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2020 – today
- 2024
[j26]Alaa Maalouf
, Gilad Eini, Ben Mussay, Dan Feldman, Margarita Osadchy:
A Unified Approach to Coreset Learning. IEEE Trans. Neural Networks Learn. Syst. 35(5): 6893-6905 (2024)- [c56]David Denisov, Dan Feldman, Shlomi Dolev, Michael Segal:
Provable Imbalanced Point Clustering. CSCML 2024: 79-91 - [i50]David Denisov, Dan Feldman, Shlomi Dolev, Michael Segal:
Provable Imbalanced Point Clustering. CoRR abs/2408.14225 (2024) - 2023
- [j25]Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, Adiel Statman:
Least-Mean-Squares Coresets for Infinite Streams. IEEE Trans. Knowl. Data Eng. 35(9): 8699-8712 (2023) - [c55]Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman:
Provable Data Subset Selection For Efficient Neural Networks Training. ICML 2023: 34533-34555 - [c54]Alaa Maalouf, Yotam Gurfinkel, Barak Diker, Oren Gal, Daniela Rus, Dan Feldman:
Deep Learning on Home Drone: Searching for the Optimal Architecture. ICRA 2023: 8208-8215 - [i49]Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman:
Provable Data Subset Selection For Efficient Neural Network Training. CoRR abs/2303.05151 (2023) - [i48]Dan Feldman, Ashwin Rao, Zihao He, Kristina Lerman:
Affective Polarization in Social Networks. CoRR abs/2310.18553 (2023) - [i47]Murad Tukan, Fares Fares, Yotam Grufinkle, Ido Talmor, Loay Mualem, Vladimir Braverman, Dan Feldman:
ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration. CoRR abs/2312.13385 (2023) - 2022
- [j24]Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Fast and Accurate Least-Mean-Squares Solvers for High Dimensional Data. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9977-9994 (2022) - [j23]Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus:
Sensitivity-Informed Provable Pruning of Neural Networks. SIAM J. Math. Data Sci. 4(1): 26-45 (2022) - [j22]Ibrahim Jubran, Dan Feldman:
Aligning Points to Lines: Provable Approximations. IEEE Trans. Knowl. Data Eng. 34(1): 138-149 (2022) - [j21]Ben Mussay, Dan Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy:
Data-Independent Structured Pruning of Neural Networks via Coresets. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7829-7841 (2022) - [c53]Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman:
New Coresets for Projective Clustering and Applications. AISTATS 2022: 5391-5415 - [c52]Alaa Maalouf, Murad Tukan, Eric Price, Daniel M. Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. AISTATS 2022: 10622-10639 - [c51]Elad Tolochinsky, Ibrahim Jubran, Dan Feldman:
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more. ICML 2022: 21520-21547 - [c50]Ibrahim Jubran, Fares Fares, Yuval Alfassi, Firas Ayoub, Dan Feldman:
Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones. IROS 2022: 13363-13370 - [c49]Murad Tukan, Alaa Maalouf, Dan Feldman, Roi Poranne:
Obstacle Aware Sampling for Path Planning. IROS 2022: 13676-13683 - [c48]Sagi Lotan, Ernesto Evgeniy Sanches Shayda, Dan Feldman:
Coreset for Line-Sets Clustering. NeurIPS 2022 - [i46]Ibrahim Jubran, Fares Fares, Yuval Alfassi, Firas Ayoub, Dan Feldman:
Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones. CoRR abs/2203.02686 (2022) - [i45]Alaa Maalouf, Murad Tukan, Eric Price, Daniel Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. CoRR abs/2203.03009 (2022) - [i44]Murad Tukan, Alaa Maalouf, Dan Feldman, Roi Poranne:
Obstacle Aware Sampling for Path Planning. CoRR abs/2203.04075 (2022) - [i43]Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman:
New Coresets for Projective Clustering and Applications. CoRR abs/2203.04370 (2022) - [i42]Alaa Maalouf, Yotam Gurfinkel, Barak Diker, Oren Gal, Daniela Rus, Dan Feldman:
Deep Learning on Home Drone: Searching for the Optimal Architecture. CoRR abs/2209.11064 (2022) - 2021
- [j20]Murad Tukan, Alaa Maalouf, Matan Weksler, Dan Feldman:
No Fine-Tuning, No Cry: Robust SVD for Compressing Deep Networks. Sensors 21(16): 5599 (2021) - [j19]Alaa Maalouf, Ibrahim Jubran, Murad Tukan, Dan Feldman:
Coresets for the Average Case Error for Finite Query Sets. Sensors 21(19): 6689 (2021) - [j18]Murad Tukan, Cenk Baykal, Dan Feldman, Daniela Rus:
On coresets for support vector machines. Theor. Comput. Sci. 890: 171-191 (2021) - [j17]Yolanda Gil, Daniel Garijo, Deborah Khider, Craig A. Knoblock, Varun Ratnakar, Maximiliano Osorio, Hernán Vargas, Minh Pham, Jay Pujara, Basel Shbita, Binh Vu, Yao-Yi Chiang, Dan Feldman, Yijun Lin, Hayley Song, Vipin Kumar, Ankush Khandelwal, Michael S. Steinbach, Kshitij Tayal, Shaoming Xu, Suzanne A. Pierce, Lissa Pearson, Daniel Hardesty-Lewis, Ewa Deelman, Rafael Ferreira da Silva, Rajiv Mayani, Armen R. Kemanian, Yuning Shi, Lorne Leonard, Scott D. Peckham, Maria Stoica, Kelly M. Cobourn, Zeya Zhang, Christopher J. Duffy, Lele Shu:
Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. ACM Trans. Interact. Intell. Syst. 11(2): 11:1-11:49 (2021) - [j16]Ibrahim Jubran, Alaa Maalouf, Dan Feldman:
Overview of accurate coresets. WIREs Data Mining Knowl. Discov. 11(6) (2021) - [c47]Vladimir Braverman, Dan Feldman, Harry Lang, Adiel Statman, Samson Zhou:
Efficient Coreset Constructions via Sensitivity Sampling. ACML 2021: 948-963 - [c46]Ibrahim Jubran, Alaa Maalouf, Ron Kimmel, Dan Feldman:
Provably Approximated Point Cloud Registration. ICCV 2021: 13249-13258 - [c45]Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman:
Deep Learning meets Projective Clustering. ICLR 2021 - [c44]Jeryes Danial, Yosi Ben-Asher, Dan Feldman:
Resolving battery status and customer matching to create 24/7 drones based advertisement system. IRC 2021: 131-136 - [c43]Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus:
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. NeurIPS 2021: 5328-5344 - [c42]Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan Newman, Dan Feldman:
Coresets for Decision Trees of Signals. NeurIPS 2021: 30352-30364 - [i41]Ibrahim Jubran, Alaa Maalouf, Ron Kimmel, Dan Feldman:
Provably Approximated ICP. CoRR abs/2101.03588 (2021) - [i40]Cenk Baykal, Lucas Liebenwein, Dan Feldman, Daniela Rus:
Low-Regret Active learning. CoRR abs/2104.02822 (2021) - [i39]Lucas Liebenwein, Alaa Maalouf, Oren Gal, Dan Feldman, Daniela Rus:
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. CoRR abs/2107.11442 (2021) - [i38]Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan Newman, Dan Feldman:
Coresets for Decision Trees of Signals. CoRR abs/2110.03195 (2021) - [i37]Alaa Maalouf, Gilad Eini, Ben Mussay, Dan Feldman, Margarita Osadchy:
A Unified Approach to Coreset Learning. CoRR abs/2111.03044 (2021) - [i36]Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Introduction to Coresets: Approximated Mean. CoRR abs/2111.03046 (2021) - 2020
- [j15]Artem Barger, Dan Feldman:
Deterministic Coresets for k-Means of Big Sparse Data †. Algorithms 13(4): 92 (2020) - [j14]Dror Epstein, Dan Feldman:
Sphere Fitting with Applications to Machine Tracking. Algorithms 13(8): 177 (2020) - [j13]Adiel Statman, Liat Rozenberg, Dan Feldman:
k-Means: Outliers-Resistant Clustering+++. Algorithms 13(12): 311 (2020) - [j12]Eitan Netzer, Alex Frid, Dan Feldman:
Real-time EEG classification via coresets for BCI applications. Eng. Appl. Artif. Intell. 89: 103455 (2020) - [j11]Hayim Shaul, Dan Feldman, Daniela Rus:
Secure k-ish Nearest Neighbors Classifier. Proc. Priv. Enhancing Technol. 2020(3): 42-61 (2020) - [j10]Soliman Nasser, Ibrahim Jubran, Dan Feldman:
Autonomous Toy Drone via Coresets for Pose Estimation. Sensors 20(11): 3042 (2020) - [j9]Dan Feldman, Melanie Schmidt, Christian Sohler:
Turning Big Data Into Tiny Data: Constant-Size Coresets for k-Means, PCA, and Projective Clustering. SIAM J. Comput. 49(3): 601-657 (2020) - [j8]Dan Feldman:
Core-sets: An updated survey. WIREs Data Mining Knowl. Discov. 10(1) (2020) - [c41]Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus:
Provable Filter Pruning for Efficient Neural Networks. ICLR 2020 - [c40]Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman:
Data-Independent Neural Pruning via Coresets. ICLR 2020 - [c39]Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman:
Sets Clustering. ICML 2020: 4994-5005 - [c38]Alaa Maalouf, Adiel Statman, Dan Feldman:
Tight Sensitivity Bounds For Smaller Coresets. KDD 2020: 2051-2061 - [c37]Murad Tukan, Alaa Maalouf, Dan Feldman:
Coresets for Near-Convex Functions. NeurIPS 2020 - [c36]Murad Tukan, Cenk Baykal, Dan Feldman, Daniela Rus:
On Coresets for Support Vector Machines. TAMC 2020: 287-299 - [i35]Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, Adiel Statman:
Sparse Coresets for SVD on Infinite Streams. CoRR abs/2002.06296 (2020) - [i34]Murad Tukan, Cenk Baykal, Dan Feldman, Daniela Rus:
On Coresets for Support Vector Machines. CoRR abs/2002.06469 (2020) - [i33]Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman:
Sets Clustering. CoRR abs/2003.04135 (2020) - [i32]Alaa Maalouf, Ibrahim Jubran, Murad Tukan, Dan Feldman:
Faster PAC Learning and Smaller Coresets via Smoothed Analysis. CoRR abs/2006.05441 (2020) - [i31]Murad Tukan, Alaa Maalouf, Dan Feldman:
Coresets for Near-Convex Functions. CoRR abs/2006.05482 (2020) - [i30]Ben Mussay, Dan Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy:
Data-Independent Structured Pruning of Neural Networks via Coresets. CoRR abs/2008.08316 (2020) - [i29]Murad Tukan, Alaa Maalouf, Matan Weksler, Dan Feldman:
Compressed Deep Networks: Goodbye SVD, Hello Robust Low-Rank Approximation. CoRR abs/2009.05647 (2020) - [i28]Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman:
Deep Learning Meets Projective Clustering. CoRR abs/2010.04290 (2020) - [i27]Dan Feldman:
Introduction to Core-sets: an Updated Survey. CoRR abs/2011.09384 (2020) - [i26]Adiel Statman, Liat Rozenberg, Dan Feldman:
Faster Projective Clustering Approximation of Big Data. CoRR abs/2011.13476 (2020) - [i25]Hayim Shaul, Dan Feldman, Daniela Rus:
Secure k-ish nearest neighbors classifier. IACR Cryptol. ePrint Arch. 2020: 319 (2020)
2010 – 2019
- 2019
- [j7]Liat Rozenberg, Sagi Lotan, Dan Feldman:
Finding Patterns in Signals Using Lossy Text Compression. Algorithms 12(12): 267 (2019) - [j6]Jeryes Danial, Dan Feldman, Ariel Hutterer:
Position Estimation of Moving Objects: Practical Provable Approximation. IEEE Robotics Autom. Lett. 4(2): 1985-1992 (2019) - [j5]Adi Akavia, Dan Feldman, Hayim Shaul:
Secure Data Retrieval on the Cloud: Homomorphic Encryption meets Coresets. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019(2): 80-106 (2019) - [c35]Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus:
Streaming Coreset Constructions for M-Estimators. APPROX-RANDOM 2019: 62:1-62:15 - [c34]Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus:
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds. ICLR (Poster) 2019 - [c33]Daniel Garijo, Deborah Khider, Varun Ratnakar, Yolanda Gil, Ewa Deelman, Rafael Ferreira da Silva, Craig A. Knoblock, Yao-Yi Chiang, Minh Pham, Jay Pujara, Binh Vu, Dan Feldman, Rajiv Mayani, Kelly M. Cobourn, Christopher J. Duffy, Armen R. Kemanian, Lele Shu, Vipin Kumar, Ankush Khandelwal, Kshitij Tayal, Scott D. Peckham, Maria Stoica, Anna Dabrowski, Daniel Hardesty-Lewis, Suzanne A. Pierce:
An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models. IUI Companion 2019: 111-112 - [c32]Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Fast and Accurate Least-Mean-Squares Solvers. NeurIPS 2019: 8305-8316 - [c31]Yair Marom, Dan Feldman:
k-Means Clustering of Lines for Big Data. NeurIPS 2019: 12797-12806 - [c30]Harry Lang, Cenk Baykal, Najib Abu Samra, Tony Tannous, Dan Feldman, Daniela Rus:
Deterministic Coresets for Stochastic Matrices with Applications to Scalable Sparse PageRank. TAMC 2019: 410-423 - [i24]Eitan Netzer, Alex Frid, Dan Feldman:
Real-Time EEG Classification via Coresets for BCI Applications. CoRR abs/1901.00512 (2019) - [i23]Ibrahim Jubran, David Cohn, Dan Feldman:
Provable Approximations for Constrained $\ell_p$ Regression. CoRR abs/1902.10407 (2019) - [i22]Yair Marom, Dan Feldman:
k-Means Clustering of Lines for Big Data. CoRR abs/1903.06904 (2019) - [i21]Alaa Maalouf, Ibrahim Jubran, Dan Feldman:
Fast and Accurate Least-Mean-Squares Solvers. CoRR abs/1906.04705 (2019) - [i20]Dan Feldman, Zahi Kfir, Xuan Wu:
Coresets for Gaussian Mixture Models of Any Shape. CoRR abs/1906.04895 (2019) - [i19]Alaa Maalouf, Adiel Statman, Dan Feldman:
Tight Sensitivity Bounds For Smaller Coresets. CoRR abs/1907.01433 (2019) - [i18]Ben Mussay, Samson Zhou, Vladimir Braverman, Dan Feldman:
On Activation Function Coresets for Network Pruning. CoRR abs/1907.04018 (2019) - [i17]Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus:
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks. CoRR abs/1910.05422 (2019) - [i16]Ibrahim Jubran, Alaa Maalouf, Dan Feldman:
Introduction to Coresets: Accurate Coresets. CoRR abs/1910.08707 (2019) - [i15]Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus:
Provable Filter Pruning for Efficient Neural Networks. CoRR abs/1911.07412 (2019) - 2018
- [j4]Dror Epstein, Dan Feldman:
Quadcopter Tracks Quadcopter via Real-Time Shape Fitting. IEEE Robotics Autom. Lett. 3(1): 544-550 (2018) - [c29]Adi Akavia, Dan Feldman, Hayim Shaul:
Secure Search on Encrypted Data via Multi-Ring Sketch. CCS 2018: 985-1001 - [i14]Hayim Shaul, Dan Feldman, Daniela Rus:
Scalable Secure Computation of Statistical Functions with Applications to k-Nearest Neighbors. CoRR abs/1801.07301 (2018) - [i13]Elad Tolochinsky, Dan Feldman:
Coresets For Monotonic Functions with Applications to Deep Learning. CoRR abs/1802.07382 (2018) - [i12]Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus:
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds. CoRR abs/1804.05345 (2018) - [i11]Dan Feldman, Melanie Schmidt, Christian Sohler:
Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering. CoRR abs/1807.04518 (2018) - [i10]Ibrahim Jubran, Dan Feldman:
Minimizing Sum of Non-Convex but Piecewise log-Lipschitz Functions using Coresets. CoRR abs/1807.08446 (2018) - [i9]Adi Akavia, Dan Feldman, Hayim Shaul:
Secure Search via Multi-Ring Fully Homomorphic Encryption. IACR Cryptol. ePrint Arch. 2018: 245 (2018) - [i8]Adi Akavia, Dan Feldman, Hayim Shaul:
Secure Data Retrieval On The Cloud Homomorphic Encryption Meets Coresets. IACR Cryptol. ePrint Arch. 2018: 1003 (2018) - 2017
- [j3]Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman:
Training Gaussian Mixture Models at Scale via Coresets. J. Mach. Learn. Res. 18: 160:1-160:25 (2017) - [c28]Weiwei Duan, Yao-Yi Chiang, Craig A. Knoblock, Vinil Jain, Dan Feldman, Johannes H. Uhl, Stefan Leyk:
Automatic alignment of geographic features in contemporary vector data and historical maps. GeoAI@SIGSPATIAL 2017: 45-54 - [c27]Dan Feldman, Sedat Ozer, Daniela Rus:
Coresets for Vector Summarization with Applications to Network Graphs. ICML 2017: 1117-1125 - [c26]Dan Feldman, Chongyuan Xiang, Ruihao Zhu, Daniela Rus:
Coresets for differentially private k-means clustering and applications to privacy in mobile sensor networks. IPSN 2017: 3-15 - [c25]Ephraim Goldin, Dan Feldman, George K. Georgoulas, Miguel Castaño, George Nikolakopoulos:
Cloud computing for big data analytics in the Process Control Industry. MED 2017: 1373-1378 - [i7]Dan Feldman, Sedat Ozer, Daniela Rus:
Coresets for Vector Summarization with Applications to Network Graphs. CoRR abs/1706.05554 (2017) - [i6]Adi Akavia, Dan Feldman, Hayim Shaul:
Secure Search on the Cloud via Coresets and Sketches. CoRR abs/1708.05811 (2017) - 2016
- [c24]Dan Feldman, Mikhail Volkov, Daniela Rus:
Dimensionality Reduction of Massive Sparse Datasets Using Coresets. NIPS 2016: 2766-2774 - [c23]Artem Barger, Dan Feldman:
k-Means for Streaming and Distributed Big Sparse Data. SDM 2016: 342-350 - [i5]Vladimir Braverman, Dan Feldman, Harry Lang:
New Frameworks for Offline and Streaming Coreset Constructions. CoRR abs/1612.00889 (2016) - 2015
- [j2]Dan Feldman, Cynthia R. Sung, Andrew Sugaya, Daniela Rus:
iDiary: From GPS Signals to a Text- Searchable Diary. ACM Trans. Sens. Networks 11(4): 60:1-60:41 (2015) - [c22]Mikhail Volkov, Guy Rosman, Dan Feldman, John W. Fisher III, Daniela Rus:
Coresets for visual summarization with applications to loop closure. ICRA 2015: 3638-3645 - [c21]Soliman Nasser, Andew Barry, Marek Doniec, Guy Peled, Guy Rosman, Daniela Rus, Mikhail Volkov, Dan Feldman:
Fleye on the car: big data meets the internet of things. IPSN 2015: 382-383 - [c20]Dan Feldman, Tamir Tassa:
More Constraints, Smaller Coresets: Constrained Matrix Approximation of Sparse Big Data. KDD 2015: 249-258 - [i4]Dan Feldman, Mikhail Volkov, Daniela Rus:
Dimensionality Reduction of Massive Sparse Datasets Using Coresets. CoRR abs/1503.01663 (2015) - [i3]Artem Barger, Dan Feldman:
k-Means for Streaming and Distributed Big Sparse Data. CoRR abs/1511.08990 (2015) - [i2]Soliman Nasser, Ibrahim Jubran, Dan Feldman:
Low-cost and Faster Tracking Systems Using Core-sets for Pose-Estimation. CoRR abs/1511.09120 (2015) - 2014
- [c19]Alexander Munteanu, Christian Sohler, Dan Feldman:
Smallest enclosing ball for probabilistic data. SoCG 2014: 214 - [c18]Rohan Paul, Dan Feldman, Daniela Rus, Paul Newman:
Visual precis generation using coresets. ICRA 2014: 1304-1311 - [c17]Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, Daniela Rus:
Coresets for k-Segmentation of Streaming Data. NIPS 2014: 559-567 - 2013
- [j1]Dan Feldman, Micha Feigin, Nir A. Sochen:
Learning Big (Image) Data via Coresets for Dictionaries. J. Math. Imaging Vis. 46(3): 276-291 (2013) - [c16]Dan Feldman, Stephanie Gil, Ross A. Knepper, Brian J. Julian, Daniela Rus:
K-robots clustering of moving sensors using coresets. ICRA 2013: 881-888 - [c15]Dan Feldman, Andrew Sugaya, Cynthia R. Sung, Daniela Rus:
iDiary: from GPS signals to a text-searchable diary. SenSys 2013: 6:1-6:12 - [c14]Dan Feldman, Melanie Schmidt, Christian Sohler:
Turning big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering. SODA 2013: 1434-1453 - 2012
- [c13]Dan Feldman, Cynthia R. Sung, Daniela Rus:
The single pixel GPS: learning big data signals from tiny coresets. SIGSPATIAL/GIS 2012: 23-32 - [c12]Dan Feldman, Andrew Sugaya, Daniela Rus:
An effective coreset compression algorithm for large scale sensor networks. IPSN 2012: 257-268 - [c11]Cynthia R. Sung, Dan Feldman, Daniela Rus:
Trajectory clustering for motion prediction. IROS 2012: 1547-1552 - [c10]Stephanie Gil, Dan Feldman, Daniela Rus:
Communication coverage for independently moving robots. IROS 2012: 4865-4872 - [c9]Dan Feldman, Leonard J. Schulman:
Data reduction for weighted and outlier-resistant clustering. SODA 2012: 1343-1354 - 2011
- [c8]Dan Feldman, Matthew Faulkner, Andreas Krause:
Scalable Training of Mixture Models via Coresets. NIPS 2011: 2142-2150 - [c7]Micha Feigin, Dan Feldman, Nir A. Sochen:
From High Definition Image to Low Space Optimization. SSVM 2011: 459-470 - [c6]Dan Feldman, Michael Langberg:
A unified framework for approximating and clustering data. STOC 2011: 569-578 - [i1]Dan Feldman, Michael Langberg:
A Unified Framework for Approximating and Clustering Data. CoRR abs/1106.1379 (2011) - 2010
- [b1]Dan Feldman:
Coresets and their applications. Tel Aviv University, Israel, 2010 - [c5]Dan Feldman, Morteza Monemizadeh, Christian Sohler, David P. Woodruff:
Coresets and Sketches for High Dimensional Subspace Approximation Problems. SODA 2010: 630-649
2000 – 2009
- 2009
- [c4]Dan Feldman, Amos Fiat, Haim Kaplan, Kobbi Nissim:
Private coresets. STOC 2009: 361-370 - 2007
- [c3]Dan Feldman, Morteza Monemizadeh, Christian Sohler:
A PTAS for k-means clustering based on weak coresets. SCG 2007: 11-18 - [c2]Dan Feldman, Amos Fiat, Micha Sharir, Danny Segev:
Bi-criteria linear-time approximations for generalized k-mean/median/center. SCG 2007: 19-26 - 2006
- [c1]Dan Feldman, Amos Fiat, Micha Sharir:
Coresets forWeighted Facilities and Their Applications. FOCS 2006: 315-324
Coauthor Index
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