- 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.
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)
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)
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)