Search Results for author: Nandyala Hemachandra

Found 8 papers, 1 papers with code

Multi-Agent Congestion Cost Minimization With Linear Function Approximations

1 code implementation26 Jan 2023 Prashant Trivedi, Nandyala Hemachandra

The proof requires the convergence of cost function parameters, the MAEVI algorithm, and analysis of the regret bounds induced by the MAEVI triggering condition for each agent.

Multi-agent Reinforcement Learning

Anomaly Detection using Capsule Networks for High-dimensional Datasets

no code implementations27 Dec 2021 Inderjeet Singh, Nandyala Hemachandra

To the best of our knowledge, this is the first instance where a capsule network is analyzed for the anomaly detection task in a high-dimensional complex data setting.

Anomaly Detection Binary Classification +3

Multi-agent Natural Actor-critic Reinforcement Learning Algorithms

no code implementations3 Sep 2021 Prashant Trivedi, Nandyala Hemachandra

We observe an almost 25\% reduction in the average congestion in 2 MAN algorithms; the average congestion in another MAN algorithm is on par with the MAAC algorithm.

Multi-agent Reinforcement Learning reinforcement-learning +1

Thompson Sampling for Unsupervised Sequential Selection

no code implementations16 Sep 2020 Arun Verma, Manjesh K. Hanawal, Nandyala Hemachandra

The total loss is the sum of the cost incurred for selecting the arm and the stochastic loss associated with the selected arm.

Multi-Armed Bandits Thompson Sampling

Optimal Posteriors for Chi-squared Divergence based PAC-Bayesian Bounds and Comparison with KL-divergence based Optimal Posteriors and Cross-Validation Procedure

no code implementations14 Aug 2020 Puja Sahu, Nandyala Hemachandra

We investigate optimal posteriors for recently introduced \cite{begin2016pac} chi-squared divergence based PAC-Bayesian bounds in terms of nature of their distribution, scalability of computations, and test set performance.

Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis

no code implementations25 Dec 2019 Arun Verma, Manjesh K. Hanawal, Nandyala Hemachandra

In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e. g., blood test, urine test, followed by invasive tests.

feature selection Medical Diagnosis

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