Search Results for author: Vaibhav Rajan

Found 17 papers, 11 papers with code

Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization

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.

Multi-Task Learning

Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models

1 code implementation8 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.

A Joint-Reasoning based Disease Q&A System

no code implementations6 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.

Knowledge Graphs Misinformation +1

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction

1 code implementation21 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.

Combinatorial Optimization Graph Representation Learning

ExpertNet: A Symbiosis of Classification and Clustering

no code implementations17 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.

Classification Clustering +1

RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning

1 code implementation22 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.

Clustering Graph Clustering +1

Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization

no code implementations27 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.

Clustering Matrix Completion +1

Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making

no code implementations2 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).

Computational Efficiency Decision Making +1

Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data

1 code implementation23 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.

Classification Clustering +2

Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks

1 code implementation12 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.

Link Prediction Network Embedding +1

Multi-way Spectral Clustering of Augmented Multi-view Data through Deep Collective Matrix Tri-factorization

no code implementations12 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.

Clustering

Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data

1 code implementation8 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.

Clustering Model Selection

Inferring Concept Prerequisite Relations from Online Educational Resources

1 code implementation30 Nov 2018 Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan

PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data.

Deep Collective Matrix Factorization for Augmented Multi-View Learning

1 code implementation28 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.

Bayesian Optimization Matrix Completion +1

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