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Linear Time Small Coresets for k-Mean Clustering of Segments with Applications, Denisov, D., Dolev, S., Feldman, D. & Segal, M., 2026, WALCOM: Algorithms and Computation - 20th International Conference and Workshops on Algorithms and Computation, WALCOM 2026, Proceedings. Di Giacomo, E. & Mondal, D. (eds.). Springer Science and Business Media Deutschland GmbH, p. 110-124 15 p. (Lecture Notes in Computer Science; vol. 16444 LNCS).
Provable imbalanced point clustering, Denisov, D., Dolev, S., Feldman, D. & Segal, M., 2026, (Accepted/In press) In: Cryptography and Communications.
Turning Big Data Into Tiny Data: Coresets for Unsupervised Learning Problems, Feldman, D., Schmidt, M. & Sohler, C., 6 Nov 2025, In: SIAM Review. 67, 4, p. 801-861 61 p.
Provable Imbalanced Point Clustering, Denisov, D., Feldman, D., Dolev, S. & Segal, M., 2025, Cyber Security, Cryptology, and Machine Learning - 8th International Symposium, CSCML 2024, Proceedings. Dolev, S., Elhadad, M., Kutyłowski, M. & Persiano, G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 79-91 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; vol. 15349 LNCS).
Smart-Init of neural networks, Denisov, D., Feldman, D., Dolev, S. & Segal, M., 2025, Proceedings - 2025 11th International Conference on Computing and Artificial Intelligence, ICCAI 2025. Institute of Electrical and Electronics Engineers Inc., p. 250-255 6 p. (Proceedings - 2025 11th International Conference on Computing and Artificial Intelligence, ICCAI 2025).
Quadrotor with wheels: design and experimental evaluation, Aizelman, I., Magazinnik, D., Feldman, D. & Klein, I., Dec 2024, In: Scientific Reports. 14, 1, 15603.
A Unified Approach to Coreset Learning, Maalouf, A., Eini, G., Mussay, B., Feldman, D. & Osadchy, M., 1 May 2024, In: IEEE Transactions on Neural Networks and Learning Systems. 35, 5, p. 6893-6905 13 p.
Least-Mean-Squares Coresets for Infinite Streams, Braverman, V., Feldman, D., Lang, H., Rus, D. & Statman, A., 1 Sep 2023, In: IEEE Transactions on Knowledge and Data Engineering. 35, 9, p. 8699-8712 14 p.
Deep Learning on Home Drone: Searching for the Optimal Architecture, Maalouf, A., Gurfinkel, Y., Diker, B., Gal, O., Rus, D. & Feldman, D., 2023, Proceedings - ICRA 2023: IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., p. 8208-8215 8 p. (Proceedings - IEEE International Conference on Robotics and Automation; vol. 2023-May).
Provable Data Subset Selection For Efficient Neural Networks Training, Tukan, M., Zhou, S., Maalouf, A., Rus, D., Braverman, V. & Feldman, D., 2023, In: Proceedings of Machine Learning Research. 202, p. 34533-34555 23 p.
Fast and Accurate Least-Mean-Squares Solvers for High Dimensional Data, Maalouf, A., Jubran, I. & Feldman, D., 1 Dec 2022, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 44, 12, p. 9977-9994 18 p.
Data-Independent Structured Pruning of Neural Networks via Coresets, Mussay, B., Feldman, D., Zhou, S., Braverman, V. & Osadchy, M., Dec 2022, In: IEEE Transactions on Neural Networks and Learning Systems. 33, 12, p. 7829-7841 13 p.
Sensitivity-Informed Provable Pruning of Neural Networks, Baykal, C., Liebenwein, L., Gilitschenski, I., Feldman, D. & Rus, D., 2022, In: SIAM Journal on Mathematics of Data Science. 4, 1, p. 26-45 20 p.
Obstacle Aware Sampling for Path Planning, Tukan, M., Maalouf, A., Feldman, D. & Poranne, R., 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. Institute of Electrical and Electronics Engineers Inc., p. 13676-13683 8 p. (IEEE International Conference on Intelligent Robots and Systems; vol. 2022-October).
Coreset for Line-Sets Clustering, Lotan, S., Shayda, E. E. S. & Feldman, D., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).
Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones, Jubran, I., Fares, F., Alfassi, Y., Ayoub, F. & Feldman, D., 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. Institute of Electrical and Electronics Engineers Inc., p. 13363-13370 8 p. (IEEE International Conference on Intelligent Robots and Systems; vol. 2022-October).
Aligning Points to Lines: Provable Approximations, Jubran, I. & Feldman, D., 1 Jan 2022, In: IEEE Transactions on Knowledge and Data Engineering. 34, 1, p. 138-149 12 p.
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more, Tolochinsky, E., Jubran, I. & Feldman, D., 2022, In: Proceedings of Machine Learning Research. 162, p. 21520-21547 28 p.
New Coresets for Projective Clustering and Applications, Tukan, M., Wu, X., Zhou, S., Braverman, V. & Feldman, D., 2022, In: Proceedings of Machine Learning Research. 151, p. 5391-5415 25 p.
Coresets for Data Discretization and Sine Wave Fitting, Maalouf, A., Tukan, M., Price, E., Kane, D. & Feldman, D., 2022, In: Proceedings of Machine Learning Research. 151, p. 10622-10639 18 p.
Overview of accurate coresets, Jubran, I., Maalouf, A. & Feldman, D., 1 Nov 2021, In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 11, 6, p. 1-18
On coresets for support vector machines, Tukan, M., Baykal, C., Feldman, D. & Rus, D., 12 Oct 2021, In: Theoretical Computer Science. 890, p. 171-191 21 p.
Coresets for the average case error for finite query sets, Maalouf, A., Jubran, I., Tukan, M. & Feldman, D., 1 Oct 2021, In: Sensors. 21, 19, 6689.
No fine-tuning, no cry: Robust svd for compressing deep networks, Tukan, M., Maalouf, A., Weksler, M. & Feldman, D., 2 Aug 2021, In: Sensors. 21, 16, p. 5599
DEEP LEARNING MEETS PROJECTIVE CLUSTERING, Maalouf, A., Lang, H., Rus, D. & Feldman, D., 2021.
Efficient Coreset Constructions via Sensitivity Sampling, Braverman, V., Feldman, D., Lang, H., Statman, A. & Zhou, S., 2021, In: Proceedings of Machine Learning Research. 157, p. 948-963 16 p.
Coresets for Decision Trees of Signals, Jubran, I., Sanches Shayda, E. E., Newman, I. & Feldman, D., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 30352-30364 13 p. (Advances in Neural Information Processing Systems; vol. 36).
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition, Liebenwein, L., Maalouf, A., Gal, O., Feldman, D. & Rus, D., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 5328-5344 17 p. (Advances in Neural Information Processing Systems; vol. 7).
SiPPing Neural Networks - Sensitivity-informed Provable Pruning of Neural Networks., Baykal, C., Liebenwein, L., Gilitschenski, I., Feldman, D. & Rus, D., 2021, In: SIAM Journal on Mathematics of Data Science.
Provably Approximated Point Cloud Registration, Jubran, I., Maalouf, A., Kimmel, R. & Feldman, D., 2021, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’21) . Institute of Electrical and Electronics Engineers Inc., p. 13269-13278 10 p. (Proceedings of the IEEE International Conference on Computer Vision).
Resolving battery status and customer matching to create 24/7 drones based advertisement system, Danial, J., Ben Asher, Y. & Feldman, D., 2021, Proceedings - 2021 5th IEEE International Conference on Robotic Computing, IRC 2021. Institute of Electrical and Electronics Engineers Inc., p. 131-136 6 p. (Proceedings - 2021 5th IEEE International Conference on Robotic Computing, IRC 2021).
Sphere fitting with applications to machine tracking, Epstein, D. & Feldman, D., Aug 2020, In: Algorithms. 13, 8, 18 p., 177.
Sets Clustering, Jubran, I., Tukan, M., Maalouf, A. & Feldman, D., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 4961-4972 12 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-7).
DATA-INDEPENDENT NEURAL PRUNING VIA CORESETS, Mussay, B., Osadchy, M., Braverman, V., Zhou, S. & Feldman, D., 2020.
Sets Clustering, Jubran, I., Tukan, M., Maalouf, A. & Feldman, D., 2020, In: Proceedings of Machine Learning Research. 119
Data-Independent Structured Pruning of Neural Networks via Coresets, Mussay, B., Feldman, D., Zhou, S., Braverman, V. & Osadchy, M., 2020, 8th International Conference on Learning Representations, ICLR 2020. 24 p.
Sets Clustering, Jubran, I., Tukan, M., Maalouf, A. & Feldman, D., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 4961-4972 12 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-7).
Measuring and Protecting Privacy in the Always-On Era, Feldman, D. & Haber, E., 2020, In: Berkeley Technology Law Journal. 35, 1, p. 197-249 53 p.
Core-Sets: Updated Survey, Feldman, D., 2020, Sampling Techniques for Supervised or Unsupervised Tasks. Ros, F. & Guillaume, S. (eds.). Cham: Springer International Publishing, p. 23-44 22 p.
Secure k-ish Nearest Neighbors Classifier, Shaul, H., Feldman, D. & Rus, D., 2020, In: Proceedings on Privacy Enhancing Technologies. 2020, 3, p. 42-61 20 p.
Coresets for near-convex functions, Tukan, M., Maalouf, A. & Feldman, D., 2020, Conference on Neural Information Processing Systems (NeurIPS, formerly NIPS). Vol. 2020-December. (Advances in Neural Information Processing Systems).
PROVABLE FILTER PRUNING FOR EFFICIENT NEURAL NETWORKS, Liebenwein, L., Baykal, C., Lang, H., Feldman, D. & Rus, D., 2020.
Finding patterns in signals using lossy text compression, Rozenberg, L., Lotan, S. & Feldman, D., 1 Feb 2019, In: Algorithms. 12, 2, 18 p., 267.
Secure data retrieval on the cloud homomorphic encryption meets coresets, Akavia, A., Feldman, D. & Shaul, H., 2019, In: IACR Transactions on Cryptographic Hardware and Embedded Systems. 2019, 2, p. 80-106 27 p.
Training Gaussian mixture models at scale via coresets, Lucic, M., Faulkner, M., Krause, A. & Feldman, D., 1 May 2018, In: Journal of Machine Learning Research. 18, p. 1-25 25 p.
Coresets for vector summarization with applications to network graphs, Feldman, D., Ozer, S. & Rus, D., 2017, 34th International Conference on Machine Learning, ICML 2017. International Machine Learning Society (IMLS), p. 1847-1855 9 p. (34th International Conference on Machine Learning, ICML 2017; vol. 3).
IDiary: From GPS signals to a text-searchable diary, Feldman, D., Sugaya, A., Sung, C. & Rus, D., 11 Nov 2013, SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, p. 6:1-6:12 6. (SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems).
K-robots clustering of moving sensors using coresets, Feldman, D., Gil, S., Knepper, R. A., Julian, B. & Rus, D., 2013, 2013 IEEE International Conference on Robotics and Automation, ICRA 2013. p. 881-888 8 p. 6630677. (Proceedings - IEEE International Conference on Robotics and Automation).
Scalable training of mixture models via coresets, Feldman, D., Faulkner, M. & Krause, A., 2011, Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011. Neural Information Processing Systems, (Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011).
Using all sky cameras to determine cloud statistics for the thirty meter telescope candidate sites, Skidmore, W., Schöck, M., Magnier, E., Walker, D., Feldman, D., Riddle, R., Els, S., Travouillon, T., Bustos, E., Seguel, J., Vasquez, J., Blum, R., Gillett, P. & Gregory, B., 2008, Ground-based and Airborne Telescopes II. 701224. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 7012).

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