2021 Winter

Date: 2021-04-09 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 843 0865 5572 Passcode: 690084 Abstract: Multivariate claim data are common in insurance applications, e.g. claims of each policyholder for different types of insurance coverages. Understanding the dependencies among such multivariate risks is essential for the solvency and profitability of insurers. Effectively modeling insurance claim data is challenging due to their special complexities. At the policyholder level, claims data usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution.

Date: 2021-03-26 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 843 0865 5572 Passcode: 690084 Abstract: There has been a recent surge of interest in the machine learning community in developing causal models that handle the effect of interventions in a system. In this talk, I will consider the problem of learning (estimating) a causal graphical model from data. The search over possible directed acyclic graphs modeling the causal structure is inherently combinatorial, but I’ll describe our recent work which use gradient-based continuous optimization for learning both the parameters of the distribution and the causal graph jointly, and can be combined naturally with flexible parametric families that use neural networks.

Date: 2021-03-19 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 843 0865 5572 Passcode: 690084 Abstract: Foreign public issuers (FPIs) are required by the Securities and Exchanges Commission (SEC) to file Form 20-F as comprehensive annual reports. In an effort to increase the usefulness of 20-Fs, the SEC recently enacted a regulation to accelerate the deadline of 20-F filing from six months to four months after the fiscal year-end. The rationale is that the shortened reporting lag would improve the informational relevance of 20-Fs.

Date: 2021-03-12 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 939 8331 3215 Passcode: 096952 Abstract: Informative selection, in which the distribution of response variables given that they are sampled is different from their distribution in the population, is pervasive in complex surveys. Failing to take such informativeness into account can produce severe inferential errors, including biased and inconsistent estimation of population parameters. While several parametric procedures exist to test for informative selection, these methods are limited in scope and their parametric assumptions are difficult to assess.

Date: 2021-02-26 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 843 0865 5572 Passcode: 690084 Abstract: We consider point estimation and generation of confidence intervals under the constraint of differential privacy. We provide a simple and practical framework for these tasks in relatively general settings. Our investigation addresses a novel challenge that arises in the differentially private setting, which involves the cost of weak a priori bounds on the parameters of interest.

Date: 2021-02-19 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 843 0865 5572 Passcode: 690084 Abstract: We consider the joint estimation of regression parameters from multiple potentially heterogeneous data sources with correlated vector outcomes. The primary goal of this joint integrative analysis is to estimate covariate effects on all vector outcomes through a marginal regression model in a statistically and computationally efficient way. We present a general class of distributed estimators that can be implemented in a parallelized computational scheme.

Date: 2021-02-12 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 939 8331 3215 Passcode: 096952 Abstract: Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and for understanding the role TCs play in the global climate system, yet that characterization is complicated by the high-noise ocean environment, correlations inherent in spatio-temporal data, relative scarcity of in situ observations and the entanglement of the TC-induced signal with seasonal signals.

Date: 2021-02-05 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 843 0865 5572 Passcode: 690084 Abstract: This work studies an important sequential decision making problem known as the multi-armed bandit problem with covariates. Under a linear bandit framework with high-dimensional covariates, we propose a general arm allocation algorithm that integrates both arm elimination and randomized assignment strategies. By employing a class of high-dimensional regression methods for coefficient estimation, the proposed algorithm is shown to have near optimal finite-time regret performance under a new study scope that requires neither a margin condition nor a reward gap condition for competitive arms.

Date: 2021-01-29 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 939 8331 3215 Passcode: 096952 Abstract: The under-five mortality rate (U5MR) is a key barometer of the health of a nation. Unfortunately, many people living in low- and middle-income countries are not covered by civil registration systems. This makes estimation of the U5MR, particularly at the subnational level, difficult. In this talk, I will describe models that have been developed to produce the official United Nations (UN) subnational U5MR estimates in 22 countries.

Date: 2021-01-15 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 843 0865 5572 Passcode: 690084 Abstract: During the last decade, hundreds of machine learning methods have been developed for disease outcome prediction based on high-throughput genomics data. However, the quality of the input genomics features and the output clinical variables has been ignored in these algorithms. In this talk, I will introduce two studies that develop methods to learn more accurate molecular signatures and drug response values for cancer research.