Tadepalli, Prasad

87 publications

NeurIPS 2025 Graph Neural Network Based Action Ranking for Planning Rajesh Devaraddi Mangannavar, Stefan Lee, Alan Fern, Prasad Tadepalli

AAAI 2025 Self-Attention-Based Diffusion Model for Time-Series Imputation in Partial Blackout Scenarios Mohammad Rafid Ul Islam, Prasad Tadepalli, Alan Fern

ICML 2024 Adversarial Attacks on Combinatorial Multi-Armed Bandits Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang, Qingyun Wu, Haoyu Zhao

MLJ 2024 Explainable Models via Compression of Tree Ensembles Siwen Yan, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli

AAAI 2023 Global Explanations for Image Classifiers (Student Abstract) Bhavan K. Vasu, Prasad Tadepalli

NeurIPSW 2022 Formalizing the Problem of Side Effect Regularization Alexander Matt Turner, Aseem Saxena, Prasad Tadepalli

NeurIPS 2022 Parametrically Retargetable Decision-Makers Tend to Seek Power Alex Turner, Prasad Tadepalli

NeurIPSW 2021 Deep RePReL--Combining Planning and Deep RL for Acting in Relational Domains Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli

ICLR 2021 DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs Aayam Kumar Shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern

NeurIPS 2021 One Explanation Is Not Enough: Structured Attention Graphs for Image Classification Vivswan Shitole, Fuxin Li, Minsuk Kahng, Prasad Tadepalli, Alan Fern

NeurIPS 2021 Optimal Policies Tend to Seek Power Alex Turner, Logan Smith, Rohin Shah, Andrew Critch, Prasad Tadepalli

AAAI 2021 PAC Learning of Causal Trees with Latent Variables Prasad Tadepalli, Stuart J. Russell

NeurIPS 2020 Avoiding Side Effects in Complex Environments Alex Turner, Neale Ratzlaff, Prasad Tadepalli

NeurIPSW 2020 Human Adversarial QA: Did the Model Understand the Paragraph? Prachi Shriram Rahurkar, Matthew Lyle Olson, Prasad Tadepalli

AAAI 2020 The Choice Function Framework for Online Policy Improvement Murugeswari Issakkimuthu, Alan Fern, Prasad Tadepalli

IJCAI 2018 Emergency Response Optimization Using Online Hybrid Planning Durga Harish Dayapule, Aswin Raghavan, Prasad Tadepalli, Alan Fern

ALT 2017 Adaptive Submodularity with Varying Query Sets: An Application to Active Multi-Label Learning Alan Fern, Robby Goetschalckx, Mandana Hamidi-Haines, Prasad Tadepalli

AAAI 2017 Hindsight Optimization for Hybrid State and Action MDPs Aswin Raghavan, Scott Sanner, Roni Khardon, Prasad Tadepalli, Alan Fern

ACML 2017 Multi-Task Structured Prediction for Entity Analysis: Search-Based Learning Algorithms Chao Ma, Janardhan Rao Doppa, Prasad Tadepalli, Hamed Shahbazi, Xiaoli Fern

IJCAI 2015 Active Imitation Learning of Hierarchical Policies Mandana Hamidi, Prasad Tadepalli, Robby Goetschalckx, Alan Fern

AAAI 2015 Factored MCTS for Large Scale Stochastic Planning Hao Cui, Roni Khardon, Alan Fern, Prasad Tadepalli

AAAI 2015 Learning Greedy Policies for the Easy-First Framework Jun Xie, Chao Ma, Janardhan Rao Doppa, Prashanth Mannem, Xiaoli Z. Fern, Thomas G. Dietterich, Prasad Tadepalli

UAI 2015 Memory-Effcient Symbolic Online Planning for Factored MDPs Aswin Raghavan, Roni Khardon, Prasad Tadepalli, Alan Fern

IJCAI 2015 Multitask Coactive Learning Robby Goetschalckx, Alan Fern, Prasad Tadepalli

JAIR 2014 A Decision-Theoretic Model of Assistance Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli

JMLR 2014 Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning Kshitij Judah, Alan P. Fern, Thomas G. Dietterich, Prasad Tadepalli

AAAI 2014 Coactive Learning for Locally Optimal Problem Solving Robby Goetschalckx, Alan Fern, Prasad Tadepalli

AAAI 2014 HC-Search for Multi-Label Prediction: An Empirical Study Janardhan Rao Doppa, Jun Yu, Chao Ma, Alan Fern, Prasad Tadepalli

JAIR 2014 HC-Search: A Learning Framework for Search-Based Structured Prediction Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli

AAAI 2014 Imitation Learning with Demonstrations and Shaping Rewards Kshitij Judah, Alan Fern, Prasad Tadepalli, Robby Goetschalckx

AAAI 2014 Learning Scripts as Hidden Markov Models John Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Z. Fern, Thomas G. Dietterich

JMLR 2014 Structured Prediction via Output Space Search Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli

JMLR 2014 Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning Aaron Wilson, Alan Fern, Prasad Tadepalli

AAAI 2013 HC-Search: Learning Heuristics and Cost Functions for Structured Prediction Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli

ECML-PKDD 2013 Solving Relational MDPs with Exogenous Events and Additive Rewards Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, Alan Fern

NeurIPS 2013 Symbolic Opportunistic Policy Iteration for Factored-Action MDPs Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli

NeurIPS 2012 A Bayesian Approach for Policy Learning from Trajectory Preference Queries Aaron Wilson, Alan Fern, Prasad Tadepalli

ICML 2012 Output Space Search for Structured Prediction Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli

AAAI 2012 Planning in Factored Action Spaces with Symbolic Dynamic Programming Aswin Raghavan, Saket Joshi, Alan Fern, Prasad Tadepalli, Roni Khardon

NeurIPS 2011 Autonomous Learning of Action Models for Planning Neville Mehta, Prasad Tadepalli, Alan Fern

IJCAI 2011 Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik

NeurIPS 2011 Inverting Grice's Maxims to Learn Rules from Natural Language Extractions Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich, Xiaoli Z. Fern

ACML 2011 Learning Rules from Incomplete Examples via Implicit Mention Models Janardhan Rao Doppa, Mohammad Shahed Sorower, Mohammad Nasresfahani, Jed Irvine, Walker Orr, Thomas G. Dietterich, Xiaoli Fern, Prasad Tadepalli

MLJ 2011 The First Learning Track of the International Planning Competition Alan Fern, Roni Khardon, Prasad Tadepalli

NeurIPS 2010 A Computational Decision Theory for Interactive Assistants Alan Fern, Prasad Tadepalli

AAAI 2010 Bayesian Policy Search for Multi-Agent Role Discovery Aaron Wilson, Alan Fern, Prasad Tadepalli

ECML-PKDD 2010 Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik

ECML-PKDD 2010 Incorporating Domain Models into Bayesian Optimization for RL Aaron Wilson, Alan Fern, Prasad Tadepalli

ECML-PKDD 2010 Learning Algorithms for Link Prediction Based on Chance Constraints Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, Lise Getoor

MLJ 2009 Guest Editorial: Special Issue on Structured Prediction Charles Parker, Yasemin Altun, Prasad Tadepalli

ICML 2008 Automatic Discovery and Transfer of MAXQ Hierarchies Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich

MLJ 2008 Guest Editors' Introduction: Special Issue on Inductive Logic Programming (ILP-2007) Hendrik Blockeel, Jude W. Shavlik, Prasad Tadepalli

MLJ 2008 Structured Machine Learning: The Next Ten Years Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli

MLJ 2008 Transfer in Variable-Reward Hierarchical Reinforcement Learning Neville Mehta, Sriraam Natarajan, Prasad Tadepalli, Alan Fern

IJCAI 2007 A Decision-Theoretic Model of Assistance Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli

ICML 2007 Learning for Efficient Retrieval of Structured Data with Noisy Queries Charles Parker, Alan Fern, Prasad Tadepalli

ICML 2007 Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepalli

AAAI 2006 Gradient Boosting for Sequence Alignment Charles Parker, Alan Fern, Prasad Tadepalli

ECML-PKDD 2006 Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery Scott Proper, Prasad Tadepalli

ICML 2005 Dynamic Preferences in Multi-Criteria Reinforcement Learning Sriraam Natarajan, Prasad Tadepalli

ICML 2005 Learning First-Order Probabilistic Models with Combining Rules Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar

ICML 2002 Learning Decision Rules by Randomized Iterative Local Search Michael Chisholm, Prasad Tadepalli

ICML 2002 Model-Based Hierarchical Average-Reward Reinforcement Learning Sandeep Seri, Prasad Tadepalli

MLJ 2001 On Exact Learning of Unordered Tree Patterns Thomas R. Amoth, Paul Cull, Prasad Tadepalli

COLT 1999 Exact Learning of Unordered Tree Patterns from Queries Thomas R. Amoth, Paul Cull, Prasad Tadepalli

COLT 1998 Exact Learning of Tree Patterns from Queries and Counterexamples Thomas R. Amoth, Paul Cull, Prasad Tadepalli

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

MLJ 1998 Learning from Examples and Membership Queries with Structured Determinations Prasad Tadepalli, Stuart Russell

AAAI 1997 Active Learning with Committees Ray Liere, Prasad Tadepalli

AAAI 1997 Active Learning with Committees for Text Categorization Ray Liere, Prasad Tadepalli

ICML 1997 Hierarchical Explanation-Based Reinforcement Learning Prasad Tadepalli, Thomas G. Dietterich

AAAI 1997 Learning Goal-Decomposition Rules Using Exercises Chandra Reddy, Prasad Tadepalli

ICML 1997 Learning Goal-Decomposition Rules Using Exercises Chandra Reddy, Prasad Tadepalli

JAIR 1996 A Formal Framework for Speedup Learning from Problems and Solutions Prasad Tadepalli, Balas K. Natarajan

AAAI 1996 Auto-Exploratory Average Reward Reinforcement Learning DoKyeong Ok, Prasad Tadepalli

ICML 1996 Scaling up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function Prasad Tadepalli, DoKyeong Ok

ICML 1996 Theory-Guided Empirical Speedup Learning of Goal Decomposition Rules Chandra Reddy, Prasad Tadepalli, Silvana Roncagliolo

MLJ 1994 Quantifying Prior Determination Knowledge Using the PAC Learning Model Sridhar Mahadevan, Prasad Tadepalli

ICML 1993 Learning from Queries and Examples with Tree-Structured Bias Prasad Tadepalli

AAAI 1992 A Theory of Unsupervised Speedup Learning Prasad Tadepalli

IJCAI 1991 A Formalization of Explanation-Based Macro-Operator Learning Prasad Tadepalli

ICML 1991 Learning with Incrutable Theories Prasad Tadepalli

IJCAI 1989 Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem Prasad Tadepalli

ICML 1989 Planning Approximate Plans for Use in the Real World Prasad Tadepalli

ICML 1988 On the Tractability of Learning from Incomplete Theories Sridhar Mahadevan, Prasad Tadepalli

ICML 1988 Two New Frameworks for Learning Balas K. Natarajan, Prasad Tadepalli

AAAI 1987 Optimizing the Predictive Value of Diagnostic Decision Rules Sholom M. Weiss, Robert S. Galen, Prasad Tadepalli