Speaker |
Makoto Yamada |
|---|---|
Affiliation |
Kyoto University and RIKEN AIP center |
Date |
Friday, 17 December 2021 |
Time |
10:00-11:00 |
Location |
Zoom |
Link |
https://ucl.zoom.us/j/92007296671 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Abstract: Finding a set of statistically significant features from complex data (e.g., nonlinear and/or multi-dimensional output data) is important for scientific discovery and has many practical applications, including biomarker discovery. In this talk, I introduce kernel-based selective inference frameworks that can be used to find a set of statistically significant features from non-linearly related data without splitting the data for selection and inference. Specifically, I introduce a selective variant of hypothesis testing framework based on post selection inference: two sample test with Maximum Mean Discrepancy (MMD), an independence test with Hilbert-Schmidt Independence Criterion (HSIC), a goodness of fit with Kernel Stein Discrepancy (KSD). For example, in the selective independence test, we propose the hsicInf algorithm, which can handle non-linearity and/or multi-variate/multi-class outputs through kernels. Then, I show applications of kernel-based selective inference algorithms and discuss potential future work. The talk will be an overview of our recent ICML, NeurIPS, and AISTATS publications. |
Biography |