Coresets forWeighted Facilities and Their Applications. - Dan Feldman; Amos Fiat; Micha Sharir (FOCS, 2006)

Bi-criteria linear-time approximations for generalized k-mean/median/center. - Dan Feldman; Amos Fiat; Micha Sharir; Danny Segev (SCG, 2007)

A PTAS for k-means clustering based on weak coresets. - Dan Feldman; Morteza Monemizadeh; Christian Sohler (SCG, 2007)

Private coresets. - Dan Feldman; Amos Fiat; Haim Kaplan; Kobbi Nissim (STOC, 2009)

Coresets and Sketches for High Dimensional Subspace Approximation Problems. - Dan Feldman; Morteza Monemizadeh; Christian Sohler; David P. Woodruff (SODA, 2010)

Coresets and their applications - Dan Feldman (2010)

Scalable Training of Mixture Models via Coresets. - Dan Feldman; Matthew Faulkner 001; Andreas Krause 001 (NIPS, 2011)

From High Definition Image to Low Space Optimization. - Micha Feigin; Dan Feldman; Nir A. Sochen (SSVM, 2011)

A unified framework for approximating and clustering data. - Dan Feldman; Michael Langberg (STOC, 2011)

The single pixel GPS: learning big data signals from tiny coresets. - Dan Feldman; Cynthia R. Sung; Daniela Rus (SIGSPATIAL/GIS, 2012)

An effective coreset compression algorithm for large scale sensor networks. - Dan Feldman; Andrew Sugaya; Daniela Rus (IPSN, 2012)

Communication coverage for independently moving robots. - Stephanie Gil; Dan Feldman; Daniela Rus (IROS, 2012)

Trajectory clustering for motion prediction. - Cynthia R. Sung; Dan Feldman; Daniela Rus (IROS, 2012)

Data reduction for weighted and outlier-resistant clustering. - Dan Feldman; Leonard J. Schulman (SODA, 2012)

K-robots clustering of moving sensors using coresets. - Dan Feldman; Stephanie Gil; Ross A. Knepper; Brian J. Julian; Daniela Rus (ICRA, 2013)

Turning big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering. - Dan Feldman; Melanie Schmidt 001; Christian Sohler (SODA, 2013)

Learning Big (Image) Data via Coresets for Dictionaries. - Dan Feldman; Micha Feigin; Nir A. Sochen (J. Math. Imaging Vis., 2013)

Smallest enclosing ball for probabilistic data. - Alexander Munteanu; Christian Sohler; Dan Feldman (SoCG, 2014)

Visual precis generation using coresets. - Rohan Paul; Dan Feldman; Daniela Rus; Paul Newman 001 (ICRA, 2014)

Coresets for k-Segmentation of Streaming Data. - Guy Rosman; Mikhail Volkov 002; Dan Feldman; John W. Fisher III; Daniela Rus (NIPS, 2014)

Coresets for visual summarization with applications to loop closure. - Mikhail Volkov 002; Guy Rosman; Dan Feldman; John W. Fisher III; Daniela Rus (ICRA, 2015)

Fleye on the car: big data meets the internet of things. - Soliman Nasser; Andew Barry; Marek Doniec; Guy Peled; Guy Rosman; Daniela Rus; Mikhail Volkov 002; Dan Feldman (IPSN, 2015)

More Constraints, Smaller Coresets: Constrained Matrix Approximation of Sparse Big Data. - Dan Feldman; Tamir Tassa (KDD, 2015)

k-Means for Streaming and Distributed Big Sparse Data. - Artem Barger; Dan Feldman (CoRR, 2015)

Low-cost and Faster Tracking Systems Using Core-sets for Pose-Estimation. - Soliman Nasser; Ibrahim Jubran; Dan Feldman (CoRR, 2015)

iDiary: From GPS Signals to a Text- Searchable Diary. - Dan Feldman; Cynthia R. Sung; Andrew Sugaya; Daniela Rus (ACM Trans. Sens. Networks, 2015)

Dimensionality Reduction of Massive Sparse Datasets Using Coresets. - Dan Feldman; Mikhail Volkov 002; Daniela Rus (NIPS, 2016)

k-Means for Streaming and Distributed Big Sparse Data. - Artem Barger; Dan Feldman (SDM, 2016)

New Frameworks for Offline and Streaming Coreset Constructions. - Vladimir Braverman; Dan Feldman; Harry Lang (CoRR, 2016)

Automatic alignment of geographic features in contemporary vector data and historical maps. - Weiwei Duan; Yao-Yi Chiang; Craig A. Knoblock; Vinil Jain; Dan Feldman; Johannes H. Uhl; Stefan Leyk (GeoAI@SIGSPATIAL, 2017)

Coresets for Vector Summarization with Applications to Network Graphs. - Dan Feldman; Sedat Ozer; Daniela Rus (ICML, 2017)

Coresets for differentially private k-means clustering and applications to privacy in mobile sensor networks. - Dan Feldman; Chongyuan Xiang; Ruihao Zhu; Daniela Rus (IPSN, 2017)

Cloud computing for big data analytics in the Process Control Industry. - Ephraim Goldin; Dan Feldman; George K. Georgoulas; Miguel Castaño; George Nikolakopoulos (MED, 2017)

Secure Search on the Cloud via Coresets and Sketches. - Adi Akavia; Dan Feldman; Hayim Shaul (CoRR, 2017)

Training Gaussian Mixture Models at Scale via Coresets. - Mario Lucic; Matthew Faulkner 001; Andreas Krause 001; Dan Feldman (J. Mach. Learn. Res., 2017)

Secure Search on Encrypted Data via Multi-Ring Sketch. - Adi Akavia; Dan Feldman; Hayim Shaul (CCS, 2018)

Scalable Secure Computation of Statistical Functions with Applications to k-Nearest Neighbors. - Hayim Shaul; Dan Feldman; Daniela Rus (CoRR, 2018)

Coresets For Monotonic Functions with Applications to Deep Learning. - Elad Tolochinsky; Dan Feldman (CoRR, 2018)

Minimizing Sum of Non-Convex but Piecewise log-Lipschitz Functions using Coresets. - Ibrahim Jubran; Dan Feldman (CoRR, 2018)

Secure Search via Multi-Ring Fully Homomorphic Encryption. - Adi Akavia; Dan Feldman; Hayim Shaul (IACR Cryptol. ePrint Arch., 2018)

Quadcopter Tracks Quadcopter via Real-Time Shape Fitting. - Dror Epstein; Dan Feldman (IEEE Robotics Autom. Lett., 2018)

Streaming Coreset Constructions for M-Estimators. - Vladimir Braverman; Dan Feldman; Harry Lang; Daniela Rus (APPROX-RANDOM, 2019)

Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds. - Cenk Baykal; Lucas Liebenwein; Igor Gilitschenski; Dan Feldman; Daniela Rus (ICLR (Poster), 2019)

An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models. - Daniel Garijo; Deborah Khider; Varun Ratnakar; Yolanda Gil; Ewa Deelman; Rafael Ferreira da Silva; Craig A. Knoblock; Yao-Yi Chiang; Minh Pham 004; Jay Pujara; Binh Vu; Dan Feldman; et al. (IUI Companion, 2019)

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

k-Means Clustering of Lines for Big Data. - Yair Marom; Dan Feldman (NeurIPS, 2019)

Deterministic Coresets for Stochastic Matrices with Applications to Scalable Sparse PageRank. - Harry Lang; Cenk Baykal; Najib Abu Samra; Tony Tannous; Dan Feldman; Daniela Rus (TAMC, 2019)

Finding Patterns in Signals Using Lossy Text Compression. - Liat Rozenberg; Sagi Lotan; Dan Feldman (Algorithms, 2019)

Provable Approximations for Constrained $ ell_p$ Regression. - Ibrahim Jubran; David Cohn; Dan Feldman (CoRR, 2019)

Coresets for Gaussian Mixture Models of Any Shape. - Dan Feldman; Zahi Kfir; Xuan Wu 002 (CoRR, 2019)

On Activation Function Coresets for Network Pruning. - Ben Mussay; Samson Zhou; Vladimir Braverman; Dan Feldman (CoRR, 2019)

SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks. - Cenk Baykal; Lucas Liebenwein; Igor Gilitschenski; Dan Feldman; Daniela Rus (CoRR, 2019)

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

Position Estimation of Moving Objects: Practical Provable Approximation. - Jeryes Danial; Dan Feldman; Ariel Hutterer (IEEE Robotics Autom. Lett., 2019)

Secure Data Retrieval on the Cloud: Homomorphic Encryption meets Coresets. - Adi Akavia; Dan Feldman; Hayim Shaul (IACR Trans. Cryptogr. Hardw. Embed. Syst., 2019)

Provable Filter Pruning for Efficient Neural Networks. - Lucas Liebenwein; Cenk Baykal; Harry Lang; Dan Feldman; Daniela Rus (ICLR, 2020)

Data-Independent Neural Pruning via Coresets. - Ben Mussay; Margarita Osadchy; Vladimir Braverman; Samson Zhou; Dan Feldman (ICLR, 2020)

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)

Deterministic Coresets for k-Means of Big Sparse Data †. - Artem Barger; Dan Feldman (Algorithms, 2020)

Sphere Fitting with Applications to Machine Tracking. - Dror Epstein; Dan Feldman (Algorithms, 2020)

k-Means: Outliers-Resistant Clustering+++. - Adiel Statman; Liat Rozenberg; Dan Feldman (Algorithms, 2020)

Sparse Coresets for SVD on Infinite Streams. - Vladimir Braverman; Dan Feldman; Harry Lang; Daniela Rus; Adiel Statman (CoRR, 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)

Introduction to Core-sets: an Updated Survey. - Dan Feldman (CoRR, 2020)

Faster Projective Clustering Approximation of Big Data. - Adiel Statman; Liat Rozenberg; Dan Feldman (CoRR, 2020)

Real-time EEG classification via coresets for BCI applications. - Eitan Netzer; Alex Frid; Dan Feldman (Eng. Appl. Artif. Intell., 2020)

Secure k-ish Nearest Neighbors Classifier. - Hayim Shaul; Dan Feldman; Daniela Rus (Proc. Priv. Enhancing Technol., 2020)

Autonomous Toy Drone via Coresets for Pose Estimation. - Soliman Nasser; Ibrahim Jubran; Dan Feldman (Sensors, 2020)

Turning Big Data Into Tiny Data: Constant-Size Coresets for k-Means, PCA, and Projective Clustering. - Dan Feldman; Melanie Schmidt 001; Christian Sohler (SIAM J. Comput., 2020)

Core-sets: An updated survey. - Dan Feldman (WIREs Data Mining Knowl. Discov., 2020)

Efficient Coreset Constructions via Sensitivity Sampling. - Vladimir Braverman; Dan Feldman; Harry Lang; Adiel Statman; Samson Zhou (ACML, 2021)

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)

Resolving battery status and customer matching to create 24/7 drones based advertisement system. - Jeryes Danial; Yosi Ben-Asher; Dan Feldman (IRC, 2021)

Coresets for Decision Trees of Signals. - Ibrahim Jubran; Ernesto Evgeniy Sanches Shayda; Ilan Newman; Dan Feldman (NeurIPS, 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)

Low-Regret Active learning. - Cenk Baykal; Lucas Liebenwein; Dan Feldman; Daniela Rus (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)

On coresets for support vector machines. - Murad Tukan; Cenk Baykal; Dan Feldman; Daniela Rus (Theor. Comput. Sci., 2021)

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. - Yolanda Gil; Daniel Garijo; Deborah Khider; Craig A. Knoblock; Varun Ratnakar; Maximiliano Osorio; Hernán Vargas; Minh Pham 004; Jay Pujara; Basel Shbita; Binh Vu; Yao-Yi Chiang; et al. (ACM Trans. Interact. Intell. Syst., 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)

New Coresets for Projective Clustering and Applications. - Murad Tukan; Xuan Wu 002; Samson Zhou; Vladimir Braverman; Dan Feldman (AISTATS, 2022)

Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more. - Elad Tolochinsky; Ibrahim Jubran; Dan Feldman (ICML, 2022)

Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones. - Ibrahim Jubran; Fares Fares; Yuval Alfassi; Firas Ayoub; Dan Feldman (IROS, 2022)

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

Coreset for Line-Sets Clustering. - Sagi Lotan; Ernesto Evgeniy Sanches Shayda; Dan Feldman (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)

Sensitivity-Informed Provable Pruning of Neural Networks. - Cenk Baykal; Lucas Liebenwein; Igor Gilitschenski; Dan Feldman; Daniela Rus (SIAM J. Math. Data Sci., 2022)

Aligning Points to Lines: Provable Approximations. - Ibrahim Jubran; Dan Feldman (IEEE Trans. Knowl. Data Eng., 2022)

Data-Independent Structured Pruning of Neural Networks via Coresets. - Ben Mussay; Dan Feldman; Samson Zhou; Vladimir Braverman; Margarita Osadchy (IEEE Trans. Neural Networks Learn. Syst., 2022)

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)

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

Affective Polarization in Social Networks. - Dan Feldman; Ashwin Rao; Zihao He; Kristina Lerman (CoRR, 2023)

ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration. - Murad Tukan; Fares Fares; Yotam Grufinkle; Ido Talmor; Loay Mualem; Vladimir Braverman; Dan Feldman (CoRR, 2023)

Least-Mean-Squares Coresets for Infinite Streams. - Vladimir Braverman; Dan Feldman; Harry Lang; Daniela Rus; Adiel Statman (IEEE Trans. Knowl. Data Eng., 2023)

Provable Imbalanced Point Clustering. - David Denisov; Dan Feldman; Shlomi Dolev; Michael Segal 001 (CSCML, 2024)

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

k-PCA for (non-squared) Euclidean Distances: Polynomial Time Approximation. - Daniel Greenhut; Dan Feldman (CoRR, 2025)