Hussein Hazimeh

Orcid: 0000-0003-4501-0678

Affiliations:
  • Google Research
  • Massachusetts Institute of Technology, Cambridge, MA, USA
  • University of Illinois at Urbana-Champaign, Urbana, IL, USA (former)


According to our database1, Hussein Hazimeh authored at least 20 papers between 2015 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2025
An Optimization Framework for Differentially Private Sparse Fine-Tuning.
CoRR, March, 2025

DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation.
Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2025

Scaling Laws for Downstream Task Performance in Machine Translation.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024
Scaling Laws for Downstream Task Performance of Large Language Models.
CoRR, 2024

OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization.
J. Mach. Learn. Res., 2023

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy.
J. Artif. Intell. Res., 2023

Benchmarking Robustness to Adversarial Image Obfuscations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Fast as CHITA: Neural Network Pruning with Combinatorial Optimization.
Proceedings of the International Conference on Machine Learning, 2023

Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Sparse regression at scale: branch-and-bound rooted in first-order optimization.
Math. Program., 2022

Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives.
J. Mach. Learn. Res., 2021

Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives.
CoRR, 2021

DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms.
Oper. Res., 2020

The Tree Ensemble Layer: Differentiability meets Conditional Computation.
Proceedings of the 37th International Conference on Machine Learning, 2020

Learning Hierarchical Interactions at Scale: A Convex Optimization Approach.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2015
Axiomatic Analysis of Smoothing Methods in Language Models for Pseudo-Relevance Feedback.
Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 2015


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