Search Results for author: Shrinu Kushagra

Found 9 papers, 2 papers with code

PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX

2 code implementations8 Feb 2022 Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Shrinu Kushagra, Dileep George

PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalable loopy belief propagation (LBP) in JAX.

On sampling from data with duplicate records

no code implementations24 Aug 2020 Alireza Heidari, Shrinu Kushagra, Ihab F. Ilyas

Our goal is to develop a procedure that samples uniformly from the set of entities present in the database in the presence of duplicates.

Record fusion: A learning approach

no code implementations18 Jun 2020 Alireza Heidari, George Michalopoulos, Shrinu Kushagra, Ihab F. Ilyas, Theodoros Rekatsinas

We use this feature vector alongwith the ground-truth information to learn a classifier for each of the attributes of the database.

Adjoined Networks: A Training Paradigm with Applications to Network Compression

1 code implementation10 Jun 2020 Utkarsh Nath, Shrinu Kushagra, Yingzhen Yang

In this paper, we introduce Adjoined Networks, or AN, a learning paradigm that trains both the original base network and the smaller compressed network together.

Knowledge Distillation Neural Architecture Search +1

Semi-supervised clustering for de-duplication

no code implementations10 Oct 2018 Shrinu Kushagra, Shai Ben-David, Ihab Ilyas

In this work, we view de-duplication as a clustering problem where the goal is to put records corresponding to the same physical entity in the same cluster and putting records corresponding to different physical entities into different clusters.


Provably noise-robust, regularised $k$-means clustering

no code implementations30 Nov 2017 Shrinu Kushagra, Yao-Liang Yu, Shai Ben-David

We focus on the $k$-means objective and we prove that the regularised version of $k$-means is NP-Hard even for $k=1$.


Clustering with Same-Cluster Queries

no code implementations NeurIPS 2016 Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David

We show that there is a trade off between computational complexity and query complexity; We prove that for the case of $k$-means clustering (i. e., when the expert conforms to a solution of $k$-means), having access to relatively few such queries allows efficient solutions to otherwise NP hard problems.


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