326 citations · 1k across the 67 of their papers we have counts for
14 papers · 1 filter
Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems
Hardik Meisheri, Vinita Baniwal, Nazneen N Sultana +2
This paper describes a purely data-driven solution to a class of sequential decision-making problems with a large number of concurrent online decisions, with applications to comput…
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning
Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran
Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent. However, such techniques often yield a…
Let's Ask Again: Refine Network for Automatic Question Generation
Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra +2
In this work, we focus on the task of Automatic Question Generation (AQG) where given a passage and an answer the task is to generate the corresponding question. It is desired that…
Generalized Random Surfer-Pair Models
Sai Kiran Narayanaswami, Balaraman Ravindran, Venkatesh Ramaiyan
SimRank is a widely studied link-based similarity measure that is known for its simple, yet powerful philosophy that two nodes are similar if they are referenced by similar nodes.…
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling
Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder +2
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art m…
Network Representation Learning: Consolidation and Renewed Bearing
Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan +9
Graphs are a natural abstraction for many problems where nodes represent entities and edges represent a relationship across entities. An important area of research that has emerged…
ExTra: Transfer-guided Exploration
Anirban Santara, Rishabh Madan, Balaraman Ravindran +1
In this work we present a novel approach for transfer-guided exploration in reinforcement learning that is inspired by the human tendency to leverage experiences from similar encou…
Learning Interpretable Models Using Uncertainty Oracles
Abhishek Ghose, Balaraman Ravindran
A desirable property of interpretable models is small size, so that they are easily understandable by humans. This leads to the following challenges: (a) small sizes typically impl…
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
Manan Tomar, Akhil Sathuluri, Balaraman Ravindran
Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion. This makes the problem less complex a…
Successor Options: An Option Discovery Framework for Reinforcement Learning
Rahul Ramesh, Manan Tomar, Balaraman Ravindran
The options framework in reinforcement learning models the notion of a skill or a temporally extended sequence of actions. The discovery of a reusable set of skills has typically e…
Interpretability with Accurate Small Models
Abhishek Ghose, Balaraman Ravindran
Models often need to be constrained to a certain size for them to be considered interpretable. For example, a decision tree of depth 5 is much easier to understand than one of dept…
Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning
Harish Kumar, Balaraman Ravindran
In the domain of algorithmic music composition, machine learning-driven systems eliminate the need for carefully hand-crafting rules for composition. In particular, the capability…
Edge Replacement Grammars: A Formal Language Approach for Generating Graphs
Revanth Reddy, Sarath Chandar, Balaraman Ravindran
Graphs are increasingly becoming ubiquitous as models for structured data. A generative model that closely mimics the structural properties of a given set of graphs has utility in…
An Active Learning Framework for Efficient Robust Policy Search
Sai Kiran Narayanaswami, Nandan Sudarsanam, Balaraman Ravindran
Robust Policy Search is the problem of learning policies that do not degrade in performance when subject to unseen environment model parameters. It is particularly relevant for tra…