77 citations · 129 across the 18 of their papers we have counts for
19 papers
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Shubham Gupta, Sahil Manchanda, Sayan Ranu +1
Graph neural networks (GNNs), in general, are built on the assumption of a static set of features characterizing each node in a graph. This assumption is often violated in practice…
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Sahil Manchanda, Shubham Gupta, Sayan Ranu +1
Deep graph generative modeling has gained enormous attraction in recent years due to its impressive ability to directly learn the underlying hidden graph distribution. Despite thei…
Reinforcement Learning for ConnectX
Sheel Shah, Shubham Gupta
ConnectX is a two-player game that generalizes the popular game Connect 4. The objective is to get X coins across a row, column, or diagonal of an M x N board. The first player to…
A Survey on Temporal Graph Representation Learning and Generative Modeling
Shubham Gupta, Srikanta Bedathur
Temporal graphs represent the dynamic relationships among entities and occur in many real life application like social networks, e commerce, communication, road networks, biologica…
TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs
Shubham Gupta, Sahil Manchanda, Srikanta Bedathur +1
There has been a recent surge in learning generative models for graphs. While impressive progress has been made on static graphs, work on generative modeling of temporal graphs is…
On consistency of constrained spectral clustering under representation-aware stochastic block model
Shubham Gupta, Ambedkar Dukkipati
Spectral clustering is widely used in practice due to its flexibility, computational efficiency, and well-understood theoretical performance guarantees. Recently, spectral clusteri…
Differentiable Rule Induction with Learned Relational Features
Remy Kusters, Yusik Kim, Marine Collery +2
Rule-based decision models are attractive due to their interpretability. However, existing rule induction methods often result in long and consequently less interpretable rule mode…
CoviHawkes: Temporal Point Process and Deep Learning based Covid-19 forecasting for India
Ambedkar Dukkipati, Tony Gracious, Shubham Gupta
Lockdowns are one of the most effective measures for containing the spread of a pandemic. Unfortunately, they involve a heavy financial and emotional toll on the population that of…
Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions
Shubham Gupta, Ambedkar Dukkipati
Spectral clustering is popular among practitioners and theoreticians alike. While performance guarantees for spectral clustering are well understood, recent studies have focused on…
End-to-End Attention-based Image Captioning
Carola Sundaramoorthy, Lin Ziwen Kelvin, Mahak Sarin +1
In this paper, we address the problem of image captioning specifically for molecular translation where the result would be a predicted chemical notation in InChI format for a given…
Pure Exploration with Structured Preference Feedback
Shubham Gupta, Aadirupa Saha, Sumeet Katariya
We consider the problem of pure exploration with subset-wise preference feedback, which contains arms with features. The learner is allowed to query subsets of size and rec…
Active Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
Rishi Hazra, Parag Dutta, Shubham Gupta +2
While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. Ho…
Networked Multi-Agent Reinforcement Learning with Emergent Communication
Shubham Gupta, Rishi Hazra, Ambedkar Dukkipati
Multi-Agent Reinforcement Learning (MARL) methods find optimal policies for agents that operate in the presence of other learning agents. Central to achieving this is how the agent…
Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs
Tony Gracious, Shubham Gupta, Arun Kanthali +2
Although static networks have been extensively studied in machine learning, data mining, and AI communities for many decades, the study of dynamic networks has recently taken cente…
Equipping SBMs with RBMs: An Explainable Approach for Analysis of Networks with Covariates
Shubham Gupta, Gururaj K., Ambedkar Dukkipati +1
Networks with node covariates offer two advantages to community detection methods, namely, (i) exploit covariates to improve the quality of communities, and more importantly, (ii)…
Active Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
Rishi Hazra, Parag Dutta, Shubham Gupta +2
While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. Ho…
Winning an Election: On Emergent Strategic Communication in Multi-Agent Networks
Shubham Gupta, Ambedkar Dukkipati
Humans use language to collectively execute abstract strategies besides using it as a referential tool for identifying physical entities. Recently, multiple attempts at replicating…
A Generative Model for Dynamic Networks with Applications
Shubham Gupta, Gaurav Sharma, Ambedkar Dukkipati
Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper,…
Attentive Recurrent Comparators
Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati
Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form repres…