Keynote Speakers

Abstract: 

Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse applications in casual discovery. We study the problem of learning the sparse DAG structure of a BN from continuous observational data. The central problem can be modeled as a mixed-integer program with an objective function composed of a convex quadratic loss function and a regularization penalty subject to linear constraints. The optimal solution to this mathematical program is known to have desirable statistical properties under certain conditions.  However, the state-of-the-art optimization solvers are not able to obtain provably optimal solutions to the existing mathematical formulations for medium-size problems within reasonable computational times. To address this difficulty, we tackle the problem from both computational and statistical perspectives. On the one hand, we propose a concrete early stopping criterion to terminate the branch-and-bound process in order to obtain a near-optimal solution to the mixed-integer program, and establish the consistency of this approximate solution. On the other hand, we improve the existing formulations by replacing the linear big-M constraints that represent the relationship between the continuous and binary indicator variables with second-order conic constraints.  Our numerical results demonstrate the effectiveness of the proposed approaches. This is joint work with Tong Xu, Armeen Taeb, Ali Shojaie.

Bio: 

Simge Küçükyavuz is Chair and David A. and Karen Richards Sachs Professor in the Industrial Engineering and Management Sciences Department at Northwestern University. She is an expert in mixed-integer, large-scale, and stochastic optimization. Her methodologies have applications in complex computational problems across numerous domains, including social networks, computing and energy infrastructure, statistical learning, and logistics. Her research has been supported by multiple grants from the National Science Foundation (NSF) and the Office of Naval Research (ONR). She is an INFORMS Fellow, and the recipient of the NSF CAREER Award and the INFORMS Computing Society (ICS) Prize. She is the past chair of ICS and serves on the editorial boards of Mathematics of Operations Research, Mathematical Programming, Operations Research, SIAM Journal on Optimization, and MOS-SIAM Optimization Book Series. She received her Ph.D. in Industrial Engineering and Operations Research from the University of California, Berkeley.

Abstract:

Respondent-driven sampling (RDS) is a network-based sampling strategy used to study hidden populations for which no sampling frame is available. In each epoch of an RDS study, the current wave of study participants are incentivized to recruit the next wave through their social connections. The success and efficiency of RDS can depend critically on attributes of incentives and the underlying (latent) network structure. We propose a reinforcement learning-based adaptive RDS design to optimize some measure of study utility, e.g., efficiency, treatment dissemination, reach, etc. Our design is based on a branching process approximation to the RDS process, however, our proposed post-study inferential procedures apply to general network models even when the network is not fully identified. Simulation experiments show that the proposed design provides substantial gains in efficiency over static and two-step RDS

procedures.

Bio:

Laber is the James B. Duke Distinguished Professor of Statistical Sciences at Duke University.  His work focuses on the development of methodologies for statistical reinforcement learning with applications in precision medicine, public health, e-sports, and retail.  He is also passionate about K-12 STEM outreach.  More information about his lab is available at: https://laber-labs.com.