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Many path planning algorithms are based on sampling the state space.
Some np-complete problems in quadratic and nonlinear programming
K. G. Murty and S. N. Kabadi · 1987
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Ellipsoids of maximal volume in convex bodies
K. Ball · 1992
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Probabilistic roadmaps for path planning in high-dimensional configuration spaces
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars · 1996
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Enhancing gjk: Computing minimum and penetration distances between convex polyhedra
S. Cameron · 1997
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Rapidly-exploring random trees: A new tool for path planning
S. M. LaValle et al · 1998
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Randomized kinodynamic planning
S. M. LaValle and J. J. Kuffner Jr · 2001
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Approximating extent measures of points
P. K. Agarwal, S. Har-Peled, and K. R. Varadarajan · 2004
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Minimum-volume enclosing ellipsoids and core sets
P. Kumar and E. A. Yildirim · 2005
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Core vector machines: Fast svm training on very large data sets
I. W. Tsang, J. T. Kwok, and P.-M. Cheung · 2005
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Very large svm training using core vector machines
I. W. Tsang, J. T.-Y. Kwok, and P.-M. Cheung · 2005
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Improved approximation algorithms for large matrices via random projections
T. Sarlos · 2006
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Generalized core vector machines
I.-H. Tsang, J.-Y. Kwok, and J. M. Zurada · 2006
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Maximum margin coresets for active and noise tolerant learning
S. Har-Peled, D. Roth, and D. Zimak · 2007
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On khachiyan’s algorithm for the computation of minimum-volume enclosing ellipsoids
M. J. Todd and E. A. Yıldırım · 2007
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Sampling algorithms and coresets for \ \backslash ell_p regression
A. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney · 2009
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Coresets and sketches for high dimensional subspace approximation problems
D. Feldman, M. Monemizadeh, C. Sohler, and D. P. Woodruff · 2010
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Incremental sampling-based algorithms for optimal motion planning
S. Karaman and E. Frazzoli · 2010
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Sampling-based algorithms for optimal motion planning
S. Karaman and E. Frazzoli · 2011
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Subspace embeddings for the l1-norm with applications
C. Sohler and D. P. Woodruff · 2011
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Geometric algorithms and combinatorial optimization
M. Grötschel, L. Lovász, and A. Schrijver · 2012
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A coreset-based semi-supverised clustering using one-class support vector machines
L. Gu · 2012
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Rrt ∗ \ast -smart: Rapid convergence implementation of rrt ∗ \ast towards optimal solution
F. Islam, J. Nasir, U. Malik, Y. Ayaz, and O. Hasan · 2012
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Rapidly-exploring random tree based memory efficient motion planning
O. Adiyatov and H. A. Varol · 2013
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K-robots clustering of moving sensors using coresets
D. Feldman, S. Gil, R. A. Knepper, B. Julian, and D. Rus · 2013
Cited alongside, same era.
Rrt*-smart: A rapid convergence implementation of rrt
J. Nasir, F. Islam, U. Malik, Y. Ayaz, O. Hasan, M. Khan, and M. S. Muhammad · 2013
Introduction to coresets: Accurate coresets
I. Jubran, A. Maalouf, and D. Feldman · 2019
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Discrepancy, coresets, and sketches in machine learning
Z. Karnin and E. Liberty · 2019
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Balancing global exploration and local-connectivity exploitation with rapidly-exploring random disjointed-trees
T. Lai, F. Ramos, and G. Francis · 2019
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Provable filter pruning for efficient neural networks
L. Liebenwein, C. Baykal, H. Lang, D. Feldman, and D. Rus · 2019
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Fast and accurate least-mean-squares solvers
A. Maalouf, I. Jubran, and D. Feldman · 2019
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Fair coresets and streaming algorithms for fair k-means
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Cited alongside, same era.
Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic
J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot · 2014
Cited alongside, same era.
Time-based rrt algorithm for rendezvous planning of two dynamic systems
A. Sintov and A. Shapiro · 2014
Cited alongside, same era.
Lp row sampling by lewis weights
M. B. Cohen and R. Peng · 2015
Cited alongside, same era.
Rt-rrt* a real-time path planning algorithm based on rrt
K. Naderi, J. Rajamäki, and P. Hämäläinen · 2015
Cited alongside, same era.
Coresets for scalable bayesian logistic regression
J. Huggins, T. Campbell, and T. Broderick · 2016
Cited alongside, same era.
Strong coresets for hard and soft bregman clustering with applications to exponential family mixtures
M. Lucic, O. Bachem, and A. Krause · 2016
Cited alongside, same era.
M. Schmidt, C. Schwiegelshohn, and C. Sohler · 2019
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Core-sets: Updated survey
D. Feldman · 2020
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Sets clustering
I. Jubran, M. Tukan, A. Maalouf, and D. Feldman · 2020
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Coresets for robust training of neural networks against noisy labels
B. Mirzasoleiman, K. Cao, and J. Leskovec · 2020
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Autonomous toy drone via coresets for pose estimation
S. Nasser, I. Jubran, and D. Feldman · 2020
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Small-gan: Speeding up gan training using core-sets
S. Sinha, H. Zhang, A. Goyal, Y. Bengio, H. Larochelle, and A. Odena · 2020
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Coresets for near-convex functions
M. Tukan, A. Maalouf, and D. Feldman · 2020
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Coresets for decision trees of signals
I. Jubran, E. E. Sanches Shayda, I. Newman, and D. Feldman · 2021
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Introduction to coresets: Approximated mean
A. Maalouf, I. Jubran, and D. Feldman · 2021
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Coresets for the average case error for finite query sets
A. Maalouf, I. Jubran, M. Tukan, and D. Feldman · 2021
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Data-independent structured pruning of neural networks via coresets
B. Mussay, D. Feldman, S. Zhou, V. Braverman, and M. Osadchy · 2021
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Rrt-rope: A deterministic shortening approach for fast near-optimal path planning in large-scale uncluttered 3d environments
L. Petit and A. L. Desbiens · 2021
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On coresets for support vector machines
M. Tukan, C. Baykal, D. Feldman, and D. Rus · 2021
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