Prasad Tadepalli

发表

Multi-Task Structured Prediction for Entity Analysis: Search-Based Learning Algorithms

Xiaoli Z. Fern, Chao Ma, Janardhan Rao Doppa, 2017, ACML.

Lower Bounding Klondike Solitaire with Monte-Carlo Planning

Alan Fern, Prasad Tadepalli, Ronald Bjarnason, 2009, ICAPS.

Prune-and-Score: Learning for Greedy Coreference Resolution

Thomas G. Dietterich, Xiaoli Z. Fern, Chao Ma, 2014, EMNLP.

Planning in Factored Action Spaces with Symbolic Dynamic Programming

Alan Fern, Prasad Tadepalli, Roni Khardon, 2012, AAAI.

Planning Approximate Plans for Use in the Real World

Prasad Tadepalli, 1989, ML.

A Decision-Theoretic Model of Assistance

Sriraam Natarajan, Alan Fern, Prasad Tadepalli, 2007, IJCAI.

Hybrid Computational Models for Skill Acquisition

Thomas G. Dietterich, Prasad Tadepalli, 1998 .

Multiagent Transfer Learning via Assignment-Based Decomposition

Prasad Tadepalli, Scott Proper, 2009, 2009 International Conference on Machine Learning and Applications.

Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation pdf

Xiaoli Z. Fern, Prasad Tadepalli, Hamed Shahbazi, 2019, ArXiv.

Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule

Sriraam Natarajan, Prasad Tadepalli, Jude W. Shavlik, 2009, 2009 International Conference on Machine Learning and Applications.

The Use of Active Learning in Text Categorization

Prasad Tadepalli, Ray Liere, 1996 .

Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach

Kristian Kersting, Sriraam Natarajan, Prasad Tadepalli, 2011, IJCAI.

Best Of NIPS 2005: Highlights on the 'Inductive Transfer : 10 Years Later' Workshop

Massimiliano Pontil, Rich Caruana, Prasad Tadepalli, 2006 .

A Theory of Unsupervised Speedup Learning

Prasad Tadepalli, Prasad Tadepalli, 1992, AAAI.

Partial Planning Reinforcement Learning

Alan Fern, Prasad Tadepalli, 2012 .

Dependent Gated Reading for Cloze-Style Question Answering

Xiaoli Z. Fern, Prasad Tadepalli, Hamed Shahbazi, 2018, COLING.

An Apprentice-Based Approach to Knowledge Acquisition

Tom M. Mitchell, Jack Mostow, Sridhar Mahadevan, 1993, Artif. Intell..

Joint Neural Entity Disambiguation with Output Space Search pdf

Xiaoli Z. Fern, Chao Ma, Prasad Tadepalli, 2018, COLING.

A Bayesian Approach for Policy Learning from Trajectory Preference Queries

Alan Fern, Prasad Tadepalli, Aaron Wilson, 2012, NIPS.

Solving multiagent assignment Markov decision processes

Prasad Tadepalli, Scott Proper, Prasad Tadepalli, 2009, AAMAS.

Learning Greedy Policies for the Easy-First Framework

Thomas G. Dietterich, Xiaoli Z. Fern, Chao Ma, 2015, AAAI.

Active learning with committees: an approach to efficient learning in text categorization using linear threshold algorithms

Prasad Tadepalli, Ray Liere, Prasad Tadepalli, 2000 .

Event Nugget Detection with Forward-Backward Recurrent Neural Networks pdf

Xiaoli Z. Fern, Prasad Tadepalli, Reza Ghaeini, 2016, ACL.

Relation Extraction with Explanation pdf

Xiaoli Z. Fern, Prasad Tadepalli, Hamed Shahbazi, 2020, ACL.

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models

Kristian Kersting, Sriraam Natarajan, Prasad Tadepalli, 2010, StarAI@AAAI.

Imitation Learning with Demonstrations and Shaping Rewards

Alan Fern, Prasad Tadepalli, Kshitij Judah, 2014, AAAI.

Active Learning of Hierarchical Policies from State-Action Trajectories

Alan Fern, Prasad Tadepalli, Robby Goetschalckx, 2015 .

Label Embedding Approach for Transfer Learning

Xiaoli Z. Fern, Prasad Tadepalli, Rasha Obeidat, 2016, ICBO/BioCreative.

Event Detection with Neural Networks: A Rigorous Empirical Evaluation pdf

Xiaoli Z. Fern, Prasad Tadepalli, John Walker Orr, 2018, EMNLP.

Coactive Learning for Locally Optimal Problem Solving

Alan Fern, Prasad Tadepalli, Robby Goetschalckx, 2014, AAAI.

Multitask Coactive Learning

Alan Fern, Prasad Tadepalli, Robby Goetschalckx, 2015, IJCAI.

Area Under Curve

Sanjoy Dasgupta, Hannu Toivonen, Pieter Abbeel, 2020, Encyclopedia of Machine Learning.

Market Basket Analysis

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning.

Conservative Agency

Dylan Hadfield-Menell, Prasad Tadepalli, Alexander Matt Turner, 2019, AISafety@IJCAI.

Knowledge based planning in games

Prasad Tadepalli, Prasad Tadepalli, 1989 .

Towards learning rules from natural texts

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2010, HLT-NAACL 2010.

Active Imitation Learning of Hierarchical Policies

Alan Fern, Prasad Tadepalli, Robby Goetschalckx, 2015, IJCAI.

KI-LEARN: Knowledge-Intensive Learning Methods for Knowledge-Rich/Data-Poor Domains

Thomas G. Dietterich, Alan Fern, Jianqiang Shen, 2006 .

Learning first-order probabilistic models with combining rules

Thomas G. Dietterich, Sriraam Natarajan, Alan Fern, 2005, Annals of Mathematics and Artificial Intelligence.

Learning Horn Definitions with Equivalence and Membership Queries

Prasad Tadepalli, Chandra Reddy, 1997, ILP.

Memory-Effcient Symbolic Online Planning for Factored MDPs

Alan Fern, Prasad Tadepalli, Roni Khardon, 2015, UAI.

Learning Rules from Incomplete Examples via Implicit Mention Models

Thomas G. Dietterich, Jed Irvine, Xiaoli Z. Fern, 2011, ACML.

Dynamic preferences in multi-criteria reinforcement learning

Sriraam Natarajan, Prasad Tadepalli, Prasad Tadepalli, 2005, ICML.

The Choice Function Framework for Online Policy Improvement pdf

Prasad Tadepalli, Murugeswari Issakkimuthu, Alan Fern, 2020, AAAI.

Transfer Learning in Sequential Decision Problems: A Hierarchical Bayesian Approach

Alan Fern, Prasad Tadepalli, Aaron Wilson, 2012, ICML Unsupervised and Transfer Learning.

Hierarchical structure discovery and transfer in sequential decision problems

Prasad Tadepalli, Neville Mehta, N. Mehta, 2011 .

Learning Algorithms for Link Prediction Based on Chance Constraints

Jun Yu, Lise Getoor, Janardhan Rao Doppa, 2010, ECML/PKDD.

Output Space Search for Structured Prediction

Alan Fern, Janardhan Rao Doppa, Prasad Tadepalli, 2012, ICML.

Hindsight Optimization for Probabilistic Planning with Factored Actions

Alan Fern, Prasad Tadepalli, Roni Khardon, 2015, ICAPS.

Gradient Boosting for Sequence Alignment

Alan Fern, Prasad Tadepalli, Charles Parker, 2006, AAAI.

Learning Decision Rules by Randomized Iterative Local Search

Prasad Tadepalli, Michael Chisholm, Prasad Tadepalli, 2002, ICML.

Searching Solitaire in Real Time

Alan Fern, Prasad Tadepalli, Ronald Bjarnason, 2007, J. Int. Comput. Games Assoc..

Symbolic Opportunistic Policy Iteration for Factored-Action MDPs

Alan Fern, Prasad Tadepalli, Roni Khardon, 2013, NIPS.

Partial Planning Reinforcement Learning: Final Report

Alan Fern, Prasad Tadepalli, 2012 .

Model-Based Reinforcement Learning

Prasad Tadepalli, Soumya Ray, Prasad Tadepalli, 2010, Encyclopedia of Machine Learning and Data Mining.

Model-Based Reinforcement Learning

Prasad Tadepalli, Soumya Ray, 2010 .

HC-Search: A Learning Framework for Search-based Structured Prediction

Alan Fern, Janardhan Rao Doppa, Prasad Tadepalli, 2014, J. Artif. Intell. Res..

Automatic discovery and transfer of MAXQ hierarchies

Thomas G. Dietterich, Prasad Tadepalli, Soumya Ray, 2008, ICML '08.

Active Learning with Committees in Text Categorization: Preliminary Results in Comparing Winnow and Perceptron

Prasad Tadepalli, Ray Liere, 1998 .

Model-Based Average Reward Reinforcement Learning

Prasad Tadepalli, DoKyeong Ok, Prasad Tadepalli, 1998, Artif. Intell..

Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery

Prasad Tadepalli, Scott Proper, Scott Proper, 2006, ECML.

Mean Squared Error

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning.

Learning Horn definitions: Theory and an application to planning

Prasad Tadepalli, Chandra Reddy, Prasad Tadepalli, 2009, New Generation Computing.

Learning hierarchical decomposition rules for planning: an inductive logic programming approach

Prasad Tadepalli, Chandrasekhara K. Reddy, Prasad Tadepalli, 1999 .

Exact learning of unordered tree patterns from queries

Paul Cull, Prasad Tadepalli, Thomas R. Amoth, 1999, COLT '99.

Relational Markov Decision Processes: Promise and Prospects

Alan Fern, Prasad Tadepalli, Roni Khardon, 2013, StarAI@AAAI.

Optimizing Earth Moving Operations Via Reinforcement Learning

Prasad Tadepalli, Joseph Louis, Vivswan Shitole, 2019, 2019 Winter Simulation Conference (WSC).

Auto-Exploratory Average Reward Reinforcement Learning

Prasad Tadepalli, DoKyeong Ok, Prasad Tadepalli, 1996, AAAI/IAAI, Vol. 1.

Conservative Agency via Attainable Utility Preservation

Dylan Hadfield-Menell, Prasad Tadepalli, Alexander Matt Turner, 2019, AIES.

Learning Goal-Decomposition Rules Using Exercises

Prasad Tadepalli, Chandra Reddy, Prasad Tadepalli, 1997, AAAI/IAAI.

Deep Reading and Learning

Thomas G. Dietterich, Xiaoli Z. Fern, Prasad Tadepalli, 2017 .

Simulation-based Optimization of Resource Placement and Emergency Response

Alan Fern, Prasad Tadepalli, Ronald Bjarnason, 2009, IAAI.

Autonomous Learning of Action Models for Planning

Alan Fern, Prasad Tadepalli, Neville Mehta, 2011, NIPS.

Learning and transferring roles in multi-agent MDPs

Alan Fern, Prasad Tadepalli, Aaron Wilson, 2008, AAAI 2008.

Learning First-Order Acyclic Horn Programs from Entailment

Prasad Tadepalli, Chandra Reddy, 1998, ILP.

Multistrategy Ensemble Learning

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning.

Relational Reinforcement Learning: An Overview

Robert Givan, Prasad Tadepalli, Kurt Driessens, 2004, ICML 2004.

Learning first-order probabilistic models with combining rules

Thomas G. Dietterich, Sriraam Natarajan, Alan Fern, 2009, Annals of Mathematics and Artificial Intelligence.

On Exact Learning of Unordered Tree Patterns

Paul Cull, Prasad Tadepalli, Thomas R. Amoth, 2001, Machine Learning.

HC-Search: Learning Heuristics and Cost Functions for Structured Prediction

Alan Fern, Janardhan Rao Doppa, Prasad Tadepalli, 2013, AAAI.

A Formalization of Explanation-Based Macro-operator Learning

Prasad Tadepalli, Prasad Tadepalli, 1991, IJCAI.

On the Tractability of Learning from Incomplete Theories

Sridhar Mahadevan, Prasad Tadepalli, 1988, ML.

Active Learning with Committees

Prasad Tadepalli, Ray Liere, 1997, AAAI/IAAI.

Multi-Armed Bandit

Johannes Fürnkranz, Luís Torgo, Katharina Morik, 2010, Encyclopedia of Machine Learning.

Missing Values

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning.

Saliency Learning: Teaching the Model Where to Pay Attention pdf

Xiaoli Z. Fern, Prasad Tadepalli, Hamed Shahbazi, 2019, NAACL-HLT.

Model-based Hierarchical Average-reward Reinforcement Learning

Prasad Tadepalli, Sandeep Seri, Prasad Tadepalli, 2002, ICML.

Learning Scripts as Hidden Markov Models

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2014, AAAI.

Learning from Examples and Membership Queries with Structured Determinations

Prasad Tadepalli, Stuart J. Russell, P. Tadepalli, 1998, Machine Learning.

Hindsight Optimization for Hybrid State and Action MDPs

Scott Sanner, Alan Fern, Prasad Tadepalli, 2017, AAAI.

Proceedings of the 17th international conference on Inductive logic programming

Hendrik Blockeel, Prasad Tadepalli, Jude Shavlik, 2007 .

Learning from Queries and Examples with Tree-structured Bias

Prasad Tadepalli, Prasad Tadepalli, P. Tadepalli, 1993, ICML.

H-Learning: A Reinforcement Learning Method for Optimizing Undiscounted Average Reward

Prasad Tadepalli, DoKyeong Ok, Prasad Tadepalli, 1994 .

Real-time scheduling using minimum search

Prasad Tadepalli, Varad Joshi, 1992 .

Automatic Discovery and Transfer of Task Hierarchies in Reinforcement Learning

Thomas G. Dietterich, Prasad Tadepalli, Soumya Ray, 2011, AI Mag..

HC-Search for Multi-Label Prediction: An Empirical Study

Jun Yu, Alan Fern, Chao Ma, 2014, AAAI.

On the Sub-Layer Functionalities of Transformer Decoder

Shuming Shi, Zhaopeng Tu, Prasad Tadepalli, 2020, FINDINGS.

Description-Based Zero-shot Fine-Grained Entity Typing

Xiaoli Z. Fern, Prasad Tadepalli, Hamed Shahbazi, 2019, NAACL.

Active lmitation learning: formal and practical reductions to I.I.D. learning

Thomas G. Dietterich, Alan Fern, Prasad Tadepalli, 2014, J. Mach. Learn. Res..

A Computational Decision Theory for Interactive Assistants

Alan Fern, Prasad Tadepalli, Alan Fern, 2010, Interactive Decision Theory and Game Theory.

Inductive Logic Programming, 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers

Jan Ramon, Prasad Tadepalli, Jude Shavlik, 2008, ILP.

Exact learning of tree patterns from queries and counterexamples

Paul Cull, Prasad Tadepalli, Thomas R. Amoth, 1998, COLT' 98.

Bayesian Policy Search for Multi-Agent Role Discovery

Alan Fern, Prasad Tadepalli, Aaron Wilson, 2010, AAAI.

An Ensemble Learning and Problem Solving Architecture for Airspace Management

Santiago Ontañón, James A. Hendler, Thomas G. Dietterich, 2009, IAAI.

LEARNING TO SOLVE PROBLEMS FROM EXERCISES

Prasad Tadepalli, Prasad Tadepalli, P. Tadepalli, 2008, Comput. Intell..

Accelerating Imitation Learning in Relational Domains via Transfer by Initialization

Kristian Kersting, Sriraam Natarajan, Prasad Tadepalli, 2013, ILP.

Average-Reward Reinforcement Learning

Prasad Tadepalli, Prasad Tadepalli, 2017, Encyclopedia of Machine Learning and Data Mining.

DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs pdf

Alan Fern, Prasad Tadepalli, Stefan Lee, 2020, ICLR.

Multi-task reinforcement learning: a hierarchical Bayesian approach

Alan Fern, Prasad Tadepalli, Aaron Wilson, 2007, ICML '07.

Cross Lingual Mention and Entity Embeddings for Cross-Lingual Entity Disambiguation

Xiaoli Z. Fern, Chao Ma, Prasad Tadepalli, 2016, TAC.

Hierarchical Explanation-Based Reinforcement Learning

Thomas G. Dietterich, Prasad Tadepalli, Prasad Tadepalli, 1997, ICML.

Planning in games using approximately learned macros

Prasad Tadepalli, Prasad Tadepalli, 1989, ICML 1989.

An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

Santiago Ontañón, James A. Hendler, Thomas G. Dietterich, 2012, TIST.

Interactive Naming for Explaining Deep Neural Networks: A Formative Study pdf

Alan Fern, Prasad Tadepalli, Fuxin Li, 2018, IUI Workshops.

Reports of the AAAI 2019 Spring Symposium Series

Knut Hinkelmann, Keiki Takadama, Murat Kocaoglu, 2019, AI Mag..

Structured machine learning: the next ten years

Thomas G. Dietterich, Stephen Muggleton, Pedro M. Domingos, 2008, Machine Learning.

Factored MCTS for Large Scale Stochastic Planning

Alan Fern, Prasad Tadepalli, Roni Khardon, 2015, AAAI.

Transfer Learning via Relational Templates

Prasad Tadepalli, Scott Proper, 2009, ILP.

Multi-Agent Inverse Reinforcement Learning

Kristian Kersting, Sriraam Natarajan, Prasad Tadepalli, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Inverting Grice's Maxims to Learn Rules from Natural Language Extractions

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2011, NIPS.

Structured prediction via output space search

Alan Fern, Janardhan Rao Doppa, Prasad Tadepalli, 2014, J. Mach. Learn. Res..

Solving Relational MDPs with Exogenous Events and Additive Rewards pdf

Alan Fern, Prasad Tadepalli, Roni Khardon, 2013, ECML/PKDD.

Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function

Prasad Tadepalli, DoKyeong Ok, Prasad Tadepalli, 1996, ICML.

Optimizing the Predictive Value of Diagnostic Decision Rules

Sholom M. Weiss, Prasad Tadepalli, Robert S. Galen, 1987, AAAI.

Average-Reward Reinforcement Learning

Prasad Tadepalli, Prasad Tadepalli, 2010, Encyclopedia of Machine Learning.

Approximate Dynamic Programming

Geoffrey I. Webb, Stephen Grossberg, Mauro Birattari, 2011 .

Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference pdf

Xiaoli Z. Fern, Prasad Tadepalli, Reza Ghaeini, 2018, EMNLP.

Learning Rules from Incomplete Examples: A Pragmatic Approach

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2010, HLT-NAACL 2010.

Optimal Policies Tend to Seek Power.

Prasad Tadepalli, Rohin Shah, Andrew Critch, 2020 .

Learning with Incrutable Theories

Prasad Tadepalli, 1991, ML.

A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems

Sriraam Natarajan, Alan Fern, Prasad Tadepalli, 2007, Interaction Challenges for Intelligent Assistants.

Adaptive Control Processes

Sanjoy Dasgupta, Hannu Toivonen, Pieter Abbeel, 2010, Encyclopedia of Machine Learning and Data Mining.

Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies

Sriraam Natarajan, Alan Fern, Prasad Tadepalli, 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.

Guest editorial: special issue on structured prediction

Yasemin Altun, Prasad Tadepalli, Charles Parker, 2009, Machine Learning.

A Formal Framework for Speedup Learning from Problems and Solutions

Prasad Tadepalli, Balas K. Natarajan, B. Natarajan, 1996, J. Artif. Intell. Res..

Transfer in variable-reward hierarchical reinforcement learning

Sriraam Natarajan, Alan Fern, Prasad Tadepalli, 2008, Machine Learning.

Average-Reward Reinforcement Learning

Prasad Tadepalli, 2010 .

The first learning track of the international planning competition

Alan Fern, Prasad Tadepalli, Roni Khardon, 2011, Machine Learning.

Bayesian role discovery for multi-agent reinforcement learning

Alan Fern, Prasad Tadepalli, Aaron Wilson, 2010, AAMAS.

Learning for efficient retrieval of structured data with noisy queries

Alan Fern, Prasad Tadepalli, Charles Parker, 2007, ICML '07.

Avoiding Side Effects in Complex Environments pdf

Prasad Tadepalli, Alexander Matt Turner, Neale Ratzlaff, 2020, NeurIPS.

Model-Based Reinforcement Learning

Prasad Tadepalli, Soumya Ray, Prasad Tadepalli, 2010, Encyclopedia of Machine Learning and Data Mining.

Emergency Response Optimization using Online Hybrid Planning

Alan Fern, Prasad Tadepalli, Aswin Raghavan, 2018, IJCAI.

Guest editors’ introduction: special issue on inductive logic programming (ILP-2007)

Hendrik Blockeel, Prasad Tadepalli, Jude W. Shavlik, 2008, Machine Learning.

Incorporating Domain Models into Bayesian Optimization for RL

Alan Fern, Prasad Tadepalli, Aaron Wilson, 2010, ECML/PKDD.

Adaptive Submodularity with Varying Query Sets: An Application to Active Multi-label Learning

Alan Fern, Prasad Tadepalli, Robby Goetschalckx, 2017, ALT.

Active Learning with Committees for Text Categorization

Prasad Tadepalli, Ray Liere, Prasad Tadepalli, 1997, AAAI/IAAI.

Mean Absolute Error

Johannes Fürnkranz, Luís Torgo, Thomas Stützle, 2010, Encyclopedia of Machine Learning and Data Mining.

Tractable learning and planning in games

Tom M. Mitchell, Prasad Tadepalli, Tom Michael Mitchell, 1990 .

Reinforcement learning in scheduling

Thomas G. Dietterich, Prasad Tadepalli, Wei Zhang, 1994 .

A relational hierarchical model for decision-theoretic assistance

Sriraam Natarajan, Alan Fern, Prasad Tadepalli, 2011, Knowledge and Information Systems.

Quantifying prior determination knowledge using the PAC learning model

Sridhar Mahadevan, Prasad Tadepalli, S. Mahadevan, 2004, Machine Learning.

A Relational Hierarchical Model for Decision-Theoretic Assistance

Sriraam Natarajan, Alan Fern, Prasad Tadepalli, 2007, ILP.

Structured Attention Graphs for Understanding Deep Image Classifications pdf

Alan Fern, Prasad Tadepalli, Minsuk Kahng, 2020, ArXiv.

Voting-based integration algorithm improves causal network learning from interventional and observational data: an application to cell signaling network inference

Prasad Tadepalli, Stephen A. Ramsey, Meghamala Sinha, 2020 .

Attentional Multi-Reading Sarcasm Detection pdf

Xiaoli Z. Fern, Prasad Tadepalli, Reza Ghaeini, 2018, ArXiv.

Using trajectory data to improve bayesian optimization for reinforcement learning

Alan Fern, Prasad Tadepalli, Aaron Wilson, 2014, J. Mach. Learn. Res..

Theory-guided Empirical Speedup Learning of Goal Decomposition Rules

Prasad Tadepalli, Chandra Reddy, Silvana Roncagliolo, 1996, ICML.

Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem

Prasad Tadepalli, Prasad Tadepalli, 1989, IJCAI.

Learning in intractable domains

Prasad Tadepalli, 1986 .

Two New Frameworks for Learning

Prasad Tadepalli, Balas K. Natarajan, B. Natarajan, 1988, ML.

Logical Hierarchical Hidden Markov Models for Modeling User Activities

Weng-Keen Wong, Kristian Kersting, Sriraam Natarajan, 2008, ILP.

Training Deep Reactive Policies for Probabilistic Planning Problems

Alan Fern, Prasad Tadepalli, Murugeswari Issakkimuthu, 2018, ICAPS.

PAC Learning of Causal Trees with Latent Variables

Prasad Tadepalli, Stuart J. Russell, Prasad Tadepalli, 2021, AAAI.

Improving Multilingual Translation by Representation and Gradient Regularization

Alexandre Muzio, Prasad Tadepalli, Akiko Eriguchi, 2021, EMNLP.

User-Guided Global Explanations for Deep Image Recognition: A User Study

Li Fuxin, Prasad Tadepalli, Zhongang Qi, 2021 .

RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction

Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, 2021, ICAPS.

From Heatmaps to Structural Explanations of Image Classifiers pdf

Alan Fern, Li Fuxin, Prasad Tadepalli, 2021, Applied AI Letters.

Dynamic probabilistic logic models for effective abstractions in RL pdf

Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, 2021, ArXiv.

Machine Learning for the Knowledge Plane

Thomas G. Dietterich, Alan Fern, Prasad Tadepalli, 2006 .

Structure Refinement in First Order Conditional Influence Language

Weng-Keen Wong, Prasad Tadepalli, Sriraam Natarajan, 2006 .

Imitation Learning in Relational Domains Using Functional Gradient Boosting

Kristian Kersting, Prasad Tadepalli, Saket Joshi, .

Scaling Average-reward Reinforcement Learning for Product Delivery

Prasad Tadepalli, Scott Proper, Prasad Tadepalli, 2004 .

Efficient Learning of Action Models for Planning

Alan Fern, Prasad Tadepalli, Neville Mehta, 2011 .

Knowledge Intensive Learning : Directed vs Undirected SRL Models

Prasad Tadepalli, Gautam Kunapuli, Sriraam Natarajan, 2009 .

Learning Rules from Incomplete Examples via Observation Models

Thomas G. Dietterich, Jed Irvine, Xiaoli Z. Fern, 2011 .

A Review of the Fourth International Workshop on Machine Learning

Brian Falkenhainer, Prasad Tadepalli, Michael H. Sims, 2005, Machine Learning.