326 citations · 1k across the 67 of their papers we have counts for
79 papers
GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts
Returaj Burnwal, Anirban Santara, Nirav P. Bhatt +2
Model predictive control (MPC) is a popular approach for trajectory optimization in practical robotics applications. MPC policies can optimize trajectory parameters under kinodynam…
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning
Aravind Venugopal, Stephanie Milani, Fei Fang +1
Multi-agent reinforcement learning (MARL) methods often suffer from high sample complexity, limiting their use in real-world problems where data is sparse or expensive to collect.…
Physics-Informed Model-Based Reinforcement Learning
Adithya Ramesh, Balaraman Ravindran
We apply reinforcement learning (RL) to robotics tasks. One of the drawbacks of traditional RL algorithms has been their poor sample efficiency. One approach to improve the sample…
Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning
Kushal Chauhan, Soumya Chatterjee, Akash Reddy +2
The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstract…
Evolutionary Approach to Security Games with Signaling
Adam Żychowski, Jacek Mańdziuk, Elizabeth Bondi +3
Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have…
A Causal Approach for Unfair Edge Prioritization and Discrimination Removal
Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran
In budget-constrained settings aimed at mitigating unfairness, like law enforcement, it is essential to prioritize the sources of unfairness before taking measures to mitigate them…
Smooth Imitation Learning via Smooth Costs and Smooth Policies
Sapana Chaudhary, Balaraman Ravindran
Imitation learning (IL) is a popular approach in the continuous control setting as among other reasons it circumvents the problems of reward mis-specification and exploration in re…
Dynamic probabilistic logic models for effective abstractions in RL
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan +2
State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments. Recently, we proposed RePReL (Kokel et al. 2021), a hie…
Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis
Amrit Diggavi Seshadri, Balaraman Ravindran
Synthesizing high-quality, realistic images from text-descriptions is a challenging task, and current methods synthesize images from text in a multi-stage manner, typically by firs…
Semi-Supervised Deep Learning for Multiplex Networks
Anasua Mitra, Priyesh Vijayan, Ranbir Sanasam +3
Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer.…
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes
Siddharth Nishtala, Lovish Madaan, Aditya Mate +11
India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100,000 live births. Lack of access to preventive care information is a major contributing factor…
Hyperedge Prediction using Tensor Eigenvalue Decomposition
Deepak Maurya, Balaraman Ravindran
Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple dyadic interactions and could require th…
qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems
Nahas Pareekutty, Francis James, Balaraman Ravindran +1
This paper presents a sampling-based method for optimal motion planning in non-holonomic systems in the absence of known cost functions. It uses the principle of learning through e…
Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1
Jahnvi Patel, Devika Jay, Balaraman Ravindran +1
In real time electricity markets, the objective of generation companies while bidding is to maximize their profit. The strategies for learning optimal bidding have been formulated…
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…