no code implementations • 22 Oct 2015 • Xi He, Martin Takáč
This work is motivated by recent work of Shai Shalev-Shwartz on dual free SDCA method, however, we allow a non-uniform selection of "dual" coordinates in SDCA.
no code implementations • 2 Jun 2016 • Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takáč
Training deep neural network is a high dimensional and a highly non-convex optimization problem.
1 code implementation • 29 Dec 2017 • Chang Ge, Xi He, Ihab F. Ilyas, Ashwin Machanavajjhala
Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets.
Databases
no code implementations • 26 Oct 2018 • Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takáč
In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE) method in which sample size is gradually increasing to quickly obtain a solution whose empirical loss is under satisfactory statistical accuracy.
1 code implementation • 8 Jan 2020 • Xi He
The classifier trained on the aligned labelled data set can be transferred to the unlabelled data set to predict the target labels.
1 code implementation • 7 May 2020 • Xi He
Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels.
no code implementations • 4 Dec 2020 • Panpan Zhou, Liyang Chen, Yue Liu, Ilya Sochnikov, Anthony T. Bollinger, Myung-Geun Han, Yimei Zhu, Xi He, Ivan Bozovic, Douglas Natelson
In the quest to understand high-temperature superconductivity in copper oxides, a vigorous debate has been focused on the pseudogap - a partial gap that opens over portions of the Fermi surface in the 'normal' state above the bulk critical temperature ($T_{c}$).
Superconductivity Mesoscale and Nanoscale Physics Strongly Correlated Electrons
1 code implementation • 31 Dec 2020 • Chang Ge, Shubhankar Mohapatra, Xi He, Ihab F. Ilyas
Existing differentially private data synthesis methods aim to generate useful data based on applications, but they fail in keeping one of the most fundamental data properties of the structured data -- the underlying correlations and dependencies among tuples and attributes (i. e., the structure of the data).
Databases Cryptography and Security
no code implementations • 5 Jul 2021 • Max A. Little, Xi He, Ugur Kayas
Dynamic programming (DP) is an algorithmic design paradigm for the efficient, exact solution of otherwise intractable, combinatorial problems.
no code implementations • NeurIPS 2021 • Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar
Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection.
1 code implementation • 6 Jun 2023 • Tao Lei, Rui Sun, Xuan Wang, Yingbo Wang, Xi He, Asoke Nandi
The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation.
no code implementations • 21 Jun 2023 • Xi He, Waheed Ul Rahman, Max A. Little
We demonstrate the effectiveness of this algorithm on synthetic and real-world datasets, showing optimal accuracy both in and out-of-sample, in practical computational time.