Ojash Neopane

I am fourth year PhD student in the Machine Learning Department at Carnegie Mellon University where I am fortunate to be advised by Aaditya Ramdas and Aarti Singh.

My research interests are broadly in the areas of adaptive learning and sequential decision making. More specifically, I am interested in theoretical problems pertaining to Multi-Armed Bandits and Reinforcement Learning. Currently, my focuses are in problems related to regret minimization and pure exploration in Multi-Armed Bandits.

Email  /  Google Scholar

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Papers:

  1. Best Arm Identification Under Additive Transfer Bandits
    Ojash Neopane, Aaditya Ramdas, Aarti Singh
    Best Student Paper Award Asilomar 2021
    [Paper] (Slides/Talk to be uploaded soon)


  2. A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
    Xinyu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado
    arXiv:1705.02583
    [Paper]


  3. A Nonparametric Framework for Quantifying Generative Inference on Neuromorphic Systems
    Ojash Neopane, Srinjoy Das, Ery Arias-Castro, Ken Kreutz-Delgado
    International Symposium on Circuits and Systems, 2016
    [Paper]