Semih Çaycı
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I am an NSF TRIPODS Postdoctoral Fellow at the University of Illinois at Urbana-Champaign. I am affiliated with Illinois Institute of Data Science and Dynamical Systems. My research interests lie in theoretical machine learning, optimization and applied probability, with a focus on:
  • theoretical foundations of reinforcement learning,
  • deep learning theory.

My curriculum vitae can be found here.

Selected Papers
  • Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic [paper]
    Semih Cayci, Niao He, R. Srikant
    To be presented in International Conference on Continuous Optimization 2022
  • Sample Complexity and Overparameterization Bounds for TD Learning with Neural Network Approximation [paper]
    Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant
    ICML Workshop on Overparameterization 2021
  • Convergence of Entropy-Regularized Natural Policy Gradient Methods with Linear Function Approximation [paper]
    Semih Cayci, Niao He, R. Srikant
    ICML Workshop on Reinforcement Learning Theory 2021
  • A Lyapunov-Based Methodology for Constrained Optimization with Bandit Feedback
    Semih Cayci, Yilin Zheng, Atilla Eryilmaz
    AAAI 2022
    [paper]
  • Group-Fair Online Allocation in Continuous Time
    Semih Cayci, Swati Gupta, Atilla Eryilmaz
    NeurIPS 2020
    [paper]
  • Budget-Constrained Bandits over General Cost and Reward Distributions
    Semih Cayci, Atilla Eryilmaz, R. Srikant
    AISTATS 2020
    [paper]
  • Learning to Control Renewal Processes with Bandit Feedback
    Semih Cayci, Atilla Eryilmaz, R. Srikant
    ACM SIGMETRICS 2019

    [paper]
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