The spectral norm error of the naive Nystrom extension - Alex Gittens (CoRR, 2011)

Approximate Spectral Clustering via Randomized Sketching. - Alex Gittens; Prabhanjan Kambadur; Christos Boutsidis (CoRR, 2013)

Improved Matrix Algorithms via the Subsampled Randomized Hadamard Transform. - Christos Boutsidis; Alex Gittens (SIAM J. Matrix Anal. Appl., 2013)

Compact Random Feature Maps. - Raffay Hamid; Ying Xiao 003; Alex Gittens; Dennis DeCoste (ICML, 2014)

Hardware compliant approximate image codes. - Da Kuang; Alex Gittens; Raffay Hamid (CVPR, 2015)

Spectral Clustering via the Power Method - Provably. - Christos Boutsidis; Prabhanjan Kambadur; Alex Gittens (ICML, 2015)

Tensor machines for learning target-specific polynomial features. - Jiyan Yang; Alex Gittens (CoRR, 2015)

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; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (IEEE BigData, 2016)

A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. - Alex Gittens; Jey Kottalam; Jiyan Yang; Michael F. Ringenburg; Jatin Chhugani; Evan Racah; Mohitdeep Singh; Yushu Yao; Curt Fischer; Oliver Rübel; Benjamin P. Bowen; Norman G. Lewis; et al. (IPDPS Workshops, 2016)

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; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (CoRR, 2016)

Revisiting the Nystrom Method for Improved Large-scale Machine Learning. - Alex Gittens; Michael W. Mahoney (J. Mach. Learn. Res., 2016)

Skip-Gram - Zipf + Uniform = Vector Additivity. - Alex Gittens; Dimitris Achlioptas; Michael W. Mahoney (ACL (1), 2017)

Breaking Locality Accelerates Block Gauss-Seidel. - Stephen Tu; Shivaram Venkataraman; Ashia C. Wilson; Alex Gittens; Michael I. Jordan; Benjamin Recht (ICML, 2017)

Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds. - Shusen Wang; Alex Gittens; Michael W. Mahoney (CoRR, 2017)

Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. - Shusen Wang; Alex Gittens; Michael W. Mahoney (J. Mach. Learn. Res., 2017)

Accelerating a Distributed CPD Algorithm for Large Dense, Skewed Tensors. - Kareem S. Aggour; Alex Gittens; Bülent Yener (IEEE BigData, 2018)

Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. - Alex Gittens; Kai Rothauge; Shusen Wang; Michael W. Mahoney; Lisa Gerhardt; Prabhat; Jey Kottalam; Michael F. Ringenburg; Kristyn J. Maschhoff (KDD, 2018)

Alchemist: An Apache Spark ⇔ MPI interface. - Alex Gittens; Kai Rothauge; Shusen Wang; Michael W. Mahoney; Jey Kottalam; Lisa Gerhardt; Prabhat; Michael F. Ringenburg; Kristyn J. Maschhoff (Concurr. Comput. Pract. Exp., 2019)

Fast Fixed Dimension L2-Subspace Embeddings of Arbitrary Accuracy, With Application to L1 and L2 Tasks. - Malik Magdon-Ismail; Alex Gittens (CoRR, 2019)

Group Collaborative Representation for Image Set Classification. - Bo Liu 050; Liping Jing; Jia Li 002; Jian Yu 001; Alex Gittens; Michael W. Mahoney (Int. J. Comput. Vis., 2019)

Scalable Kernel K-Means Clustering with Nystr "om Approximation: Relative-Error Bounds. - Shusen Wang; Alex Gittens; Michael W. Mahoney (J. Mach. Learn. Res., 2019)

Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition. - Alex Gittens; Kareem S. Aggour; Bülent Yener (ICML, 2020)

NoisyCUR: An Algorithm for Two-Cost Budgeted Matrix Completion. - Dong Hu; Alex Gittens; Malik Magdon-Ismail (ECML/PKDD (1), 2020)

MALOnt: An Ontology for Malware Threat Intelligence. - Nidhi Rastogi; Sharmishtha Dutta; Mohammed J. Zaki; Alex Gittens; Charu C. Aggarwal (CoRR, 2020)

Sparse Graph Based Sketching for Fast Numerical Linear Algebra. - Dong Hu; Shashanka Ubaru; Alex Gittens; Kenneth L. Clarkson; Lior Horesh; Vassilis Kalantzis (ICASSP, 2021)

Learning Fair Canonical Polyadical Decompositions using a Kernel Independence Criterion. - Kevin Kim; Alex Gittens (CoRR, 2021)

Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation. - Gabriel Orlanski; Alex Gittens (CoRR, 2021)

Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models. - Daniel Park; Haidar Khan; Azer Khan; Alex Gittens; Bülent Yener (CoRR, 2021)

SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models. - Anik Saha; Alex Gittens; Jian Ni; Oktie Hassanzadeh; Bülent Yener; Kavitha Srinivas (CASE@EMNLP, 2022)

TINKER: A framework for Open source Cyberthreat Intelligence. - Nidhi Rastogi; Sharmishtha Dutta; Alex Gittens; Mohammed J. Zaki; Charu C. Aggarwal (TrustCom, 2022)

An Adversarial Perspective on Accuracy, Robustness, Fairness, and Privacy: Multilateral-Tradeoffs in Trustworthy ML. - Alex Gittens; Bülent Yener; Moti Yung (IEEE Access, 2022)

Simple Disentanglement of Style and Content in Visual Representations. - Lilian Ngweta; Subha Maity; Alex Gittens; Yuekai Sun; Mikhail Yurochkin (ICML, 2023)

Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning. - Deniz Koyuncu; Alex Gittens; Bülent Yener; Moti Yung (KDD, 2023)

Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming. - Huzaifa Arif; Alex Gittens; Pin-Yu Chen (SaTML, 2023)

Word Sense Induction with Knowledge Distillation from BERT. - Anik Saha; Alex Gittens; Bülent Yener (CoRR, 2023)

Reduced Label Complexity For Tight 𝓁2 Regression. - Alex Gittens; Malik Magdon-Ismail (CoRR, 2023)

A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction. - Anik Saha; Oktie Hassanzadeh; Alex Gittens; Jian Ni; Kavitha Srinivas; Bülent Yener (CoRR, 2023)

Improving Neural Ranking Models with Traditional IR Methods. - Anik Saha; Oktie Hassanzadeh; Alex Gittens; Jian Ni; Kavitha Srinivas; Bülent Yener (CoRR, 2023)

Aligners: Decoupling LLMs and Alignment. - Lilian Ngweta; Mayank Agarwal; Subha Maity; Alex Gittens; Yuekai Sun; Mikhail Yurochkin (EMNLP (Findings), 2024)

Iterative thresholding for non-linear learning in the strong ε-contamination model. - Arvind Rathnashyam; Alex Gittens (CoRR, 2024)

Exploiting the Data Gap: Utilizing Non-ignorable Missingness to Manipulate Model Learning. - Deniz Koyuncu; Alex Gittens; Bülent Yener; Moti Yung (CoRR, 2024)

Adversarial Missingness Attacks on Causal Structure Learning. - Deniz Koyuncu; Alex Gittens; Bülent Yener; Moti Yung (ACM Trans. Intell. Syst. Technol., 2024)

Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion. - Sharmishtha Dutta; Alex Gittens; Mohammed J. Zaki; Charu C. Aggarwal (AAAI, 2025)

PEEL the Layers and Find Yourself: Revisiting Inference-Time Data Leakage for Residual Neural Networks. - Huzaifa Arif; Keerthiram Murugesan; Payel Das; Alex Gittens; Pin-Yu Chen (SaTML, 2025)

Group Fair Federated Learning via Stochastic Kernel Regularization. - Huzaifa Arif; Pin-Yu Chen; Keerthiram Murugesan; Alex Gittens (Trans. Mach. Learn. Res., 2025)