"Convergence diagnostics for stochastic gradient descent with constant step size"
(with Jerry Chee, arxiv pdf)
- "Exact tests for two-stage randomized designs in the presence of interference"
(with Guillaume Basse, Avi Feller; submitted, arxiv pdf)
"Propensity score methodology in the presence of network entanglement between treatments"
(with Alexander Volfovsky, Edoardo M. Airoldi)
"Theory and practice of screening competitions"
(with David C. Parkes)
"Implicit stochastic approximation"
(with Thibaut Horel, Edoardo M. Airoldi; arxiv pdf)
"Stochastic gradient methods for estimation with large datasets"
(with Dustin Tran, Edoardo M. Airoldi; arxiv pdf, in revision)
"Causal inference under partially revealed interference"
(with Edoardo M. Airoldi, Donald B. Rubin)
- PT, Edoardo M. Airoldi, "Asymptotic and finite-sample properties of estimators based on stochastic gradients", Annals of Statistics, Volume 45, Number 4 (2017), 1694-1727. ( pdf | | bib) )
- PT, "A useful pivotal quantity", American Statistician, 2016 ( www | pdf | bib)
- PT, Edoardo M. Airoldi, "Scalable estimation strategies based on stochastic approximations: Classical results and new insights", Statistics and Computing, 2015 ( www | pdf | bib).
- PT, David Parkes, "Design and analysis of multi-hospital kidney-exchanges using random graphs", Games and Economic Behavior, 2015 ( pdf | www | bib)
- PT, David Parkes, "Long-term causal effects via behavioral game theory", Neural Information Processing Systems, 2016, Barcelona, Spain, (NIPS'16, www | bib)
- PT, Dustin Tran, Edoardo M. Airoldi, "Towards stability and optimality in stochastic gradient descent" , AI and Statistics, 2016, Cadiz, Spain (AISTATS' 16, pdf | bib)
- PT, David Parkes, Elery Pfeffer, James Zou, "Incentive-compatible experimental design", Economics and Computation, 2015, Portland, Oregon (EC'15, pdf | bib)
- PT, Jason Rennie, Edoardo Airoldi, "Statistical analysis of stochastic gradient methods for generalized linear models", International Conference of Machine Learning, 2014, Beijing, China (ICML' 14, www, pdf | bib)
- PT, Edward Kao, "Estimation of Causal Peer Influence Effects", International Conference of Machine Learning, 2013, Atlanta, Georgia (ICML'13, www, pdf | bib)
- PT, David Parkes, "A Random Graph Model of Kidney Exchanges: Efficiency, Individual-Rationality and Incentives", Economics and Computation, 2011, San Jose, California (EC'11, pdf | bib)
- Nikolaos Mavridis, Wajahat Kazmi, PT, C. Ben-AbdelKader, "On the synergies between online social networking, Face Recognition, and Interactive Robotics", International Conference on Computational Aspects of Social Networks, 2009, Fontainebleau, France (CASoN'09, pdf | bib)
- PT, Dionisis Kehagias, Pericles Mitkas, "Mertacor, a successful trading agent", Autonomous Agents and Multi-Agent Systems, 2006, Hakodate, Japan (AAMAS'06, pdf | bib)
- Dionisis Kehagias, PT, Pericles Mitkas, "A Long-Term Profit Seeking Strategy for Continuous Double Auctions in a Trading Agent Competition", Fourth Hellenic Conference on Artificial Intelligence, 2006, Heraklion, Crete ( www | pdf | bib)
- PT, Edoardo Airoldi, "Stochastic gradient methods for principled estimation with large datasets", in "Handbook of Big Data", CRC Press, 2016 (eds. Buhlmann et. al.)-( www | pdf )
- Nikolaos Mavridis, Wajahat Kazmi, PT, "Friends with Faces: How Social Networks Can Enhance Face Recognition and Vice Versa", Computational Social Network Analysis (eds. A. Ajith & H. Aboul-Ella & S. Vaclav), pp. 453-482, Springer London
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.
- Matt Bonakdarpour, PT, "Statistical perspectives of stochastic optimization" (NIPS'16, Probabilistic Numerics Workshop, pdf)
- Implicit temporal differences ( pdf) - Using ideas from implicit stochastic gradient descent to improve the stability of the Temporal Differences algorithm (TD) in reinforcement learning. Neural Information and Processing Systems (NIPS 2014, Montreal, Canada).
- Software Engineering With R, 2013 ( pdf) - Intro to software engineering practices with R: unit testing, debugging, logging, profiling.