Presentations

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]

zlipton [at] cmu [dot] edu