P. Tadepalli

发表

Learning Goal-Decomposition Rules Using Exercises

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

On Exact Learning of Unordered Tree Patterns

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

Exact learning of tree patterns from queries and counterexamples

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

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

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

Logical Hierarchical Hidden Markov Models for Modeling User Activities

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

Market Basket Analysis

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

Optimal Policies Tend To Seek Power

Rohin Shah, Prasad Tadepalli, Andrew Critch, 2019, NeurIPS.

Two New Frameworks for Learning

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

Solving multiagent assignment Markov decision processes

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

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

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

Improving Multilingual Translation by Representation and Gradient Regularization

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

Structured machine learning: the next ten years

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

Parametrically Retargetable Decision-Makers Tend To Seek Power

Prasad Tadepalli, A. M. Turner, P. Tadepalli, 2022, NeurIPS.

An Apprentice-Based Approach to Knowledge Acquisition

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

Dynamic preferences in multi-criteria reinforcement learning

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

Dependent Gated Reading for Cloze-Style Question Answering

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

A Decision-Theoretic Model of Assistance

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

Using trajectory data to improve bayesian optimization for reinforcement learning

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

Bayesian Policy Search for Multi-Agent Role Discovery

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

Multi-task reinforcement learning: a hierarchical Bayesian approach

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

Model-Based Reinforcement Learning

R. Sutton, Soumya Ray, Leonid Kuvayev, 2010, Encyclopedia of Machine Learning.

Planning in Factored Action Spaces with Symbolic Dynamic Programming

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

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

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

Model-Based Average Reward Reinforcement Learning

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

Event Nugget Detection with Forward-Backward Recurrent Neural Networks pdf

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

Prune-and-Score: Learning for Greedy Coreference Resolution

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

Learning from Examples and Membership Queries with Structured Determinations

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

Quantifying Prior Determination Knowledge Using the PAC Learning Model

S. Mahadevan, Prasad Tadepalli, P. Tadepalli, 1994, Machine Learning.

Quantifying prior determination knowledge using the PAC learning model

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

Learning from Queries and Examples with Tree-structured Bias

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

Mixture Modeling

William T. B. Uther, Y. Shoham, J. Rissanen, 2019, Encyclopedia of Machine Learning.

Multi-Agent Inverse Reinforcement Learning

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

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

Prasad Tadepalli, DoKyeong Ok, Prasad Tadepalli, 1994 .

Learning and Transferring Roles in Multi-Agent Reinforcement

Alan Fern, Prasad Tadepalli, A. Wilson, 2008 .

Learning and transferring roles in multi-agent MDPs

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

Chance-Constrained Programs for Link Prediction

Prasad Tadepalli, Jun Yu, P. Tadepalli, 2009 .

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

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

Learning Algorithms for Link Prediction Based on Chance Constraints

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

Adaptive Control Processes

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

Emergency Response Optimization using Online Hybrid Planning

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

Auto-Exploratory Average Reward Reinforcement Learning

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

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

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

Event Detection with Neural Networks: A Rigorous Empirical Evaluation

Xiaoli Z. Fern, J. W. Orr, P. Tadepalli, 2018, EMNLP.

Hierarchical structure discovery and transfer in sequential decision problems

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

Maximizing the Predictive Value of Production Rules

Sholom M. Weiss, Robert S. Galen, Prasad V. Tadepalli, 1990, Artif. Intell..

Maximizing the Predictive Value of Production Rules 1

S. Weiss, R. Galen, P. Tadepalli, 2022 .

A Bayesian Approach for Policy Learning from Trajectory Preference Queries

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

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

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

Towards learning rules from natural texts

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

Learning Rules from Incomplete Examples: A Pragmatic Approach

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

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

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

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

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

Output Space Search for Structured Prediction

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

Structured prediction via output space search

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

RePReL: a unified framework for integrating relational planning and reinforcement learning for effective abstraction in discrete and continuous domains

Balaraman Ravindran, Sriraam Natarajan, Harsha Kokel, 2022, Neural Computing and Applications.

Simulation-based Optimization of Resource Placement and Emergency Response

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

Transfer in variable-reward hierarchical reinforcement learning

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

LEARNING TO SOLVE PROBLEMS FROM EXERCISES

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

Automatic discovery and transfer of MAXQ hierarchies

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

Automatic Induction of MAXQ Hierarchies

Prasad Tadepalli, Soumya Ray, N. Mehta, 2007 .

Multi-Agent Shared Hierarchy Reinforcement Learning

Prasad Tadepalli, N. Mehta, P. Tadepalli, 2005 .

RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction

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

Active Imitation Learning of Hierarchical Policies

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

Dynamic probabilistic logic models for effective abstractions in RL pdf

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

Learning first-order probabilistic models with combining rules

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

Exact learning of unordered tree patterns from queries

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

Training Deep Reactive Policies for Probabilistic Planning Problems

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

Relational Reinforcement Learning: An Overview

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

Conservative Agency

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

Conservative Agency via Attainable Utility Preservation

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

An Ensemble Learning and Problem Solving Architecture for Airspace Management

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

Lower Bounding Klondike Solitaire with Monte-Carlo Planning

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

Avoiding Side Effects in Complex Environments pdf

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

One Explanation is Not Enough: Structured Attention Graphs for Image Classification

Alan Fern, Minsuk Kahng, L. Fuxin, 2020, NeurIPS.

Hindsight Optimization for Hybrid State and Action MDPs

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

Description-Based Zero-shot Fine-Grained Entity Typing

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