Publications

  • Dynamic probabilistic logic models for effective abstractions in RL,
    Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, In Statistical Relational AI (StarAI) Workshop at IJCLR 2021.
    paper
  • Human-guided Collaborative Problem Solving: A Natural Language based Framework,
    Harsha Kokel, Mayukh Das, Rakibul Islam, Julia Bonn, Jon Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth, In ICAPS (demo track) 2021.
    paper | blog | video | cite
    @article{KokeletalDemo,
       title={Human-guided Collaborative Problem Solving: A Natural Language based Framework},
       author={Harsha Kokel, Mayukh Das, Rakibul Islam, Julia Bonn, Jon Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth},
       journal={Thirty First International Conference on Automated Planning and Scheduling ({ICAPS}) Demo Track},
       year={2021},
       }

  • RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction (Extended Abstract),* contributed talk (11/25 accepted paper)
    Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, In Planning and RL (PRL) Workshop at ICAPS 2021.
    paper | poster | video
  • A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes,
    Athresh Karanam, Alexander L. Hayes, Harsha Kokel, David M. Haas, Predrag Radivojac, Sriraam Natarajan, In AIME 2021.
    paper | blog | DOI| video | cite
    @inproceedings{KaranamHKHRN21,
       author = {Athresh Karanam and Alexander L. Hayes and Harsha Kokel and David M. Haas and Predrag Radivojac and Sriraam Natarajan},
       title = {A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes},
       year = {2021},
       publisher = {Springer International Publishing},
       pages = {497--502},
       booktitle = {Artificial Intelligence in Medicine},
       isbn = {978-3-030-77211-6},
       DOI = {10.1007/978-3-030-77211-6_59},
       }

  • RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction,
    Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, In ICAPS 2021.
    paper | blog | code | supplemental | DOI | cite
    @article{KokelMNRT21,
       title={RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction},
       author={Kokel, Harsha and Manoharan, Arjun and Natarajan, Sriraam and Balaraman, Ravindran and Tadepalli, Prasad},
       journal={Proceedings of the International Conference on Automated Planning and Scheduling},
       number={1},
       volume={31},
       year={2021},
       month={May},
       pages={533--541},
       url={https://ojs.aaai.org/index.php/ICAPS/article/view/16001},
       }

  • Graph Sparsification via Meta-Learning,* contributed talk (4/22 accepted paper)
    Guihong Wan, Harsha Kokel, In Deep Learning for Graphs (DLG) Workshop at AAAI 2021.
    paper | slide | video | cite
    @article{WanK21,
       title={Graph Sparsification via Meta-Learning},
       author={Wan, Guihong and Kokel, Harsha},
       journal={Deep Learning for Graphs (DLG) Workshop at AAAI},
       year={2021}
       }

  • A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domain,
    Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan, In AAAI 2020.
    paper | blog | code | supplemental | poster | slide | DOI | cite
    @article{KokelOYN20,
       author = {Harsha Kokel and Phillip Odom and Shuo Yang and Sriraam Natarajan},
       title = {A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains},
       journal = {Proceedings of the AAAI Conference on Artificial Intelligence}
       volume = {34},
       number = {04},
       pages = {4460--4468},
       year = {2020},
       month = {Apr.},
       DOI = {10.1609/aaai.v34i04.5873},
       url = {https://aaai.org/ojs/index.php/AAAI/article/view/5873},
       }