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ORCIDSnehasis Mukhopadhyay
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- affiliation: Indiana University - Purdue University, Indianapolis, USA
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Journal Articles
- 2019
[j27]Thanh Nguyen
, Snehasis Mukhopadhyay, Meghna Babbar-Sebens:
Why the 'selfish' optimizing agents could solve the decentralized reinforcement learning problems. AI Commun. 32(2): 143-159 (2019)- 2018
- [j26]Thanh Nguyen, Snehasis Mukhopadhyay:
Two-phase selective decentralization to improve reinforcement learning systems with MDP. AI Commun. 31(4): 319-337 (2018) - 2015
- [j25]Meghna Babbar-Sebens, Snehasis Mukhopadhyay, Vidya Bhushan Singh, Adriana Debora Piemonti:
A web-based software tool for participatory optimization of conservation practices in watersheds. Environ. Model. Softw. 69: 111-127 (2015) - 2011
- [j24]Omkar J. Tilak, Snehasis Mukhopadhyay:
Partially decentralized reinforcement learning in finite, multi-agent Markov decision processes. AI Commun. 24(4): 293-309 (2011) - [j23]Omkar J. Tilak, Andrew Hoblitzell, Snehasis Mukhopadhyay, Qian You, Shiaofen Fang, Yuni Xia, Joseph Bidwell:
Multilevel text mining for bone biology. Concurr. Comput. Pract. Exp. 23(17): 2355-2364 (2011) - [j22]Omkar J. Tilak, Ryan Martin, Snehasis Mukhopadhyay:
Decentralized Indirect Methods for Learning Automata Games. IEEE Trans. Syst. Man Cybern. Part B 41(5): 1213-1223 (2011) - 2010
- [j21]Snehasis Mukhopadhyay, Mathew J. Palakal, Kalyan Maddu:
Multi-way association extraction and visualization from biological text documents using hyper-graphs: Applications to genetic association studies for diseases. Artif. Intell. Medicine 49(3): 145-154 (2010) - 2005
- [j20]Snehasis Mukhopadhyay, Shengquan Peng, Rajeev R. Raje, Javed Mostafa, Mathew J. Palakal:
Distributed multi-agent information filtering - A comparative study. J. Assoc. Inf. Sci. Technol. 56(8): 834-842 (2005) - 2004
- [j19]Rajeev R. Raje, Daocheng Zhu, Snehasis Mukhopadhyay, Liying Tang, Mathew J. Palakal, Javed Mostafa:
COBioSIFTER - A CORBA-Based Distributed Multi-Agent Biological Information Management System. Clust. Comput. 7(4): 373-389 (2004) - 2003
- [j18]Snehasis Mukhopadhyay:
Neural and adaptive systems: fundamentals through simulations: José C. Principe, Neil R. Euliano and W. Curt Lefebvre; John Wiley & Sons, Inc., USA, 2000, ISBN 0-471-35167-9. Autom. 39(7): 1313-1315 (2003) - [j17]Javed Mostafa, Snehasis Mukhopadhyay, Mathew J. Palakal:
Simulation Studies of Different Dimensions of Users' Interests and their Impact on User Modeling and Information Filtering. Inf. Retr. 6(2): 199-223 (2003) - [j16]Snehasis Mukhopadhyay, Shengquan Peng, Rajeev R. Raje, Mathew J. Palakal, Javed Mostafa:
Multi-agent information classification using dynamic acquaintance lists. J. Assoc. Inf. Sci. Technol. 54(10): 966-975 (2003) - [j15]Mathew J. Palakal, Matthew J. Stephens, Snehasis Mukhopadhyay, Rajeev R. Raje, Simon Rhodes:
Identification of Biological Relationships from Text Documentsusing Efficient Computational Methods. J. Bioinform. Comput. Biol. 1(2): 307-342 (2003) - [j14]Joby Varghese, Snehasis Mukhopadhyay:
Automated Web navigation using multiagent adaptive dynamic programming. IEEE Trans. Syst. Man Cybern. Part A 33(3): 412-417 (2003) - [j13]Snehasis Mukhopadhyay, Changhong Tang, Jeffrey Huang, Mathew J. Palakal:
Genetic Sequence Classification and its Application to Cross-Species Homology Detection. J. VLSI Signal Process. 35(3): 273-285 (2003) - 2002
- [j12]Mathew J. Palakal, Snehasis Mukhopadhyay, Javed Mostafa, Rajeev R. Raje, Mathias N'Cho, Santosh Mishra:
An intelligent biological information management system. Bioinform. 18(10): 1283-1288 (2002) - [j11]Rajeev R. Raje, Mingyong Qiao, Snehasis Mukhopadhyay, Mathew J. Palakal, Shengquan Peng, Javed Mostafa:
Homogeneous Agent-Based Distributed Information Filtering. Clust. Comput. 5(4): 377-388 (2002) - [j10]Haiying Wang, Snehasis Mukhopadhyay, Shiaofen Fang:
Feature Decomposition Architectures for Neural Networks: Algorithms, Error Bounds, and Applications. Int. J. Neural Syst. 12(1): 69-81 (2002) - 1998
- [j9]Rajeev R. Raje, Snehasis Mukhopadhyay, Michael Boyles, Nila Patel, Artur Papiez:
On designing and implementing a collaborative system using the distributed-object model of Java-RMI. Parallel Distributed Comput. Pract. 1(4) (1998) - [j8]Michael Boyles, Javed Mostafa, Snehasis Mukhopadhyay, Mathew J. Palakal, Artur Papiez, Nila Patel, Rajeev R. Raje:
A Bidding Mechanism for Web-Based Agents Involved in Information Classification. World Wide Web 1(3): 155-165 (1998) - 1997
- [j7]Kumpati S. Narendra, Snehasis Mukhopadhyay:
Adaptive control using neural networks and approximate models. IEEE Trans. Neural Networks 8(3): 475-485 (1997) - [j6]Javed Mostafa, Snehasis Mukhopadhyay, Wai Lam, Mathew J. Palakal:
A Multilevel Approach to Intelligent Information Filtering: Model, System, and Evaluation. ACM Trans. Inf. Syst. 15(4): 368-399 (1997) - 1994
- [j5]Kumpati S. Narendra, Snehasis Mukhopadhyay:
Adaptive control of nonlinear multivariable systems using neural networks. Neural Networks 7(5): 737-752 (1994) - 1993
- [j4]Snehasis Mukhopadhyay, Kumpati S. Narendra:
Disturbance rejection in nonlinear systems using neural networks. IEEE Trans. Neural Networks 4(1): 63-72 (1993) - 1991
- [j3]Kumpati S. Narendra, Snehasis Mukhopadhyay:
Associative learning in random environments using neural networks. IEEE Trans. Neural Networks 2(1): 20-31 (1991) - 1990
- [j2]Vladimir J. Lumelsky, Snehasis Mukhopadhyay, Kang Sun:
Dynamic path planning in sensor-based terrain acquisition. IEEE Trans. Robotics Autom. 6(4): 462-472 (1990) - 1989
- [j1]Snehasis Mukhopadhyay, M. A. L. Thathachar:
Associative learning of Boolean functions. IEEE Trans. Syst. Man Cybern. 19(5): 1008-1015 (1989)
Conference and Workshop Papers
- 2023
- [c52]Sabrina Chowdhury, Snehasis Mukhopadhyay, Kumpati S. Narendra:
Mutual Learning Algorithm for Kidney Cyst, Kidney Tumor and Kidney Stone Diagnosis. FedCSIS 2023: 401-410 - [c51]Karen D'Souza, Pratibha Varma-Nelson, Shiaofen Fang, Snehasis Mukhopadhyay:
Monitoring Cyber Peer-Led Team Learning: A Multimodal Human-in-the-loop Approach. HITLAML 2023 - [c50]Karen D'Souza, Lin Zhu, Pratibha Varma-Nelson, Shiaofen Fang, Snehasis Mukhopadhyay:
AI-Augmented Peer Led Team Learning for STEM Education. SACI 2023: 581-586 - [c49]Sabrina Tarin Chowdhury, Snehasis Mukhopadhyay, Kumpati S. Narendra:
Mutual Learning for Pattern Recognition. SMC 2023: 905-912 - 2022
- [c48]Kumpati S. Narendra, Snehasis Mukhopadhyay, Kasra Esfandiari:
Mutual Learning in Optimization. SMC 2022: 492-497 - 2021
- [c47]Hao Wang, Snehasis Mukhopadhyay, Yunyu Xiao, Shiaofen Fang:
An Interactive Approach to Bias Mitigation in Machine Learning. ICCI*CC 2021: 199-205 - [c46]Cameron Reid, Snehasis Mukhopadhyay:
Mutual Reinforcement Learning with Heterogenous Agents. SMARTCOMP 2021: 395-397 - 2020
- [c45]Kumpati S. Narendra, Snehasis Mukhopadhyay:
Mutual Learning: Part II -Reinforcement Learning. ACC 2020: 1105-1110 - [c44]Meghna Babbar-Sebens, Kenneth R. Cannady-Shultz, Snehasis Mukhopadhyay:
Interactive Watershed Optimization in the Presence of Spatially-varying and Uncertain Stakeholder Preferences. ICHMS 2020: 1-6 - 2019
- [c43]Kumpati S. Narendra, Snehasis Mukhopadhyay:
Mutual Learning: Part I - Learning Automata. ACC 2019: 916-921 - [c42]Andrew Hoblitzell, Meghna Babbar-Sebens, Snehasis Mukhopadhyay:
Non-Stationary Reinforcement-Learning Based Dimensionality Reduction for Multi-objective Optimization of Wetland Design. ICRAI 2019: 82-86 - 2018
- [c41]Andrew Hoblitzell, Meghna Babbar-Sebens, Snehasis Mukhopadhyay:
Uncertainty-Based Deep Learning Networks for Limited Data Wetland User Models. AIVR 2018: 19-26 - [c40]Huang Li, Shiaofen Fang, Snehasis Mukhopadhyay, Andrew J. Saykin, Li Shen:
Interactive Machine Learning by Visualization: A Small Data Solution. IEEE BigData 2018: 3513-3521 - [c39]Andrew Hoblitzell, Meghna Babbar-Sebens, Snehasis Mukhopadhyay:
Machine Learning with Small Data for User Modeling of Watershed Stakeholders Engaged in Interactive Optimization. CSAI/ICIMT 2018: 22-27 - [c38]Snehasis Mukhopadhyay, Omkar J. Tilak, Subir Chakrabarti:
Reinforcement Learning Algorithms for Uncertain, Dynamic, Zero-Sum Games. ICMLA 2018: 48-54 - 2017
- [c37]Thanh Nguyen, Snehasis Mukhopadhyay:
Multidisciplinary Optimization in Decentralized Reinforcement Learning. ICMLA 2017: 779-784 - [c36]Thanh Nguyen, Snehasis Mukhopadhyay:
Selectively decentralized Q-learning. SMC 2017: 328-333 - 2016
- [c35]Kumpati S. Narendra, Yu Wang, Snehasis Mukhopadhyay:
Fast Reinforcement Learning using multiple models. CDC 2016: 7183-7188 - [c34]Thanh Nguyen, Snehasis Mukhopadhyay:
Identification and optimal control of large-scale systems using selective decentralization. SMC 2016: 503-508 - [c33]Andrew Hoblitzell, Meghna Babbar-Sebens, Snehasis Mukhopadhyay:
Fuzzy and deep learning approaches for user modeling in wetland design. SMC 2016: 2133-2138 - 2014
- [c32]Snehasis Mukhopadhyay, Vidya Bhushan Singh, Meghna Babbar-Sebens:
User modeling with limited data: Application to stakeholder-driven watershed design. SMC 2014: 3855-3860 - 2013
- [c31]Vidya Bhushan Singh, Snehasis Mukhopadhyay, Meghna Babbar-Sebens:
User Modelling for Interactive Optimization Using Neural Network. SMC 2013: 3288-3293 - 2012
- [c30]Mansurul Bhuiyan, Snehasis Mukhopadhyay, Mohammad Al Hasan:
Interactive pattern mining on hidden data: a sampling-based solution. CIKM 2012: 95-104 - [c29]Vidya Bhushan Singh, Snehasis Mukhopadhyay, Meghna Babbar-Sebens:
Decentralized pursuit learning automata in batch mode. SCIS&ISIS 2012: 1567-1572 - 2011
- [c28]Omkar J. Tilak, Meghna Babbar-Sebens, Snehasis Mukhopadhyay:
Decentralized and partially decentralized reinforcement learning for designing a distributed wetland system in watersheds. SMC 2011: 271-276 - 2010
- [c27]Kumpati S. Narendra, Snehasis Mukhopadhyay:
To communicate or not to communicate: A decision-theoretic approach to decentralized adaptive control. ACC 2010: 6369-6376 - [c26]Rajeev R. Raje, Snehasis Mukhopadhyay, Sucheta Phatak, Rashmi Shastri, Lahiru S. Gallege:
Software Service Selection by Multi-level Matching and Reinforcement Learning. BIONETICS 2010: 310-324 - [c25]Andrew Hoblitzell, Snehasis Mukhopadhyay, Qian You, Shiaofen Fang, Yuni Xia, Joseph Bidwell:
Text mining for bone biology. HPDC 2010: 522-530 - [c24]Omkar J. Tilak, Snehasis Mukhopadhyay:
Decentralized and Partially Decentralized Reinforcement Learning for Distributed Combinatorial Optimization Problems. ICMLA 2010: 389-394 - [c23]Omkar J. Tilak, Snehasis Mukhopadhyay, Mihran Tuceryan, Rajeev R. Raje:
A novel reinforcement learning framework for sensor subset selection. ICNSC 2010: 95-100 - 2009
- [c22]Harsha Gopal Goud Vaka, Snehasis Mukhopadhyay:
Knowledge Extraction and Extrapolation Using Ancient and Modern Biomedical Literature. AINA Workshops 2009: 996-1001 - [c21]Bi-Hua Cheng, Harsha Gopal Goud Vaka, Snehasis Mukhopadhyay:
A Genetic Association Study between Breast Cancer and Osteoporosis Using Transitive Text Mining. BIBM 2009: 411-414 - [c20]Harsha Gopal Goud Vaka, Snehasis Mukhopadhyay:
Hypotheses Generation Pertaining to Ayurveda Using Automated Vocabulary Generation and Transitive Text Mining. NBiS 2009: 200-205 - [c19]Qian You, Shiaofen Fang, Snehasis Mukhopadhyay, Harsha Gopal Goud Vaka, Jake Yue Chen:
Visualizing a Correlative Multi-level Graph of Biology Entity Interactions. NBiS 2009: 304-309 - [c18]Snehasis Mukhopadhyay, Shengquan Peng, Rajeev R. Raje, Mathew J. Palakal, Javed Mostafa:
Comparison of Some Single-agent and Multi-agent Information Filtering Systems on a Benchmark Text Data Set. SEKE 2009: 185-188 - [c17]Meghna Babbar-Sebens, Snehasis Mukhopadhyay:
Reinforcement Learning for Human-Machine Collaborative Optimization: Application in Ground Water Monitoring. SMC 2009: 3563-3568 - 2008
- [c16]Snehasis Mukhopadhyay, Niranjan Jayadevaprakash:
Automated Metadata Generation and its Application to Biological Association Extraction. AINA Workshops 2008: 708-713 - [c15]Snehasis Mukhopadhyay, Mathew J. Palakal, Kalyan Maddu:
Multi-way Association Extraction from Biological Text Documents Using Hyper-Graphs. BIBM 2008: 257-262 - 2005
- [c14]Niranjan Jayadevaprakash, Snehasis Mukhopadhyay, Mathew J. Palakal:
Generating association graphs of non-cooccurring text objects using transitive methods. SAC 2005: 141-145 - 2004
- [c13]Pooja Bajracharya, Snehasis Mukhopadhyay:
A learning approach to the database selection problem in the presence of dynamic user interests and database contents. ASIST 2004: 239-248 - [c12]Kamal Kumar, Mathew J. Palakal, Snehasis Mukhopadhyay, Matthew J. Stephens, Huian Li:
BioMap: toward the development of a knowledge base of biomedical literature. SAC 2004: 121-127 - 2002
- [c11]Mathew J. Palakal, Matthew J. Stephens, Snehasis Mukhopadhyay, Rajeev R. Raje, Simon Rhodes:
A Multi-Level Text Mining Method to Extract Biological Relationships. CSB 2002: 97-108 - [c10]Snehasis Mukhopadhyay, Changhong Tang, Jeffrey Huang, Mulong Yu, Mathew J. Palakal:
A comparative study of genetic sequence classification algorithms. NNSP 2002: 57-66 - [c9]Mathew J. Palakal, Snehasis Mukhopadhyay, Javed Mostafa:
An intelligent biological information management system. SAC 2002: 159-163 - [c8]Yueyu Fu, Travis Bauer, Javed Mostafa, Mathew J. Palakal, Snehasis Mukhopadhyay:
Concept extraction and association from cancer literature. WIDM 2002: 100-103 - 2001
- [c7]Shengquan Peng, Snehasis Mukhopadhyay, Rajeev R. Raje, Mathew J. Palakal, Javed Mostafa:
A Comparison Between Single-agent and Multi-agent Classification of Documents. IPDPS 2001: 90 - [c6]Matthew J. Stephens, Mathew J. Palakal, Snehasis Mukhopadhyay, Rajeev R. Raje, Javed Mostafa:
Detecting Gene Relations from MEDLINE Abstracts. Pacific Symposium on Biocomputing 2001: 483-496 - [c5]Rajeev R. Raje, Mingyong Qiao, Snehasis Mukhopadhyay:
SIFTER-II: a heterogeneous agent society for information filtering. SAC 2001: 121-123 - 2000
- [c4]Snehasis Mukhopadhyay, Joby Varghese:
Multi-agent Adaptive Dynamic Programming. MICAI 2000: 574-585 - 1996
- [c3]Wai Lam, Snehasis Mukhopadhyay:
A Two-Level Approach to Learning in Nonstationary Environments. AI 1996: 271-283 - [c2]Wai Lam, Snehasis Mukhopadhyay, Javed Mostafa, Mathew J. Palakal:
Detection of Shifts in User Interests for Personalized Information Filtering. SIGIR 1996: 317-325 - 1989
- [c1]Vladimir J. Lumelsky, Snehasis Mukhopadhyay, Kang Sun:
Sensor-Based Terrain Acquisition: a "Seed Spreader" Strategy. IROS 1989: 62-67
Editorship
- 2016
- [e1]Snehasis Mukhopadhyay, ChengXiang Zhai, Elisa Bertino, Fabio Crestani, Javed Mostafa, Jie Tang, Luo Si, Xiaofang Zhou, Yi Chang, Yunyao Li, Parikshit Sondhi:
Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016. ACM 2016, ISBN 978-1-4503-4073-1 [contents]
Coauthor Index
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