Lecture by Tzach Zemer

12:20

 Recruiting team and ice cream yogurt will be waiting for you

11:00-14:00

(The talk will be given in English)

Speaker:     Dr. Elad Romanov

                    Department of Statistics, Stanford

Sunday, January 19^th, 2025

15:00 - 14:00

On Principal Component Regression in High Dimension 

Abstract

Principal component regression (PCR) is a classical two-step approach to linear regression, where one first reduces the data dimension by projecting onto its leading principal components, and then performs ordinary least squares regression. We study PCR in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. Our main deliverables are asymptotically exact limiting formulas for the estimation and prediction risks, which depend in a nuanced way on the eigenvalues of the population covariance, the alignment between the population principal components and the true signal, and the number of selected components.

A key challenge in the high-dimensional regime is that the sample covariance matrix is an inconsistent estimate of its population counterpart, and thus sample principal components may fail to capture potential latent low-dimensional structure in the data. We demonstrate this point through several case studies, including that of a spiked covariance matrix.

The analysis of (random design) linear regression in high dimension typically builds on powerful results from random matrix theory, such as the Marchenko–Pastur law and deterministic equivalents for the resolvent of a sample covariance matrix. However, these standard tools alone are not sufficient for analyzing the prediction risk of PCR. To that end, we leverage and develop somewhat less standard techniques, which, to our knowledge, have not seen wide use in the statistics literature to date: multi-resolvent traces and their associated eigenvector overlap measures.

Based on joint work with Alden Green (Stanford).

https://arxiv.org/abs/2405.11676

Short Bio

Elad Romanov is a postdoctoral researcher in the Department of Statistics, Stanford, where he is hosted by Prof. David Donoho. Prior to that, he completed his PhD in the School of Computer Science, the Hebrew University of Jerusalem, where he was advised by Profs. Or Ordentlich and Matan Gavish. His research interests broadly span high-dimensional statistics, information theory and signal processing, and the mathematics of data science.

השתתפות בסמינר תיתן קרדיט שמיעה לתלמידי תואר שני ושלישי = עפ"י רישום שם מלא + מספר ת.ז. בטופס הנוכחות שיועבר באולם במהלך הסמינר

ٍاخ

  -סמינר זה יחשב כסמינר שמיעה לתלמידי תואר שני ושלישי-  This Seminar Is Considered A Hearing Seminar For Msc/Phd Students

ביום שני, 20 בינואר 2025 , בשעה 15:00
בבנין למחקר רב-תחומי, קומה 3, חדר 315
המחלקה להנדסה ביורפואית
הפקולטה להנדסה, אוניברסיטת תל אביב
יאמרו דברים לזכרו של שמואל על ידי פרופ' מיטל זילברמן
 

* הענקת מלגות הצטיינות: פרופ' נתי שקד, ראש המחלקה להנדסה ביורפואית
* הרצאות קצרות של הזוכים במלגת הצטיינות:
 

דנה אהרוני-
"אבחנה רפואית של תאים ביולוגיים בזרימה תוך כדי דימות כמותי בזמן אמת וללא
צביעה המונחה אינטלגנציה מלאכותית "
 

ניר מלניקוב-
"פיתוח מערכת כוויה להבנת האינטראקציות בין עור מהונדס ורקמת השומן שמתחתיו "

Invitation

You are invited to a gathering and award of scholarships in memory of
Prof. Shmuel Einav
 

to be held on Monday, January 20, 2025 at 15:00 

at the Multi-Disciplinary Research Building, 3rd floor, room 315
 

Department of Biomedical Engineering
Faculty of Engineering, Tel Aviv University