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Statistics Colloquium

Statistics Colloquium

State-Space Models for Animal Movement

January 30, 2025

Speakers:

Marie Auger-Méthé, University of British Columbia

State–space models (SSMs) are commonly used to model ecological time series such as population dynamics, animal movement, and capture–recapture dat

Towards Interpretable and Trustworthy Network-Assisted Prediction

October 24, 2024

Speakers:

Liza Levina, University of Michigan

Machine learning algorithms usually assume that training samples are independent when learning to predict an outcome from a set of features.

Tensors in High Dimensional Data Analysis: Opportunities and Challenges

October 17, 2024

Speakers:

Ming Yuan, Columbia University

Large amount of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as

Winners with Confidence: Discrete Argmin Inference with Application to Model Selection

October 10, 2024

Speakers:

Jing Lei, Carnegie Mellon University

We study the problem of finding the index of the minimum value of a vector from noisy observations.

The Emergence of Clusters in Self-Attention Dynamics

October 3, 2024

Speakers:

Philippe Rigollet, MIT

Since their introduction in 2017, Transformers have revolutionized large language models and the broader field of deep learning.

MCMC, Variational Inference, and Reverse Diffusion Monte Carlo

September 19, 2024

Speakers:

Yian Ma, University of California

I will introduce some recent progress towards understanding the scalability of Markov chain Monte Carlo (MCMC) methods and their comparative advant

Bootstrapping Linear and Non-Linear Spectral Statistics in High Dimensions

September 12, 2024

Speakers:

Miles Lopes, UC Davis

Statistics arising from the eigenvalues of sample covariance matrices, known as spectral statistics, are fundamental to many tasks in high-dimensio

Bridging the Gap: A Comprehensive Approach to Responsible Data Science Education

August 15, 2024

Speakers:

Bin Yu, UC Berkeley

The rapid growth of AI relies on data science, but education lags behind.

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