2024
Adversarial Missingness Attacks on Causal Structure Learning.
ACM Trans. Intell. Syst. Technol., December, 2024
Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion.
CoRR, 2024
Exploiting the Data Gap: Utilizing Non-ignorable Missingness to Manipulate Model Learning.
CoRR, 2024
Iterative thresholding for non-linear learning in the strong ε-contamination model.
CoRR, 2024
Aligners: Decoupling LLMs and Alignment.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
2023
Improving Neural Ranking Models with Traditional IR Methods.
CoRR, 2023
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction.
CoRR, 2023
Reduced Label Complexity For Tight 𝓁<sub>2</sub> Regression.
CoRR, 2023
Word Sense Induction with Knowledge Distillation from BERT.
CoRR, 2023
Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming.
Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023
Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
Simple Disentanglement of Style and Content in Visual Representations.
Proceedings of the International Conference on Machine Learning, 2023
2022
An Adversarial Perspective on Accuracy, Robustness, Fairness, and Privacy: Multilateral-Tradeoffs in Trustworthy ML.
IEEE Access, 2022
TINKER: A framework for Open source Cyberthreat Intelligence.
Proceedings of the IEEE International Conference on Trust, 2022
SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models.
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, 2022
2021
Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models.
CoRR, 2021
Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation.
CoRR, 2021
Learning Fair Canonical Polyadical Decompositions using a Kernel Independence Criterion.
CoRR, 2021
Sparse Graph Based Sketching for Fast Numerical Linear Algebra.
Proceedings of the IEEE International Conference on Acoustics, 2021
2020
MALOnt: An Ontology for Malware Threat Intelligence.
CoRR, 2020
NoisyCUR: An Algorithm for Two-Cost Budgeted Matrix Completion.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020
Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition.
Proceedings of the 37th International Conference on Machine Learning, 2020
2019
Scalable Kernel K-Means Clustering with Nystr\"om Approximation: Relative-Error Bounds.
J. Mach. Learn. Res., 2019
Group Collaborative Representation for Image Set Classification.
Int. J. Comput. Vis., 2019
Fast Fixed Dimension L2-Subspace Embeddings of Arbitrary Accuracy, With Application to L1 and L2 Tasks.
CoRR, 2019
Alchemist: An Apache Spark ⇔ MPI interface.
Concurr. Comput. Pract. Exp., 2019
2018
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018
Accelerating a Distributed CPD Algorithm for Large Dense, Skewed Tensors.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018
2017
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging.
J. Mach. Learn. Res., 2017
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds.
CoRR, 2017
Breaking Locality Accelerates Block Gauss-Seidel.
Proceedings of the 34th International Conference on Machine Learning, 2017
Skip-Gram - Zipf + Uniform = Vector Additivity.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017
2016
Revisiting the Nystrom Method for Improved Large-scale Machine Learning.
J. Mach. Learn. Res., 2016
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies.
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CoRR, 2016
A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark.
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Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, 2016
Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies.
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Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016
2015
Tensor machines for learning target-specific polynomial features.
CoRR, 2015
Spectral Clustering via the Power Method - Provably.
Proceedings of the 32nd International Conference on Machine Learning, 2015
Hardware compliant approximate image codes.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015
2014
Compact Random Feature Maps.
Proceedings of the 31th International Conference on Machine Learning, 2014
2013
Improved Matrix Algorithms via the Subsampled Randomized Hadamard Transform.
SIAM J. Matrix Anal. Appl., 2013
Approximate Spectral Clustering via Randomized Sketching.
CoRR, 2013
2011
The spectral norm error of the naive Nystrom extension
CoRR, 2011