Chandra Reddy
Chandra Reddy
IBM Research, TJ Watson Research Center, Yorktown Heights, NY
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Cited by
Cited by
Guiding combinatorial optimization with uct
A Sabharwal, H Samulowitz, C Reddy
International conference on integration of artificial intelligence (AI) and …, 2012
Learning goal-decomposition rules using exercises
C Reddy, P Tadepalli
ICML, 278-286, 1997
The slab-design problem in the steel industry
M Dawande, J Kalagnanam, HS Lee, C Reddy, S Siegel, M Trumbo
Interfaces 34 (3), 215-225, 2004
Learning first-order acyclic Horn programs from entailment
C Reddy, P Tadepalli
International Conference on Inductive Logic Programming, 23-37, 1998
Finishing line scheduling in the steel industry
H Okano, AJ Davenport, M Trumbo, C Reddy, K Yoda, M Amano
IBM Journal of research and development 48 (5.6), 811-830, 2004
Learning Horn definitions: Theory and an application to planning
C Reddy, P Tadepalli
New Generation Computing 17 (1), 77-98, 1999
Production design for plate products in the steel industry
S Dash, J Kalagnanam, C Reddy, SH Song
IBM journal of research and development 51 (3.4), 345-362, 2007
How to foster innovation: A data-driven approach to measuring economic competitiveness
C Kuhlman, KN Ramamurthy, P Sattigeri, AC Lozano, L Cao, C Reddy, ...
IBM Journal of Research and Development 61 (6), 11: 1-11: 12, 2017
Learning Horn definitions with equivalence and membership queries
C Reddy, P Tadepalli
International Conference on Inductive Logic Programming, 243-255, 1997
Snappy: A simple algorithm portfolio
H Samulowitz, C Reddy, A Sabharwal, M Sellmann
International Conference on Theory and Applications of Satisfiability …, 2013
Towards cognitive automation of data science
A Biem, M Butrico, M Feblowitz, T Klinger, Y Malitsky, K Ng, A Perer, ...
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
Modeling and simulation of building energy performance for portfolios of public buildings
YM Lee, F Liu, L An, H Jiang, C Reddy, R Horesh, P Nevill, E Meliksetian, ...
Proceedings of the 2011 winter simulation conference (WSC), 915-927, 2011
Theory-guided empirical speedup learning of goal decomposition rules
C Reddy, P Tadepalli, S Roncagliolo
ICML, 1996
Interpretable Clustering via Multi-Polytope Machines
C Lawless, J Kalagnanam, LM Nguyen, D Phan, C Reddy
Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7309-7316, 2022
Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph
D Patel, S Shrivastava, W Gifford, S Siegel, J Kalagnanam, C Reddy
2020 IEEE International Conference on Big Data (Big Data), 1604-1613, 2020
An application of constraint programming to generating detailed operations schedules for steel manufacturing
A Davenport, J Kalagnanam, C Reddy, S Siegel, J Hou
International Conference on Principles and Practice of Constraint …, 2007
The slab design problem in the steel industry
M Dawande, J Kalagnanam, HS Lee, C Reddy, S Siegel, M Trumbo
Handbook of Production Scheduling, 243-264, 2006
Inductive logic programming for speedup learning
C Reddy, P Tadepalli
Proceedings of the IJCAL97 workshop on Frontiers of Inductive Logic …, 1997
Providing Cooperative Data Analytics for Real Applications Using Machine Learning
A Iyengar, J Kalagnanam, D Patel, C Reddy, S Shrivastava
2019 IEEE 39th International Conference on Distributed Computing Systems …, 2019
Asset Modeling using Serverless Computing
S Jayaraman, C Reddy, E Khabiri, D Patel, A Bhamidipaty, J Kalagnanam
2021 IEEE International Conference on Big Data (Big Data), 4084-4090, 2021
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