Prof. Panagiotis (Panos) Toulis
Panagiotis (Panos) Toulis

Panagiotis (Panos) Toulis
Assistant Professor of Econometrics and Statistics
Address: 5807 S Woodlawn Ave
Office: 358
tel: 773.834.5953


Working papers

  • Theory and practice of screening competitions, with Parkes, DC.
  • Exact tests for two-stage randomized designs in the presence of interference, with Basse, G., Feller, A.
    (  slides,   arxiv pdf, R&R)
  • Propensity score methodology in the presence of network entanglement between treatments
    with Volfovsky, A., Airoldi, EM. (  pdf, submitted)
  • Subclassification similarity of propensity score models, with Volfovsky, A.
  • Causal inference under partially revealed interference, with Airoldi, EM., Rubin, DB.
  • Implicit stochastic approximation, with Horel, T., Airoldi, EM.
    (  arxiv pdf)
  • Stochastic gradient methods for estimation with large datasets, with Tran, D., Airoldi, EM.
    (  arxiv pdf, submitted)
  • Exact inference with stochastic gradient methods, with Chee, J.

Journal papers

  • Asymptotic and finite-sample properties of estimators based on stochastic gradients, with Airoldi, EM.
    Annals of Statistics, Volume 45, Number 4 (2017), 1694-1727 (   pdf | |    bib) )
  • A useful pivotal quantity
    American Statistician, 2016 (   www |   pdf |    bib)
  • Scalable estimation strategies based on stochastic approximations, with Airoldi, EM.
    Statistics and Computing, 2015 (  www |   pdf |   bib).
  • Design and analysis of multi-hospital kidney-exchanges using random graphs, with Parkes, DC.
    Games and Economic Behavior, 2015 (  pdf |   www |   bib)

Conference papers

  • Convergence diagnostics for stochastic gradient descent with constant step size, with Chee, J.
    AI and Statistics, 2018 (AISTATS'18,   arxiv pdf)
  • Long-term causal effects via behavioral game theory, with Parkes, DC.
    Neural Information Processing Systems, 2016, Barcelona, Spain (NIPS'16,   www |   bib)
  • Towards stability and optimality in stochastic gradient descent, with Tran, D., Airoldi, EM.
    AI and Statistics, 2016, Cadiz, Spain (AISTATS' 16,   pdf |   bib)
  • Incentive-compatible experimental design, with Parkes, DC., Pfeffer, E., Zhou, J.
    Economics and Computation, 2015, Portland, Oregon (EC'15,    pdf |   bib)
  • Statistical analysis of stochastic gradient methods for generalized linear models, with Rennie, J., Airoldi, EM.
    International Conference of Machine Learning, 2014, Beijing, China (ICML' 14,   www,   pdf |   bib)
  • Estimation of Causal Peer Influence Effects, with Kao, E.
    International Conference of Machine Learning, 2013, Atlanta, Georgia (ICML'13,   www,   pdf |   bib)
  • A Random Graph Model of Kidney Exchanges: Efficiency, Individual-Rationality and Incentives, with Parkes.
    Economics and Computation, 2011, San Jose, California (EC'11,   pdf |   bib)
  • On the synergies between online social networking, Face Recognition, and Interactive Robotics
    with Mavridis, N., Kazmi, W., Ben-AbdelKader, C.
    International Conference on Computational Aspects of Social Networks, 2009, Fontainebleau, France (CASoN'09,   pdf |   bib)
  • Mertacor, a successful trading agent, with Kehagias, D., Mitkas, P.
    Autonomous Agents and Multi-Agent Systems, 2006, Hakodate, Japan (AAMAS'06,   pdf |   bib)
  • A Long-Term Profit Seeking Strategy for Continuous Double Auctions in a Trading Agent Competition
    with Kehagias, D., Mitkas, P.
    Fourth Hellenic Conference on Artificial Intelligence, 2006, Heraklion, Crete (  www |   pdf |   bib)

Book chapters

  • Stochastic gradient methods for principled estimation with large datasets, with Airoldi, EM.
    Handbook of Big Data, CRC Press, 2016, eds. Buhlmann et. al. (  www |   pdf )
  • Friends with Faces: How Social Networks Can Enhance Face Recognition and Vice Versa
    with Mavridis, N., Kazmi, W.
    Computational Social Network Analysis, Springer London, eds. A. Ajith et. al. (  www)

Short papers, Workshops, and Tutorials

  • Introduction to Stochastic Gradient Descent
    This is a short introduction to Stochastic Gradient Descent, trying to cover both the optimization and statistical perspective. It covers classical literature in stochastic approximation, as well as recent developments.
  • Statistical perspectives of stochastic optimization, with Bonakdarpour, M.
    Probabilistic Numerics Workshop (NIPS'16,   pdf)
  • Implicit temporal differences with Tamar, A., Mannor, S., Airoldi, EM.
    Reinforcement Learning (NIPS'14, Montreal, Canada,   pdf)
  • Software Engineering With R, 2013
    Intro to software engineering practices with R: unit testing, debugging, logging, profiling. (  pdf)
Booth Homepage | Booth Intranet | UC Homepage Copyright © 2016 Chicago Booth