Balaraman Ravindran

research

∙ 06/25/2023

PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks

Graph convolutional networks (GCNs) have achieved huge success in severa...

0 Saket Gurukar, et al. ∙

research

∙ 05/30/2023

GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts

Model predictive control (MPC) is a popular approach for trajectory opti...

3 Returaj Burnwal, et al. ∙

research

∙ 05/23/2023

Clustering Indices based Automatic Classification Model Selection

Classification model selection is a process of identifying a suitable mo...

0 Sudarsun Santhiappan, et al. ∙

research

∙ 04/12/2023

Bi-level Latent Variable Model for Sample-Efficient Multi-Agent Reinforcement Learning

Despite their potential in real-world applications, multi-agent reinforc...

0 Aravind Venugopal, et al. ∙

research

∙ 03/13/2023

Are Models Trained on Indian Legal Data Fair?

Recent advances and applications of language technology and artificial i...

19 Sahil Girhepuje, et al. ∙

research

∙ 12/05/2022

Physics-Informed Model-Based Reinforcement Learning

We apply reinforcement learning (RL) to robotics. One of the drawbacks o...

0 Adithya Ramesh, et al. ∙

research

∙ 11/27/2022

ReGrAt: Regularization in Graphs using Attention to handle class imbalance

Node classification is an important task to solve in graph-based learnin...

0 Neeraja Kirtane, et al. ∙

research

∙ 07/14/2022

GrabQC: Graph based Query Contextualization for automated ICD coding

Automated medical coding is a process of codifying clinical notes to app...

0 Jeshuren Chelladurai, et al. ∙

research

∙ 06/25/2022

Multi-Variate Time Series Forecasting on Variable Subsets

We formulate a new inference task in the domain of multivariate time ser...

24 Jatin Chauhan, et al. ∙

research

∙ 06/12/2022

Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning

The options framework in Hierarchical Reinforcement Learning breaks down...

0 Kushal Chauhan, et al. ∙

research

∙ 04/29/2022

Evolutionary Approach to Security Games with Signaling

Green Security Games have become a popular way to model scenarios involv...

14 Adam Żychowski, et al. ∙

research

∙ 03/12/2022

A Survey in Adversarial Defences and Robustness in NLP

In recent years, it has been seen that deep neural networks are lacking ...

0 Shreya Goyal, et al. ∙

research

∙ 11/29/2021

A Causal Approach for Unfair Edge Prioritization and Discrimination Removal

In budget-constrained settings aimed at mitigating unfairness, like law ...

0 Pavan Ravishankar, et al. ∙

research

∙ 11/03/2021

Smooth Imitation Learning via Smooth Costs and Smooth Policies

Imitation learning (IL) is a popular approach in the continuous control ...

0 Sapana Chaudhary, et al. ∙

research

∙ 10/15/2021

Dynamic probabilistic logic models for effective abstractions in RL

State abstraction enables sample-efficient learning and better task tran...

13 Harsha Kokel, et al. ∙

research

∙ 10/15/2021

Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis

Synthesizing high-quality, realistic images from text-descriptions is a ...

0 Amrit Diggavi Seshadri, et al. ∙

research

∙ 10/05/2021

Semi-Supervised Deep Learning for Multiplex Networks

Multiplex networks are complex graph structures in which a set of entiti...

9 Anasua Mitra, et al. ∙

research

∙ 05/11/2021

TAG: Task-based Accumulated Gradients for Lifelong learning

When an agent encounters a continual stream of new tasks in the lifelong...

0 Pranshu Malviya, et al. ∙

research

∙ 02/06/2021

Hyperedge Prediction using Tensor Eigenvalue Decomposition

Link prediction in graphs is studied by modeling the dyadic interactions...

0 Deepak Maurya, et al. ∙

research

∙ 01/07/2021

qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems

This paper presents a sampling-based method for optimal motion planning ...

0 Nahas Pareekutty, et al. ∙

research

∙ 01/07/2021

Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1

In real time electricity markets, the objective of generation companies ...

0 Jahnvi Patel, et al. ∙

research

∙ 12/18/2020

Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty

Green Security Games (GSGs) have been successfully used in the protectio...

0 Aravind Venugopal, et al. ∙

research

∙ 12/16/2020

Relational Boosted Bandits

Contextual bandits algorithms have become essential in real-world user i...

1 Ashutosh Kakadiya, et al. ∙

research

∙ 11/16/2020

Hypergraph Partitioning using Tensor Eigenvalue Decomposition

Hypergraphs have gained increasing attention in the machine learning com...

0 Deepak Maurya, et al. ∙

research

∙ 10/30/2020

Goal directed molecule generation using Monte Carlo Tree Search

One challenging and essential task in biochemistry is the generation of ...

0 Anand A. Rajasekar, et al. ∙

research

∙ 08/18/2020

Reinforcement Learning for Improving Object Detection

The performance of a trained object detection neural network depends a l...

39 Siddharth Nayak, et al. ∙

research

∙ 07/10/2020

A Causal Linear Model to Quantify Edge Unfairness for Unfair Edge Prioritization and Discrimination Removal

The dataset can be generated by an unfair mechanism in numerous settings...

0 Pavan Ravishankar, et al. ∙

research

∙ 06/07/2020

Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains

This paper describes the application of reinforcement learning (RL) to m...

30 Nazneen N Sultana, et al. ∙

research

∙ 05/28/2020

On Incorporating Structural Information to improve Dialogue Response Generation

We consider the task of generating dialogue responses from background kn...

0 Nikita Moghe, et al. ∙

research

∙ 05/09/2020

Understanding Dynamic Scenes using Graph Convolution Networks

We present a novel Multi Relational Graph Convolutional Network (MRGCN) ...

22 Sravan Mylavarapu, et al. ∙

research

∙ 04/29/2020

Towards Transparent and Explainable Attention Models

Recent studies on interpretability of attention distributions have led t...

0 Akash Kumar Mohankumar, et al. ∙

research

∙ 04/21/2020

EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks

Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their a...

6 Sanchari Sen, et al. ∙

research

∙ 02/03/2020

Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks

Understanding on-road vehicle behaviour from a temporal sequence of sens...

13 Sravan Mylavarapu, et al. ∙

research

∙ 01/15/2020

SEERL: Sample Efficient Ensemble Reinforcement Learning

Ensemble learning is a very prevalent method employed in machine learnin...

43 Rohan Saphal, et al. ∙

research

∙ 10/01/2019

Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems

This paper describes a purely data-driven solution to a class of sequent...

26 Hardik Meisheri, et al. ∙

research

∙ 09/09/2019

Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning

Option discovery and skill acquisition frameworks are integral to the fu...

6 Arjun Manoharan, et al. ∙

research

∙ 08/31/2019

Let's Ask Again: Refine Network for Automatic Question Generation

In this work, we focus on the task of Automatic Question Generation (AQG...

0 Preksha Nema, et al. ∙

research

∙ 06/27/2019

ExTra: Transfer-guided Exploration

In this work we present a novel approach for transfer-guided exploration...

8 Anirban Santara, et al. ∙

research

∙ 06/17/2019

Learning Interpretable Models Using an Oracle

As Machine Learning (ML) becomes pervasive in various real world systems...

7 Abhishek Ghose, et al. ∙

research

∙ 05/17/2019

MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning

Shaping in humans and animals has been shown to be a powerful tool for l...

11 Manan Tomar, et al. ∙

research

∙ 05/14/2019

Successor Options: An Option Discovery Framework for Reinforcement Learning

The options framework in reinforcement learning models the notion of a s...

6 Rahul Ramesh, et al. ∙

research

∙ 05/04/2019

Optimal Resampling for Learning Small Models

Models often need to be constrained to a certain size for them to be con...

26 Abhishek Ghose, et al. ∙

research

∙ 05/02/2019

Network Representation Learning: Consolidation and Renewed Bearing

Graphs are a natural abstraction for many problems where nodes represent...

18 Saket Gurukar, et al. ∙

research

∙ 02/11/2019

Edge Replacement Grammars: A Formal Language Approach for Generating Graphs

Graphs are increasingly becoming ubiquitous as models for structured dat...

0 Revanth Reddy, et al. ∙

research

∙ 02/05/2019

Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning

In the domain of algorithmic music composition, machine learning-driven ...

18 Harish Kumar, et al. ∙

research

∙ 01/01/2019

An Active Learning Framework for Efficient Robust Policy Search

Robust Policy Search is the problem of learning policies that do not deg...

10 Sai Kiran Narayanaswami, et al. ∙

research

∙ 12/28/2018

Hypergraph Clustering: A Modularity Maximization Approach

Clustering on hypergraphs has been garnering increased attention with po...

28 Tarun Kumar, et al. ∙

research

∙ 12/26/2018

Studying the Plasticity in Deep Convolutional Neural Networks using Random Pruning

Recently there has been a lot of work on pruning filters from deep convo...

12 Deepak Mittal, et al. ∙

research

∙ 12/23/2018

Learning to Prevent Monocular SLAM Failure using Reinforcement Learning

Monocular SLAM refers to using a single camera to estimate robot ego mot...

0 Vignesh Prasad, et al. ∙

research

∙ 12/01/2018

Discovering hierarchies using Imitation Learning from hierarchy aware policies

Learning options that allow agents to exhibit temporally higher order be...

0 Ameet Deshpande, et al. ∙

Balaraman Ravindran

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