Theory-guided Empirical Speedup Learning of Goal Decomposition Rules. - Chandra Reddy; Prasad Tadepalli; Silvana Roncagliolo (ICML, 1996)

Learning Goal-Decomposition Rules using Exercises. - Chandra Reddy; Prasad Tadepalli (ICML, 1997)

Learning Horn Definitions with Equivalence and Membership Queries. - Chandra Reddy; Prasad Tadepalli (ILP, 1997)

Learning First-Order Acyclic Horn Programs from Entailment. - Chandra Reddy; Prasad Tadepalli (ICML, 1998)

Learning Horn Definitions: Theory and an Application to Planning. - Chandra Reddy; Prasad Tadepalli (New Gener. Comput., 1999)

Finishing Line Scheduling in the steel industry. - Hiroyuki Okano; Andrew J. Davenport; Mark Trumbo; Chandra Reddy; Kunikazu Yoda; Masami Amano (IBM J. Res. Dev., 2004)

The Slab-Design Problem in the Steel Industry. - Milind Dawande; Jayant Kalagnanam; Ho Soo Lee; Chandra Reddy; Stuart Siegel; Mark Trumbo (Interfaces, 2004)

An Application of Constraint Programming to Generating Detailed Operations Schedules for Steel Manufacturing. - Andrew J. Davenport; Jayant Kalagnanam; Chandra Reddy; Stuart Siegel; John Hou (CP, 2007)

Production design for plate products in the steel industry. - Sanjeeb Dash; Jayant Kalagnanam; Chandra Reddy; Sang Hwa Song (IBM J. Res. Dev., 2007)

Modeling and simulation of building energy performance for portfolios of public buildings. - Young M. Lee; Fei Liu; Lianjun An; Huijing Jiang; Chandra Reddy; Raya Horesh; Paul Nevill; Estepan Meliksetian; Pawan Chowdhary; Nat Mills; Young Tae Chae; Jane L. Snowdon; et al. (WSC, 2011)

Guiding Combinatorial Optimization with UCT. - Ashish Sabharwal; Horst Samulowitz; Chandra Reddy (CPAIOR, 2012)

Snappy: A Simple Algorithm Portfolio. - Horst Samulowitz; Chandra Reddy; Ashish Sabharwal; Meinolf Sellmann (SAT, 2013)

Towards Cognitive Automation of Data Science. - Alain Biem; Maria Butrico; Mark Feblowitz; Tim Klinger; Yuri Malitsky; Kenney Ng; Adam Perer; Chandra Reddy; Anton Riabov; Horst Samulowitz; Daby M. Sow; Gerald Tesauro; et al. (AAAI, 2015)

Providing Cooperative Data Analytics for Real Applications Using Machine Learning. - Arun Iyengar; Jayant Kalagnanam; Dhaval Patel 002; Chandra Reddy; Shrey Shrivastava (ICDCS, 2019)

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph. - Dhaval Patel 002; Shrey Shrivastava; Wesley M. Gifford; Stuart Siegel; Jayant Kalagnanam; Chandra Reddy (IEEE BigData, 2020)

Asset Modeling using Serverless Computing. - Srideepika Jayaraman; Chandra Reddy; Elham Khabiri; Dhaval Patel 002; Anuradha Bhamidipaty; Jayant Kalagnanam (IEEE BigData, 2021)

Interpretable Clustering via Multi-Polytope Machines. - Connor Lawless; Jayant Kalagnanam; Lam M. Nguyen; Dzung T. Phan; Chandra Reddy (AAAI, 2022)

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series. - Vijay Ekambaram; Arindam Jati; Pankaj Dayama 001; Sumanta Mukherjee; Nam Nguyen; Wesley M. Gifford; Chandra Reddy; Jayant Kalagnanam (NeurIPS, 2024)

Multi-polytope Machine for Classification. - Dzung T. Phan; Lam M. Nguyen; Jayant Kalagnanam; Chandra Reddy (SDM, 2024)

Identifying Homogeneous and Interpretable Groups for Conformal Prediction. - Natalia Martinez Gil; Dhaval Patel 002; Chandra Reddy; Giridhar Ganapavarapu; Roman Vaculín; Jayant Kalagnanam (UAI, 2024)

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series. - Vijay Ekambaram; Arindam Jati; Nam H. Nguyen; Pankaj Dayama 001; Chandra Reddy; Wesley M. Gifford; Jayant Kalagnanam (CoRR, 2024)