2022 fall

Date: 2022-11-18 Time: 15:30-16:30 (Montreal time) https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: For nonparametric regression in the streaming setting, where data constantly flow in and require real-time analysis, a main challenge is that data are cleared from the computer system once processed due to limited computer memory and storage. We tackle the challenge by proposing a novel one-pass estimator based on penalized orthogonal basis expansions and developing a general framework to study the interplay between statistical efficiency and memory consumption of estimators.

Date: 2022-11-11 Time: 15:30-16:30 (Montreal time) https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: Many causal parameters involving the joint distribution of potential outcomes in treated and control states cannot be point-identified, but only be bounded from above and below. The bounds can be further tightened by conditioning on pre-treatment covariates, and the sharp version of the bounds corresponds to using a full covariate vector. This paper gives a method for estimation and inference on sharp bounds determined by a linear system of under-identified equalities (e.

Date: 2022-11-04 Time: 15:30-16:30 (Montreal time) https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: Graphical models can represent multivariate distributions in an intuitive way and, hence, facilitate statistical analysis of high-dimensional data. Such models are usually modular so that high-dimensional distributions can be described and handled by careful combination of lower dimensional factors. Furthermore, graphs are natural data structures for algorithmic treatment. Moreover, graphical models can allow for causal interpretation, often provided through a recursive system on a directed acyclic graph (DAG) and the max-linear Bayesian network we introduced in [1] is a specific example.

Date: 2022-10-21 Time: 15:30-16:30 (Montreal time) https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: We consider the problem of testing the equality of the conditional distribution of a response variable given a set of covariates between two populations. Such a testing problem is related to transfer learning and causal inference. We develop a nonparametric procedure by combining recent advances in conformal prediction with some new ingredients such as a novel choice of conformity score and data-driven choices of weight and score functions.

Date: 2022-10-07 Time: 15:30-16:30 (Montreal time) https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: High-dimensional unstructured data such images or sensor data can often be collected cheaply in experiments, but is challenging to use in a causal inference pipeline without extensive engineering and domain knowledge to extract underlying latent factors. The long term goal of causal representation learning is to find appropriate assumptions and methods to disentangle latent variables and learn the causal mechanisms that explain a system’s behaviour.

Date: 2022-09-30 Time: 15:30-16:30 (Montreal time) HTTPS://US06WEB.ZOOM.US/J/84226701306?PWD=UEZ5NVPZAULLDW5QNU8VZZIVBEJXQT09 MEETING ID: 842 2670 1306 PASSCODE: 692788 Abstract: Capture-recapture experiments are widely used to collect data needed to estimate the abundance of a closed population. To account for heterogeneity in the capture probabilities, Huggins (1989) and Alho (1990) proposed a semiparametric model in which the capture probabilities are modelled parametrically and the distribution of individual characteristics is left unspecified. A conditional likelihood method was then proposed to obtain point estimates and Wald-type confidence intervals for the abundance.

Date: 2022-09-16 Time: 15:20-16:20 (Montreal time) https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: We propose inferential tools for functional linear quantile regression where the conditional quantile of a scalar response is assumed to be a linear functional of a functional covariate. In contrast to conventional approaches, we employ kernel convolution to smooth the original loss function. The coefficient function is estimated under a reproducing kernel Hilbert space framework. A gradient descent algorithm is designed to minimize the smoothed loss function with a roughness penalty.

Date: 2022-09-09 Time: 15:30-16:30 (Montreal time) https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: For concertgoers, musical interpretation is the most important factor in determining whether or not we enjoy a classical performance. Every performance includes mistakes—intonation issues, a lost note, an unpleasant sound—but these are all easily forgotten (or unnoticed) when a performer engages her audience, imbuing a piece with novel emotional content beyond the vague instructions inscribed on the printed page.