% Entry types used on the website (scripts/bib2json.py):
%   @journal      journal article           (journal = {...})
%   @conference   conference paper          (booktitle = {...})
%   @workshop     workshop paper / abstract (howpublished = {...})
%   @talk         talk, tutorial, lecture   (howpublished = {...})
%   @underreview  under review: NO venue, keeps it double-blind
% Optional link fields: url, doi, arxiv, pdf, code, project, slides, video, poster
% Tags for the publications filter: keywords = {RL, Neurosymbolic, ...}
% The standard types (@article, @inproceedings, @misc, @unpublished) still work too.

%%% UNDER REVIEW  (on acceptance: change to @conference/@journal and add the venue)
%%%%%%%%%%%%%%%

@underreview{panda2027b,
  keywords = {RL, Hierarchical RL},
  title   = {Critical Option-Critic via Distributional {RL}},
  author  = {Panda, Subrat Prasad and Prashant, Mohit and Easwaran, Arvind},
    year    = {2027}
}

@underreview{panda2027a,
  keywords = {Verification, Neurosymbolic, RL},
  title   = {Learning Abstractions for the Safety Verification of RL Policies},
  author  = {Panda, Subrat Prasad and Yuke, Liao and Genest, Blaise and Zikelic, Djordje and Easwaran, Arvind},
    year    = {2027}
}

@underreview{panda2027_workshop_a,
  keywords = {Neurosymbolic, RL},
  title   = {Neurosymbolic Learning of Interpretable Abstraction in Reinforcement Learning},
  author  = {Panda, Subrat Prasad and Genest, Blaise and Easwaran, Arvind},
    year    = {2027}
}


%%% CONFERENCES
%%%%%%%%%%%%%%%

@conference{panda2026a,
  keywords = {RL, Hierarchical RL, Neurosymbolic, Planning, Knowledge},
  arxiv   = {https://arxiv.org/abs/2608.02993},
  code    = {https://github.com/CPS-research-group/ink_bwts},
  title   = {Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning},
  author  = {Panda, Subrat Prasad and Genest, Blaise and Easwaran, Arvind},
  booktitle = {ECML-PKDD},
  year    = {2026}
}

@conference{dtsemnet,
  keywords = {Neurosymbolic, RL},
  arxiv = {https://arxiv.org/abs/2408.09135},
  code = {https://github.com/CPS-research-group/dtsemnet},
  project = {https://cps-research-group.github.io/dtsemnet/},
  title = {Vanilla Gradient Descent for Oblique Decision Trees},
  url = {http://dx.doi.org/10.3233/FAIA240607},
  DOI = {10.3233/faia240607},
  booktitle = {European Conference on Artificial Intelligence (ECAI)},
  publisher = {IOS Press},
  author = {Panda,  Subrat Prasad and Genest,  Blaise and Easwaran,  Arvind and Suganthan,  Ponnuthurai Nagaratnam},
  year = {2024},
  month = oct 
}

@conference{panda2021user,
  keywords = {RL, Edge AI},
  code={https://github.com/subratpp/mec_alloc_rl},
  title={User Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach},
  author={Panda, Subrat Prasad and Banerjee, Ansuman and Bhattacharya, Arani},
  booktitle={2021 IEEE International Conference on Web Services (ICWS)},
  pages={447--458},
  year={2021},
  organization={IEEE}
}

@conference{panda2020dynamic,
  keywords = {Edge AI},
  title={Dynamic Edge User Allocation with User Specified {QoS} Preferences},
  author={Panda, Subrat Prasad and Ray, Kaustabha and Banerjee, Ansuman},
  booktitle={International Conference on Service-Oriented Computing},
  pages={187--197},
  year={2020},
  organization={Springer}
}

@conference{panda2021service,
  keywords = {Edge AI},
  title={Service Allocation/Placement in Multi-Access Edge Computing with Workload Fluctuations},
  author={Panda, Subrat Prasad and Ray, Kaustabha and Banerjee, Ansuman},
  booktitle={International Conference on Service-Oriented Computing},
  pages={747--755},
  year={2021},
  organization={Springer}
}


%%%% JOURNALS
%%%%%%%%%%%%%%

@journal{panda2024methods,
  keywords = {RL},
  doi={10.1016/j.segan.2024.101334},
  title={Methods for Mitigating Uncertainty in Real-Time Operations of a Connected Microgrid},
  author={Panda, Subrat Prasad and Genest, Blaise and Easwaran, Arvind and Rigo-Mariani, R{\'e}my and Lin, Pengfeng},
  journal={Sustainable Energy, Grids and Networks},
  volume={38},
  pages={101334},
  year={2024},
  publisher={Elsevier}
}

@journal{panda2025jmlr,
  keywords = {Neurosymbolic, RL},
  url     = {http://jmlr.org/papers/v27/25-2047.html},
  code    = {https://github.com/CPS-research-group/dtsemnet},
  title   = {Approximation-Free Differentiable Oblique Decision Trees},
  author  = {Panda, Subrat Prasad and Genest, Blaise and Easwaran, Arvind},
  journal = {Journal of Machine Learning Research (JMLR)},
  volume  = {27},
  year    = {2026}
}




%%% WORKSHOPS
%%%%%%%%%%%%%%%
@workshop{panda2025programmatic,
  keywords = {RL, Neurosymbolic},
  url          = {https://openreview.net/forum?id=S27okPWTtk},
  title        = {Programmatic Reinforcement Learning for Trustworthy Microgrid Management},
  author       = {Panda, Subrat Prasad and Genest, Blaise and Easwaran, Arvind},
  year         = {2025},
  howpublished = {AI4X International Conference},
  note         = {}
}

%%% TALKS
%%%%%%%%%%%%%%%
@talk{panda2026_talk_rennes,
  title        = {Neurosymbolic Techniques for Safe Reinforcement Learning},
  author       = {Panda, Subrat Prasad},
  howpublished = {Invited talk, 68NQRT Seminar, ICUR, Rennes, France, Sep.},
  year         = {2026}
}

@talk{panda2026_dl_bootcamp,
  slides       = {https://github.com/ntu-dl-bootcamp/deep-learning-2026/tree/main/SESSION5},
  title        = {Reinforcement Learning},
  author       = {Panda, Subrat Prasad},
  howpublished = {Tutorial lecture with hands-on notebooks, \href{https://github.com/ntu-dl-bootcamp/deep-learning-2026/tree/main/SESSION5}{Deep Learning Bootcamp 2026}, NTU Singapore},
  year         = {2026}
}