang, Alex Gittens, and Michael W. Mahoney
    J. Mach. Learn. Res., 2017
  2. Skip-Gram - Zipf + Uniform = Vector Additivity
    Alex Gittens, Dimitris Achlioptas, and Michael W. Mahoney
    In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, 2017
  3. Breaking Locality Accelerates Block Gauss-Seidel
    Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, and 3 more authors
    In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, 2017
  4. Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
    Shusen Wang, Alex Gittens, and Michael W. Mahoney
    In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, 2017
  5. Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
    Shusen Wang, Alex Gittens, and Michael W. Mahoney
    CoRR, 2017
  6. Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
    Shusen Wang, Alex Gittens, and Michael W. Mahoney
    CoRR, 2017

2016

  1. Revisiting the Nystrom Method for Improved Large-scale Machine Learning
    Alex Gittens, and Michael W. Mahoney
    J. Mach. Learn. Res., 2016
  2. Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies
    Alex Gittens, Aditya Devarakonda, Evan Racah, and 14 more authors
    In 2016 IEEE International Conference on Big Data (IEEE BigData 2016), Washington DC, USA, December 5-8, 2016, 2016
  3. A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark
    Alex Gittens, Jey Kottalam, Jiyan Yang, and 12 more authors
    In 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPS Workshops 2016, Chicago, IL, USA, May 23-27, 2016, 2016
  4. Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies
    Alex Gittens, Aditya Devarakonda, Evan Racah, and 14 more authors
    CoRR, 2016

2015

  1. Hardware compliant approximate image codes
    Da Kuang, Alex Gittens, and Raffay Hamid
    In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, 2015
  2. Spectral Clustering via the Power Method - Provably
    Christos Boutsidis, Prabhanjan Kambadur, and Alex Gittens
    In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, 2015
  3. Tensor machines for learning target-specific polynomial features
    Jiyan Yang, and Alex Gittens
    CoRR, 2015

2014

  1. Compact Random Feature Maps
    Raffay Hamid, Ying Xiao, Alex Gittens, and 1 more author
    In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, 2014

2013

  1. Improved Matrix Algorithms via the Subsampled Randomized Hadamard Transform
    Christos Boutsidis, and Alex Gittens
    SIAM J. Matrix Anal. Appl., 2013
  2. Revisiting the Nystrom method for improved large-scale machine learning
    Alex Gittens, and Michael W. Mahoney
    In Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, 2013
  3. Revisiting the Nystrom Method for Improved Large-Scale Machine Learning
    Alex Gittens, and Michael W. Mahoney
    CoRR, 2013
  4. Approximate Spectral Clustering via Randomized Sketching
    Alex Gittens, Prabhanjan Kambadur, and Christos Boutsidis
    CoRR, 2013
  5. Compact Random Feature Maps
    Raffay Hamid, Ying Xiao, Alex Gittens, and 1 more author
    CoRR, 2013

2012

  1. Improved matrix algorithms via the Subsampled Randomized Hadamard Transform
    Christos Boutsidis, and Alex Gittens
    CoRR, 2012

2011

  1. The spectral norm error of the naive Nystrom extension
    Alex Gittens
    CoRR, 2011