Fast and Accurate Least-Mean-Squares Solvers. - Alaa Maalouf; Ibrahim Jubran; Dan Feldman (NeurIPS, 2019)

Introduction to Coresets: Accurate Coresets. - Ibrahim Jubran; Alaa Maalouf; Dan Feldman (CoRR, 2019)

Sets Clustering. - Ibrahim Jubran; Murad Tukan; Alaa Maalouf; Dan Feldman (ICML, 2020)

Tight Sensitivity Bounds For Smaller Coresets. - Alaa Maalouf; Adiel Statman; Dan Feldman (KDD, 2020)

Coresets for Near-Convex Functions. - Murad Tukan; Alaa Maalouf; Dan Feldman (NeurIPS, 2020)

Faster PAC Learning and Smaller Coresets via Smoothed Analysis. - Alaa Maalouf; Ibrahim Jubran; Murad Tukan; Dan Feldman (CoRR, 2020)

Compressed Deep Networks: Goodbye SVD, Hello Robust Low-Rank Approximation. - Murad Tukan; Alaa Maalouf; Matan Weksler; Dan Feldman (CoRR, 2020)

Provably Approximated Point Cloud Registration. - Ibrahim Jubran; Alaa Maalouf; Ron Kimmel; Dan Feldman (ICCV, 2021)

Deep Learning meets Projective Clustering. - Alaa Maalouf; Harry Lang; Daniela Rus; Dan Feldman (ICLR, 2021)

Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. - Lucas Liebenwein; Alaa Maalouf; Dan Feldman; Daniela Rus (NeurIPS, 2021)

Provably Approximated ICP. - Ibrahim Jubran; Alaa Maalouf; Ron Kimmel; Dan Feldman (CoRR, 2021)

Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. - Lucas Liebenwein; Alaa Maalouf; Oren Gal; Dan Feldman; Daniela Rus (CoRR, 2021)

Introduction to Coresets: Approximated Mean. - Alaa Maalouf; Ibrahim Jubran; Dan Feldman (CoRR, 2021)

Coresets for the Average Case Error for Finite Query Sets. - Alaa Maalouf; Ibrahim Jubran; Murad Tukan; Dan Feldman (Sensors, 2021)

No Fine-Tuning, No Cry: Robust SVD for Compressing Deep Networks. - Murad Tukan; Alaa Maalouf; Matan Weksler; Dan Feldman (Sensors, 2021)

Overview of accurate coresets. - Ibrahim Jubran; Alaa Maalouf; Dan Feldman (WIREs Data Mining Knowl. Discov., 2021)

Coresets for Data Discretization and Sine Wave Fitting. - Alaa Maalouf; Murad Tukan; Eric Price 001; Daniel M. Kane; Dan Feldman (AISTATS, 2022)

Obstacle Aware Sampling for Path Planning. - Murad Tukan; Alaa Maalouf; Dan Feldman; Roi Poranne (IROS, 2022)

Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions. - Murad Tukan; Loay Mualem; Alaa Maalouf (NeurIPS, 2022)

Fast and Accurate Least-Mean-Squares Solvers for High Dimensional Data. - Alaa Maalouf; Ibrahim Jubran; Dan Feldman (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

MDL-BOOST: Constructing Efficient Machine/Deep Learning Systems via Data and Model Compression. - Alaa Maalouf (2022)

AutoCoreset: An Automatic Practical Coreset Construction Framework. - Alaa Maalouf; Murad Tukan; Vladimir Braverman; Daniela Rus (ICML, 2023)

Provable Data Subset Selection For Efficient Neural Networks Training. - Murad Tukan; Samson Zhou; Alaa Maalouf; Daniela Rus; Vladimir Braverman; Dan Feldman (ICML, 2023)

Deep Learning on Home Drone: Searching for the Optimal Architecture. - Alaa Maalouf; Yotam Gurfinkel; Barak Diker; Oren Gal; Daniela Rus; Dan Feldman (ICRA, 2023)

On the Size and Approximation Error of Distilled Datasets. - Alaa Maalouf; Murad Tukan; Noel Loo; Ramin M. Hasani; Mathias Lechner; Daniela Rus (NeurIPS, 2023)

ConceptFusion: Open-set multimodal 3D mapping. - Krishna Murthy Jatavallabhula; Alihusein Kuwajerwala; Qiao Gu; Mohd. Omama; Ganesh Iyer; Soroush Saryazdi; Tao Chen 046; Alaa Maalouf; Shuang Li 013; Nikhil Varma Keetha; Ayush Tewari; Joshua B. Tenenbaum; et al. (Robotics: Science and Systems, 2023)

Provable Data Subset Selection For Efficient Neural Network Training. - Murad Tukan; Samson Zhou; Alaa Maalouf; Daniela Rus; Vladimir Braverman; Dan Feldman (CoRR, 2023)

On the Size and Approximation Error of Distilled Sets. - Alaa Maalouf; Murad Tukan; Noel Loo; Ramin M. Hasani; Mathias Lechner; Daniela Rus (CoRR, 2023)

Dataset Distillation Meets Provable Subset Selection. - Murad Tukan; Alaa Maalouf; Margarita Osadchy (CoRR, 2023)

Follow Anything: Open-set detection, tracking, and following in real-time. - Alaa Maalouf; Ninad Jadhav; Krishna Murthy Jatavallabhula; Makram Chahine; Daniel M. Vogt; Robert J. Wood; Antonio Torralba 001; Daniela Rus (CoRR, 2023)

Large Scale Dataset Distillation with Domain Shift. - Noel Loo; Alaa Maalouf; Ramin M. Hasani; Mathias Lechner; Alexander Amini; Daniela Rus (ICML, 2024)

Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models. - Tsun-Hsuan Wang; Alaa Maalouf; Wei Xiao 003; Yutong Ban; Alexander Amini; Guy Rosman; Sertac Karaman; Daniela Rus (ICRA, 2024)

Probing Multimodal LLMs as World Models for Driving. - Shiva Sreeram; Tsun-Hsuan Wang; Alaa Maalouf; Guy Rosman; Sertac Karaman; Daniela Rus (CoRR, 2024)

Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models. - Makram Chahine; Alex Quach; Alaa Maalouf; Tsun-Hsuan Wang; Daniela Rus (CoRR, 2024)

Follow Anything: Open-Set Detection, Tracking, and Following in Real-Time. - Alaa Maalouf; Ninad Jadhav; Krishna Murthy Jatavallabhula; Makram Chahine; Daniel M. Vogt; Robert J. Wood; Antonio Torralba 001; Daniela Rus (IEEE Robotics Autom. Lett., 2024)

A Unified Approach to Coreset Learning. - Alaa Maalouf; Gilad Eini; Ben Mussay; Dan Feldman; Margarita Osadchy (IEEE Trans. Neural Networks Learn. Syst., 2024)

DataS^3: Dataset Subset Selection for Specialization. - Neha Hulkund; Alaa Maalouf; Levi Cai; Daniel Yang; Tsun-Hsuan Wang; Abigail O'Neil; Timm Haucke; Sandeep Mukherjee; Vikram Ramaswamy; Judy Hansen Shen; Gabriel Tseng; Mike Walmsley; et al. (CoRR, 2025)

Prompts to Summaries: Zero-Shot Language-Guided Video Summarization. - Mario Barbara; Alaa Maalouf (CoRR, 2025)