Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Paul, Saurabh"'
Autor:
Ge, Yingqiang, Zhao, Xiaoting, Yu, Lucia, Paul, Saurabh, Hu, Diane, Hsieh, Chu-Cheng, Zhang, Yongfeng
The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim
Externí odkaz:
http://arxiv.org/abs/2201.00140
Autor:
Paul, Saurabh, Tiesinga, Eite
Publikováno v:
Phys. Rev. A 94, 033606 (2016)
We propose a numerical method using the discrete variable representation (DVR) for constructing real-valued Wannier functions localized in a unit cell for both symmetric and asymmetric periodic potentials. We apply these results to finding Wannier fu
Externí odkaz:
http://arxiv.org/abs/1609.00654
We show that for ultra-cold neutral bosonic atoms held in a three-dimensional periodic potential or optical lattice, a Hubbard model with dominant, attractive three-body interactions can be generated. In fact, we derive that the effect of pair-wise i
Externí odkaz:
http://arxiv.org/abs/1604.01289
Selecting a good column (or row) subset of massive data matrices has found many applications in data analysis and machine learning. We propose a new adaptive sampling algorithm that can be used to improve any relative-error column selection algorithm
Externí odkaz:
http://arxiv.org/abs/1510.04149
Autor:
Paul, Saurabh, Tiesinga, Eite
We study ultracold atoms in an optical lattice with two local minima per unit cell and show that the low energy states of a multi-band Bose-Hubbard (BH) Hamiltonian with only pair-wise interactions is equivalent to an effective single-band Hamiltonia
Externí odkaz:
http://arxiv.org/abs/1507.04740
Autor:
Paul, Saurabh, Drineas, Petros
We introduce single-set spectral sparsification as a deterministic sampling based feature selection technique for regularized least squares classification, which is the classification analogue to ridge regression. The method is unsupervised and gives
Externí odkaz:
http://arxiv.org/abs/1506.05173
We give two provably accurate feature-selection techniques for the linear SVM. The algorithms run in deterministic and randomized time respectively. Our algorithms can be used in an unsupervised or supervised setting. The supervised approach is based
Externí odkaz:
http://arxiv.org/abs/1406.0167
Formation and decay of Bose-Einstein condensates in an excited band of a double-well optical lattice
Autor:
Paul, Saurabh, Tiesinga, Eite
We study the formation and collision-aided decay of an ultra-cold atomic Bose-Einstein condensate in the first excited band of a double-well 2D-optical lattice with weak harmonic confinement in the perpendicular $z$ direction. This lattice geometry i
Externí odkaz:
http://arxiv.org/abs/1308.4449
Let X be a data matrix of rank \rho, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction techn
Externí odkaz:
http://arxiv.org/abs/1211.6085
Publikováno v:
In Pattern Recognition December 2016 60:205-214
