1 code implementation • ICML 2020 • Debabrata Mahapatra, Vaibhav Rajan
However, they cannot be used to find exact Pareto optimal solutions satisfying user-specified preferences with respect to task-specific losses, that is not only a common requirement in applications but also a useful way to explore the infinite set of Pareto optimal solutions.
1 code implementation • 16 Feb 2024 • Aishwarya Jayagopal, Hansheng Xue, Ziyang He, Robert J. Walsh, Krishna Kumar Hariprasannan, David Shao Peng Tan, Tuan Zea Tan, Jason J. Pitt, Anand D. Jeyasekharan, Vaibhav Rajan
Cancer remains a global challenge due to its growing clinical and economic burden.
1 code implementation • 8 Feb 2024 • Siva Rajesh Kasa, Hu Yijie, Santhosh Kumar Kasa, Vaibhav Rajan
\texttt{Mixture-Models} is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants, such as Parsimonious GMMs, Mixture of Factor Analyzers, MClust models, Mixture of Student's t distributions, etc.
no code implementations • 6 Jan 2024 • Prakash Chandra Sukhwal, Vaibhav Rajan, Atreyi Kankanhalli
Medical question answer (QA) assistants respond to lay users' health-related queries by synthesizing information from multiple sources using natural language processing and related techniques.
1 code implementation • 21 Oct 2022 • Hansheng Xue, Vaibhav Rajan, Yu Lin
Understanding genetic variation, e. g., through mutations, in organisms is crucial to unravel their effects on the environment and human health.
no code implementations • 17 Jan 2022 • Shivin Srivastava, Kenji Kawaguchi, Vaibhav Rajan
We theoretically analyze the effect of clustering on its generalization gap, and empirically show that clustered latent representations from ExpertNet lead to disentangling the intrinsic structure and improvement in classification performance.
1 code implementation • 22 Dec 2021 • Hansheng Xue, Vijini Mallawaarachchi, Yujia Zhang, Vaibhav Rajan, Yu Lin
We solve the binning problem by developing new algorithms for (i) graph representation learning that preserves both homophily relations and heterophily constraints (ii) constraint-based graph clustering method that addresses the problems of skewed cluster size distribution.
no code implementations • 27 Sep 2021 • Ragunathan Mariappan, Vaibhav Rajan
Heterogeneous multi-typed, multimodal relational data is increasingly available in many domains and their exploratory analysis poses several challenges.
no code implementations • 2 Aug 2021 • Debabrata Mahapatra, Vaibhav Rajan
These shortcomings lead to modeling limitations and computational inefficiency in multi-task learning (MTL) and multi-criteria decision-making (MCDM) methods that utilize CS for their underlying non-convex multi-objective optimization (MOO).
1 code implementation • 23 Feb 2021 • Shivin Srivastava, Siddharth Bhatia, Lingxiao Huang, Lim Jun Heng, Kenji Kawaguchi, Vaibhav Rajan
In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier.
1 code implementation • 12 Feb 2021 • Hansheng Xue, Luwei Yang, Vaibhav Rajan, Wen Jiang, Yi Wei, Yu Lin
A large number of network embedding methods exist to learn vectorial node representations from general graphs with both homogeneous and heterogeneous node and edge types, including some that can specifically model the distinct properties of bipartite networks.
no code implementations • 12 Sep 2020 • Ragunathan Mariappan, Siva Rajesh Kasa, Vaibhav Rajan
We present the first deep learning based architecture for collective matrix tri-factorization (DCMTF) of arbitrary collections of matrices, also known as augmented multi-view data.
1 code implementation • 8 Jul 2020 • Siva Rajesh Kasa, Vaibhav Rajan
Our simulation studies show that EM has better clustering performance, measured by Adjusted Rand Index, compared to GD in cases of misspecification, whereas on high dimensional data GD outperforms EM.
1 code implementation • 30 Nov 2018 • Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan
PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data.
1 code implementation • 28 Nov 2018 • Ragunathan Mariappan, Vaibhav Rajan
In this paper, we develop the first deep-learning based method, called dCMF, for unsupervised learning of multiple shared representations, that can model such non-linear interactions, from an arbitrary collection of matrices.
1 code implementation • ACL 2018 • Aishwarya Jadhav, Vaibhav Rajan
We present a new neural sequence-to-sequence model for extractive summarization called SWAP-NET (Sentences and Words from Alternating Pointer Networks).
Ranked #2 on Text Summarization on CNN / Daily Mail (Anonymized)
no code implementations • 22 Feb 2016 • Abhishek Sengupta, Vaibhav Rajan, Sakyajit Bhattacharya, G R K Sarma
Stroke is a major cause of mortality and long--term disability in the world.