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 18 papers between 2015 and 2024.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation.
CoRR, 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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