326 citations · 1k across the 67 of their papers we have counts for
16 papers · 1 filter
Relational Boosted Bandits
Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran
Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representa…
Hypergraph Partitioning using Tensor Eigenvalue Decomposition
Deepak Maurya, Balaraman Ravindran
Hypergraphs have gained increasing attention in the machine learning community lately due to their superiority over graphs in capturing super-dyadic interactions among entities. In…
MADRaS : Multi Agent Driving Simulator
Anirban Santara, Sohan Rudra, Sree Aditya Buridi +4
In this work, we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. MADRaS prov…
Goal directed molecule generation using Monte Carlo Tree Search
Anand A. Rajasekar, Karthik Raman, Balaraman Ravindran
One challenging and essential task in biochemistry is the generation of novel molecules with desired properties. Novel molecule generation remains a challenge since the molecule sp…
ADAIL: Adaptive Adversarial Imitation Learning
Yiren Lu, Jonathan Tompson
We present the ADaptive Adversarial Imitation Learning (ADAIL) algorithm for learning adaptive policies that can be transferred between environments of varying dynamics, by imitati…
Reinforcement Learning for Improving Object Detection
Siddharth Nayak, Balaraman Ravindran
The performance of a trained object detection neural network depends a lot on the image quality. Generally, images are pre-processed before feeding them into the neural network and…
Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement
Siddharth Nishtala, Harshavardhan Kamarthi, Divy Thakkar +8
India accounts for 11% of maternal deaths globally where a woman dies in childbirth every fifteen minutes. Lack of access to preventive care information is a significant problem co…
A Causal Linear Model to Quantify Edge Flow and Edge Unfairness for UnfairEdge Prioritization and Discrimination Removal
Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran
Law enforcement must prioritize sources of unfairness before mitigating their underlying unfairness, considering that they have limited resources. Unlike previous works that only m…
HPRA: Hyperedge Prediction using Resource Allocation
Tarun Kumar, K Darwin, Srinivasan Parthasarathy +1
Many real-world systems involve higher-order interactions and thus demand complex models such as hypergraphs. For instance, a research article could have multiple collaborating aut…
Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains
Nazneen N Sultana, Hardik Meisheri, Vinita Baniwal +3
This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both adapted fr…
On Incorporating Structural Information to improve Dialogue Response Generation
Nikita Moghe, Priyesh Vijayan, Balaraman Ravindran +1
We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources. Specifically, given a conversation around a movie, the task…
Understanding Dynamic Scenes using Graph Convolution Networks
Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan +3
We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed b…
Towards Transparent and Explainable Attention Models
Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan +3
Recent studies on interpretability of attention distributions have led to notions of faithful and plausible explanations for a model's predictions. Attention distributions can be c…
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks
Sanchari Sen, Balaraman Ravindran, Anand Raghunathan
Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their adoption in safety-critical applications such as self-driving cars, drones, and healthcare. Notably, DNNs are…
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks
Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan +3
Understanding on-road vehicle behaviour from a temporal sequence of sensor data is gaining in popularity. In this paper, we propose a pipeline for understanding vehicle behaviour f…
SEERL: Sample Efficient Ensemble Reinforcement Learning
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere +2
Ensemble learning is a very prevalent method employed in machine learning. The relative success of ensemble methods is attributed to their ability to tackle a wide range of instanc…