Chandrashekar Lakshminarayanan

Orcid: 0000-0002-3570-7175

According to our database1, Chandrashekar Lakshminarayanan authored at least 23 papers between 2014 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Links

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Bibliography

2025
Approximate linear programming for decentralized policy iteration in cooperative multi-agent Markov decision processes.
Syst. Control. Lett., 2025

2024
Transformers with Sparse Attention for Granger Causality.
CoRR, 2024

Half-Space Feature Learning in Neural Networks.
CoRR, 2024

Deployability of Deep Reinforcement Learning in Portfolio Management.
Proceedings of the 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD), 2024

2023
Approximate Linear Programming and Decentralized Policy Improvement in Cooperative Multi-agent Markov Decision Processes.
CoRR, 2023

Unsupervised Concept Tagging of Mathematical Questions from Student Explanations.
Proceedings of the Artificial Intelligence in Education - 24th International Conference, 2023

2022
Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality.
CoRR, 2022

CurriculumTutor: An Adaptive Algorithm for Mastering a Curriculum.
Proceedings of the Artificial Intelligence in Education - 23rd International Conference, 2022

2021
Disentangling deep neural networks with rectified linear units using duality.
CoRR, 2021

2020
Deep Gated Networks: A framework to understand training and generalisation in deep learning.
CoRR, 2020

Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018
A Linearly Relaxed Approximate Linear Program for Markov Decision Processes.
IEEE Trans. Autom. Control., 2018

Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go?
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging.
CoRR, 2017

A stability criterion for two timescale stochastic approximation schemes.
Autom., 2017

Scalable Performance Tuning of Hadoop MapReduce: A Noisy Gradient Approach.
Proceedings of the 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), 2017

2016
Performance Tuning of Hadoop MapReduce: A Noisy Gradient Approach.
CoRR, 2016

Shaping Proto-Value Functions Using Rewards.
Proceedings of the ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands, 2016

2015
Shaping Proto-Value Functions via Rewards.
CoRR, 2015

A Generalized Reduced Linear Program for Markov Decision Processes.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Approximate Dynamic Programming based on Projection onto the (min, +) subsemimodule.
CoRR, 2014

A Markov Decision Process Framework for Predictable Job Completion Times on Crowdsourcing Platforms.
Proceedings of the Seconf AAAI Conference on Human Computation and Crowdsourcing, 2014

Approximate Dynamic Programming with (min; +) linear function approximation for Markov decision processes.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014


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