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Computer Science

D-Index
30
Citations
4433
World Ranking
14020
National Ranking
5570

Overview

Prasad Tadepalli is affiliated with Oregon State University in the United States and specializes in the field of Computer Science, with a significant focus on Artificial Intelligence.

The scientist's work encompasses multiple subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Computational Theory and Mathematics, and Biophysics.

The main research topics covered in Tadepalli's publications are:

  • Explainable Artificial Intelligence (XAI)
  • Reinforcement Learning in Robotics
  • Natural Language Processing Techniques
  • Bayesian Modeling and Causal Inference
  • Topic Modeling
  • Artificial Intelligence in Games
  • Cell Image Analysis Techniques

Recent papers authored or coauthored by Prasad Tadepalli address diverse areas within AI and machine learning. Selected recent works include:

  • "Planning in Factored Action Spaces with Symbolic Dynamic Programming," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Improving Multilingual Translation by Representation and Gradient Regularization," 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction," 2021, Proceedings of the International Conference on Automated Planning and Scheduling
  • "One Explanation is Not Enough: Structured Attention Graphs for Image Classification," 2020, arXiv (Cornell University)
  • "Parametrically Retargetable Decision-Makers Tend To Seek Power," 2022, arXiv (Cornell University)

Tadepalli frequently publishes in several venues, with a notable concentration in arXiv (Cornell University) and the Proceedings of the AAAI Conference on Artificial Intelligence. Other publication venues include Applied AI Letters, the Conference on Empirical Methods in Natural Language Processing, and the International Conference on Automated Planning and Scheduling.

Frequent collaborators include the following coauthors:

  • Alan Fern
  • Sriraam Natarajan
  • Stefan Lee
  • Harsha Kokel
  • Fuxin Li

Best Publications

  • Multi-task reinforcement learning: a hierarchical Bayesian approach

    Aaron Wilson;Alan Fern;Soumya Ray;Prasad Tadepalli

  • Active Learning with Committees for Text Categorization

    Ray Liere;Prasad Tadepalli

  • Dynamic preferences in multi-criteria reinforcement learning

    Sriraam Natarajan;Prasad Tadepalli

  • Relational Reinforcement Learning: An Overview

    Prasad Tadepalli;Robert Givan;Kurt Driessens

  • Structured machine learning: the next ten years

    Thomas G. Dietterich;Pedro Domingos;Lise Getoor;Stephen Muggleton

  • A Bayesian Approach for Policy Learning from Trajectory Preference Queries

    Aaron Wilson;Alan Fern;Prasad Tadepalli

  • Transfer in variable-reward hierarchical reinforcement learning

    Neville Mehta;Sriraam Natarajan;Prasad Tadepalli;Alan Fern

  • Model-based average reward reinforcement learning

    Prasad Tadepalli;DoKyeong Ok

  • Lower bounding Klondike Solitaire with Monte-Carlo planning

    Ronald Bjarnason;Alan Fern;Prasad Tadepalli

  • A decision-theoretic model of assistance

    Alan Fern;Sriraam Natarajan;Kshitij Judah;Prasad Tadepalli

  • Automatic discovery and transfer of MAXQ hierarchies

    Neville Mehta;Soumya Ray;Prasad Tadepalli;Thomas Dietterich

  • Multi-Agent Inverse Reinforcement Learning

    Sriraam Natarajan;Gautam Kunapuli;Kshitij Judah;Prasad Tadepalli

  • Maximizing the predictive value of production rules

    S. M. Weiss;R. S. Galen;P. V. Tadepalli

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

    Reza Ghaeini;Xiaoli Z. Fern;Prasad Tadepalli

  • Learning first-order probabilistic models with combining rules

    Sriraam Natarajan;Prasad Tadepalli;Thomas G. Dietterich;Alan Fern

  • Using trajectory data to improve bayesian optimization for reinforcement learning

    Aaron Wilson;Alan Fern;Prasad Tadepalli

  • Lazy explanation-based learning: a solution to the intractable theory problem

    Prasad Tadepalli

  • A decision-theoretic model of assistance

    Alan Fern;Sriraam Natarajan;Kshitij Judah;Prasad Tadepalli

  • Imitation learning in relational domains: a functional-gradient boosting approach

    Sriraam Natarajan;Saket Joshi;Prasad Tadepalli;Kristian Kersting

  • Learning Goal-Decomposition Rules using Exercises

    Chandra Reddy;Prasad Tadepalli

  • Learning goal-decomposition rules using exercises

    Chandra Reddy;Prasad Tadepalli

  • Learning first-order probabilistic models with combining rules

    Sriraam Natarajan;Prasad Tadepalli;Eric Altendorf;Thomas G. Dietterich

Frequent Co-Authors

Alan Fern
Alan Fern Oregon State University
Thomas G. Dietterich
Thomas G. Dietterich Oregon State University
Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison
Stefan Lee
Stefan Lee Oregon State University
Kristian Kersting
Kristian Kersting Technical University of Darmstadt
Weng-Keen Wong
Weng-Keen Wong Oregon State University
Sridhar Mahadevan
Sridhar Mahadevan University of Massachusetts Amherst
Stuart Russell
Stuart Russell University of California, Berkeley
Subbarao Kambhampati
Subbarao Kambhampati Arizona State University
Deborah L. McGuinness
Deborah L. McGuinness Rensselaer Polytechnic Institute

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