no code implementations • 28 Mar 2022 • Sifan Liu, Jelena Markovic-Voronov, Jonathan Taylor
Conditional selective inference requires an exact characterization of the selection event, which is often unavailable except for a few examples like the lasso.
no code implementations • 27 Jun 2021 • Sifan Liu, Pengfei Ni, Rang Liu, Yang Liu, Ming Li, Qian Liu
During the dynamical access process, an iterative algorithm is proposed to alternatively obtain the active and passive beamforming.
no code implementations • 7 Apr 2021 • Sifan Liu, Art B. Owen
Many machine learning problems optimize an objective that must be measured with noise.
no code implementations • 2 Dec 2020 • Shuxiao Chen, Sifan Liu, Zongming Ma
Focusing on the symmetric two block case, we establish minimax rates for both global estimation of the common structure and individualized estimation of layer-wise community structures.
no code implementations • NeurIPS 2020 • Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci
These show that the convergence rate for Haar and randomized Hadamard matrices are identical, and asymptotically improve upon Gaussian random projections.
no code implementations • WMT (EMNLP) 2020 • Fandong Meng, Jianhao Yan, Yijin Liu, Yuan Gao, Xianfeng Zeng, Qinsong Zeng, Peng Li, Ming Chen, Jie zhou, Sifan Liu, Hao Zhou
We participate in the WMT 2020 shared news translation task on Chinese to English.
no code implementations • 3 Feb 2020 • Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci
These show that the convergence rate for Haar and randomized Hadamard matrices are identical, and asymptotically improve upon Gaussian random projections.
2 code implementations • ICLR 2020 • Sifan Liu, Edgar Dobriban
(2) how to correctly use cross-validation to choose the regularization parameter?
1 code implementation • NeurIPS 2019 • Edgar Dobriban, Sifan Liu
We consider a least squares regression problem where the data has been generated from a linear model, and we are interested to learn the unknown regression parameters.
no code implementations • 5 Aug 2018 • Sifan Liu, Hongzhi Wang
Motived by this, we measure the relation of two concepts by the distance between their corresponding instances and detect errors within the intersection of the conflicting concept sets.