# Presentations

** Robust Deep Learning Under Distribution Shift**

Zachary C. Lipton

Simons Institute [PDF]

** Troubling Trends in Machine Learning Scholarship**

Zachary C. Lipton

Institute for Advanced Study [PDF]

** The right way to do the wrong thing (with ML)? — [Talk focuses on paper "Does Mitigating ML's Impact Disparity Require Treatment Disparity?"]**

Zachary C. Lipton

Dali 2019 [PDF]

** Invited talk at Critiquing and Correcting Trends in Machine Learning**

Zachary C. Lipton

NeurIPS 2018 [PDF]

** Integrating ML's Theory and Experiment (With Presenter Notes)**

Zachary C. Lipton

NeurIPS 2018 [PDF]

** Efficient Interactive Deep Learning with Humans in the Loop**

Zachary C. Lipton

The 8th Annual Henry Taub TCE Conference | Deep Learning: Theory & Practice [PDF]

** Detecting and Correcting for Label Shift with Black Box Predictors **

Zachary C. Lipton, Yu-Xiang Wang, Alex Smola

Machine Learning Department Faculty Seminar @ Carnegie Mellon University [PDF]

** Predicting Surgery Duration with Neural Heteroscedastic Regression (Spotlight Talk)**

Nathan Ng, Rodney A Gabriel, Julian McAuley, Charles Elkan, Zachary C Lipton

Machine Learning for Healthcare (MLHC 2017) [PDF]

** The Mythos of Model Interpretability - NYU/AIAIAI Version**

Zachary C. Lipton

Algorithms and Explanations (NYU), AIAIAI (Oslo) [PDF]

** Finding Structure in Time Series Data**

Zachary C. Lipton

Carnegie Mellon [PDF]

** Shallow Learning (Part 1)**

Zachary C. Lipton

UCSD CSE 258 [PDF]

** Shallow Learning (Part 2)**

Zachary C. Lipton

UCSD CSE 258 [PDF]

** The Mythos of Model Interpretability**

Zachary C. Lipton

Machine Learning for Healthcare (MLHC 2016) [PDF]

** A Critical Review of Recurrent Neural Networks for Sequence Learning**

Zachary C. Lipton

UCSD Research Exam [PDF]