STATSCALE SEMINARS
UPCOMING SEMINARS 2022

27th May 3-4pm

Ichiro Takeuchi (Nagoya University)

Title: More powerful and general conditional selective inference by parametric programming and its application to change-point detection and image segmentation.

Abstract: Conditional selective inference (SI) framework was recently introduced as a new statistical inference method for Lasso. This framework allows us to derive the exact conditional sampling distribution of the selected test statistic when the selection event is characterized by a polyhedron. In fact, this framework is not only useful for Lasso but also generally applicable to a certain class of data-driven hypotheses. A common limitation of existing SI methods is that the hypothesis selection event must be characterized in a simple tractable form such as a set of linear or quadratic inequalities. To overcome this limitation, we propose a new computational method for conditional SI based on parametric programming (PP), which we call called PP-based SI. In this talk, after briefly reviewing the conditional SI framework, we show that the proposed PP-based SI is more powerful than (vanilla) SI and applicable to a wider class of problems. As examples of how PP-based SI can extend the applicability of conditional SI, we present our recent works on conditional SI for multi-dimensional change-point detection and image segmentation by a deep neural network (this is joint work with Vo Nguyen Le Duy at Nagoya Institute of Technology).

If you are interesting in joining the seminars please email Dr Hyeyoung Maeng 

PREVIOUS SEMINARS

13th May 2022 - Philipp Klein (Otto von Guericke University Magdeburg)

Title: Anomaly detection based on MOSUM statistics in large image data

22nd April 2022- Daren Wang (Notre Dame)

Optimal High-dimensional Change Point Testing in Regression Settings

18th March 2022 - Lan Luo (University of Iowa)

Real-Time Regression Analysis with Streaming Health Datasets

 

4th March 2022 - Bouchra Nasri (University of Montreal)

Change-Point Problems For Multivariate Time Series Using Pseudo-Observations

25th February 2022 - Yajun Mei (Georgia Tech)

Bandit multi-stream sequential change-point detection

 

 

4th February 2022 - Karl Hallgren (Imperial College London)

Bayesian changepoint models motivated by cyber security applications

21st January 2022 - Jacob Bien (University of Southern California)

Mixture of Multivariate Regressions Modeling for Oceanographic Flow Cytometry Data

3 December 2021 - Housen Li (University of Göttingen)

Distributional limits of graph cuts on discretized samples


 

19 November 2021 - Guo Yu (University of California, Santa Barbara)

Reluctant interaction modeling in generalized linear models

5 November 2021- Francesco Sanna Passino (Imperial College London)

Mutually exciting point process graphs for modelling dynamic networks

22 October 2021- Ichiro Takeuchi (Nagoya Institute of Technology)

More powerful and general conditional selective inference by parametric programming and its application to multi-dimensional change-point detection

2 July 2021 - Kevin Lin (University of Pennsylvania)

Time-varying stochastic block models, with application to understanding the dynamics of gene co-expression

18 June 2021 - Runmin Wang (Southern Methodist University)

Dating the Break in High Dimensional Data


 

4 June 2021 -  Abolfazl Safikhani (University of Florida)

Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models


 

21th May 2021 - Hao Ni (University College London)

Sig-Wasserstein Generative models to generate realistic synthetic time series

7th May 2021 - Lynna Chu (Iowa State University)

Sequential Change-point Detection for High-Dimensional and non-Euclidean Data


 

26th March 2021 - Hao Chen (UC Davis)

A universal event detection framework for neuropixels data

 


 

12th March 2021 - Sumanta Basu (Cornell University)

Learning Financial Networks with Graphical Models of Time Series Data

26th February 2021 - Eric Kolaczyk (Boston University)

How hard is it to work with a network `average'?

12th February 2021 - Matteo Barigozzi (Università di Bologna)
Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm

29th January 2021 - Holger Dette (Ruhr-Universitaet Bochum)

Testing Relevant Hypotheses in Functional Time Series via Self-Normalization

4th December 2020 - Priyanga Dilini Talagala (University of Moratuwa)

Anomaly Detection in Streaming Time Series Data

 

 

20th November 2020 - Florian Pein (University of Cambridge)

About the loss function for cross-validation in change-point regression

 

 

6th November 2020 - Alex Aue (UC Davis)

Random matrix theory aids statistical inference in high dimensions

 

 

23rd October 2020 - Yoav Zemel (University of Cambridge)

Probabilistic approximations to optimal transport

 

 

9th October 2020 - Solt Kovacs (ETH Zurich)

Optimistic search strategy: change point detection for large-scale data via adaptive logarithmic queries

 

 

17th July 2020 - Tobias Kley (University of Bristol)

A new approach for open-end sequential change point monitoring

 

 

3rd July, 2020 - Claudia Kirch (Otto-von-Guericke University)

Functional change point detection for fMRI data 

 

 

19th June, 2020 - Martin Tveten (Dept. of Mathematics, University of Oslo)

Scalable changepoint and anomaly detection in cross-correlated data

 

 

5th June, 2020 - Yudong Chen (University of Cambridge)

High-dimensional, multiscale online changepoint detection