Axiomatic Analysis of Smoothing Methods in Language Models for Pseudo-Relevance Feedback. - Hussein Hazimeh 001; ChengXiang Zhai (ICTIR, 2015)

Learning Hierarchical Interactions at Scale: A Convex Optimization Approach. - Hussein Hazimeh 001; Rahul Mazumder (AISTATS, 2020)

The Tree Ensemble Layer: Differentiability meets Conditional Computation. - Hussein Hazimeh 001; Natalia Ponomareva 001; Petros Mol; Zhenyu Tan; Rahul Mazumder (ICML, 2020)

Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms. - Hussein Hazimeh 001; Rahul Mazumder (Oper. Res., 2020)

DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. - Hussein Hazimeh 001; Zhe Zhao 001; Aakanksha Chowdhery; Maheswaran Sathiamoorthy; Yihua Chen; Rahul Mazumder; Lichan Hong; Ed H. Chi (NeurIPS, 2021)

Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives. - Hussein Hazimeh 001; Rahul Mazumder; Peter Radchenko (CoRR, 2021)

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives. - Antoine Dedieu; Hussein Hazimeh 001; Rahul Mazumder (J. Mach. Learn. Res., 2021)

Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles. - Shibal Ibrahim; Hussein Hazimeh 001; Rahul Mazumder (KDD, 2022)

Sparse regression at scale: branch-and-bound rooted in first-order optimization. - Hussein Hazimeh 001; Rahul Mazumder; Ali Saab (Math. Program., 2022)

Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets. - Hussein Hazimeh 001; Natalia Ponomareva 001 (AISTATS, 2023)

Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. - Riade Benbaki; Wenyu Chen 003; Xiang Meng 004; Hussein Hazimeh 001; Natalia Ponomareva 001; Zhe Zhao 001; Rahul Mazumder (ICML, 2023)

COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search. - Shibal Ibrahim; Wenyu Chen 003; Hussein Hazimeh 001; Natalia Ponomareva 001; Zhe Zhao 001; Rahul Mazumder (KDD, 2023)

Benchmarking Robustness to Adversarial Image Obfuscations. - Florian Stimberg; Ayan Chakrabarti; Chun-Ta Lu; Hussein Hazimeh 001; Otilia Stretcu; Wei Qiao; Yintao Liu; Merve Kaya; Cyrus Rashtchian; Ariel Fuxman; Mehmet Tek; Sven Gowal (NeurIPS, 2023)

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. - Natalia Ponomareva 001; Hussein Hazimeh 001; Alex Kurakin; Zheng Xu 002; Carson Denison; H. Brendan McMahan; Sergei Vassilvitskii; Steve Chien; Abhradeep Guha Thakurta (J. Artif. Intell. Res., 2023)

L0Learn: A Scalable Package for Sparse Learning using L0 Regularization. - Hussein Hazimeh 001; Rahul Mazumder; Tim Nonet (J. Mach. Learn. Res., 2023)

OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization. - Xiang Meng 004; Shibal Ibrahim; Kayhan Behdin; Hussein Hazimeh 001; Natalia Ponomareva 001; Rahul Mazumder (ICML, 2024)

Scaling Laws for Downstream Task Performance of Large Language Models. - Berivan Isik; Natalia Ponomareva 001; Hussein Hazimeh 001; Dimitris Paparas; Sergei Vassilvitskii; Sanmi Koyejo (CoRR, 2024)

Scaling Laws for Downstream Task Performance in Machine Translation. - Berivan Isik; Natalia Ponomareva 001; Hussein Hazimeh 001; Dimitris Paparas; Sergei Vassilvitskii; Sanmi Koyejo (ICLR, 2025)

DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation. - Yunjuan Wang; Hussein Hazimeh 001; Natalia Ponomareva 001; Alexey Kurakin; Ibrahim Hammoud; Raman Arora (SaTML, 2025)

An Optimization Framework for Differentially Private Sparse Fine-Tuning. - Mehdi Makni; Kayhan Behdin; Gabriel Afriat; Zheng Xu 002; Sergei Vassilvitskii; Natalia Ponomareva 001; Hussein Hazimeh 001; Rahul Mazumder (CoRR, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)