Nandan Sudarsanam
Nandan Sudarsanam
Verified email at iitm.ac.in
Title
Cited by
Cited by
Year
Regularities in data from factorial experiments
X Li, N Sudarsanam, DD Frey
Complexity 11 (5), 32-45, 2006
1132006
An adaptive one-factor-at-a-time method for robust parameter design: Comparison with crossed arrays via case studies
DD Frey, N Sudarsanam
172008
Thresholding bandits with augmented ucb
S Mukherjee, KP Naveen, N Sudarsanam, B Ravindran
arXiv preprint arXiv:1704.02281, 2017
162017
Efficient-ucbv: An almost optimal algorithm using variance estimates
S Mukherjee, KP Naveen, N Sudarsanam, B Ravindran
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
72018
Rate of change analysis for interestingness measures
N Sudarsanam, N Kumar, A Sharma, B Ravindran
Knowledge and Information Systems 62 (1), 239-258, 2020
52020
Using linear stochastic bandits to extend traditional offline designed experiments to online settings
N Sudarsanam, B Ravindran
Computers & Industrial Engineering 115, 471-485, 2018
42018
Using ensemble techniques to advance adaptive one‐factor‐at‐a‐time experimentation
N Sudarsanam, DD Frey
Quality and Reliability Engineering International 27 (7), 947-957, 2011
42011
Quantifying and predicting prepayments in the microfinance environment
N Sudarsanam, DJ Philip
NSE-IFMR Finance Foundation Financial Deepening and Household Finance …, 2016
22016
Optimal replicates for designed experiments under the online framework
N Sudarsanam, BP Kannu, DD Frey
Research in Engineering Design 30 (3), 363-379, 2019
12019
Improved insights on financial health through partially constrained hidden Markov model clustering on loan repayment data
DJ Philip, N Sudarsanam, B Ravindran
ACM SIGMIS Database: the DATABASE for Advances in Information Systems 49 (3 …, 2018
12018
Linear Bandit algorithms using the Bootstrap
N Sudarsanam, B Ravindran
arXiv preprint arXiv:1605.01185, 2016
12016
Inferring customer occupancy status in for-hire vehicles using PU Learning
V Muralidharan, N Sudarsanam, B Ravindran
8th ACM IKDD CODS and 26th COMAD, 290-298, 2021
2021
Designing Dynamic Interventions to Improve Adherence in Pediatric Long-term Treatment–The Role of Perceived Value of the Physician by Primary Caregivers
K Venkatraman, V Vijayalakshmi, N Sudarsanam, A Manoharan
Health Communication, 1-16, 2020
2020
Conducting Non-adaptive Experiments in a Live Setting: A Bayesian Approach to Determining Optimal Sample Size
N Sudarsanam, R Chandran, DD Frey
Journal of Mechanical Design 142 (3), 031108, 2020
2020
Influence of Caregiver Perceived Value of Physician on Adherence in Paediatric Long-term Treatment
K Venkat Raman, N Sudarsanam, V Vijayalakshmi
Academy of Management Proceedings 2019 (1), 15099, 2019
2019
An Active Learning Framework for Efficient Robust Policy Search
S Kiran Narayanaswami, N Sudarsanam, B Ravindran
arXiv e-prints, arXiv: 1901.00117, 2019
2019
An Active Learning Framework for Efficient Robust Policy Search
SK Narayanaswami, N Sudarsanam, B Ravindran
arXiv preprint arXiv:1901.00117, 2019
2019
A Partial Parameter HMM Based Clustering on Loan Repayment Data: Insights into Financial Behavior and Intent to Repay
D Philip, N Sudarsanam, B Ravindran
Proceedings of the 51st Hawaii International Conference on System Sciences, 2018
2018
NOC: Introduction to Data Analytics
N Sudarsanam, B Ravindran
National Programme on Technology Enhanced Learning (NPTEL), 2015
2015
Ensembles of Adaptive One-Factor at-a-Time experiments: methods, evaluation, and theory
N Sudarsanam
Massachusetts Institute of Technology, 2008
2008
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