Statistical Aspects of Trustworthy Machine Learning
Videos from BIRS Workshop
Kris Sankaran, UW-Madison
Monday Feb 12, 2024 09:04 - 10:14
Interpretability and Scientific Foundation Models: A Review
Cynthia Rudin, Duke
Monday Feb 12, 2024 10:30 - 11:02
Simpler Machine Learning Models for a Complicated World
Hongtu Zhu, The University of North Carolina at Chapel Hill
Monday Feb 12, 2024 11:02 - 11:42
Deep non-crossing quantile (NQ) learning
Yuan Ji, The University of Chicago
Monday Feb 12, 2024 14:00 - 14:34
A Class of Dependent Random Distributions Based on Atom Skipping
Hubert Baniecki, University of Warsaw
Monday Feb 12, 2024 15:01 - 15:34
Interpretable machine learning for time-to-event prediction in medicine and healthcare
Debashis Mondal, Washington University in St Louis
Monday Feb 12, 2024 15:34 - 16:04
Estimating the fraction of anomaly points
Haoda Fu, Eli Lilly and Company
Tuesday Feb 13, 2024 08:30 - 09:31
Generative AI on Smooth Manifolds: A Tutorial
Bin YU, UC Berkeley
Tuesday Feb 13, 2024 09:32 - 10:04
What is uncertainty in today's practice of data science?
Lloyd Elliott, Simon Fraser University
Tuesday Feb 13, 2024 10:30 - 10:49
Teaching Machine Learning using Data for Good
Bei Jiang, University of Alberta
Tuesday Feb 13, 2024 10:49 - 11:01
Online Local Differential Private Quantile Inference via Self-normalization
Wenlong Mou, University to Toronto
Tuesday Feb 13, 2024 11:01 - 11:13
A decorrelation method for general regression adjustment in randomized experiments
Deshan Perera, University of Calgary
Tuesday Feb 13, 2024 11:13 - 11:20
CATE: An accelerated and scalable solution for large-scale genomic data processing through GPU and CPU-based parallelization
Qingrun Zhang, University of Calgary
Tuesday Feb 13, 2024 11:20 - 11:33
eXplainable representation learning via Autoencoders revealing Critical genes
Sanmi Koyejo, Stanford University
Tuesday Feb 13, 2024 13:43 - 14:33
Algorithmic Fairness; Why it’s hard and why it’s interesting (Tutorial)
Joshua Snoke, RAND Corporation
Tuesday Feb 13, 2024 14:33 - 15:04
De-Biasing the Bias: Methods for Improving Disparity Assessments with Noisy Group Measurements
Giles Hooker, University of Pennsylvania
Tuesday Feb 13, 2024 15:31 - 16:03
A Generic Approach to Stabilized Model Distillation
Danica Sutherland, University of British Columbia
Tuesday Feb 13, 2024 16:03 - 16:36
Conditional independence measures for fairer, more reliable models
Xiaoli Meng, Harvard University
Wednesday Feb 14, 2024 08:30 - 09:04
Protecting Individua Privacy against All Adversaries – Is It possible?
Xiaoxiao Li, University of British Columbia
Wednesday Feb 14, 2024 09:09 - 09:33
Forgettable Federated Linear Learning with Certified Data Removal
Mathias Lecuyer, University of British Columbia
Wednesday Feb 14, 2024 09:36 - 10:15
PANORAMIA: Efficient Privacy Auditing of Machine Learning Models without Retraining
Wei Pan, University of Minnesota
Wednesday Feb 14, 2024 10:30 - 11:05
Some applications of large-scale trait imputation with genotyped individuals and GWAS summary data
Kasper Hansen, John Hopkins University
Wednesday Feb 14, 2024 11:05 - 12:03
Large-scale genotype prediction from RNA-seq reveals new issues in policy and ethic
Pin-Yu Chen, IBM Research
Thursday Feb 15, 2024 08:34 - 09:36
An Eye for AI: Towards Scientific Approaches for Evaluating and Improving Robustness and Safety of Foundation Models
Yufeng Liu, University of North Carolina at Chapel Hill
Thursday Feb 15, 2024 09:36 - 10:07
Statistical Significance of Clustering for High Dimensional Data
Linbo Wang, University of Toronto
Thursday Feb 15, 2024 10:31 - 11:01
Sparse Causal Learning: Challenges and Opportunities
Ying Li, University of Wisconsin-Madison
Thursday Feb 15, 2024 11:02 - 11:29
Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature
Yuanjia Wang, Columbia University
Thursday Feb 15, 2024 13:02 - 13:32
Towards Generative Models for Analyzing Multi-Dimensional Digital Phenotypes
Tengyuan Liang, University of Chicago
Thursday Feb 15, 2024 13:36 - 14:08
Randomization Inference When N = 1
Donglin Zeng, University of Michigan
Thursday Feb 15, 2024 14:09 - 14:41
Integrating Tools from Statistical Modelling and Machine Learning to Learn Optimal Treatment Regimes from Electronic Health Records
Anna Neufeld, Fred Hutchinson Cancer Center
Thursday Feb 15, 2024 15:15 - 15:45
Data thinning and its applications
Sanmi Koyejo, Stanford University
Thursday Feb 15, 2024 15:52 - 16:25
Learning from Uncertain Pairwise Preferences
