RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism.
Nurilla Avazov (presenter)
Machine Learning based Channel Modeling for Molecular MIMO Communications
The Mythos of Model Interpretability
Zachary C. Lipton
Lifelong Learning with Dynamically Expandable Networks
A New Model for Learning in Graph Domains
Causal Inference on Multivariate and Mixed-Type Data
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Associative Learning on a Continuum in Evolved
Dynamical Neural Networks
Eduardo Izquierdo,1 Inman Harvey,1 Randall D. Beer2
Troubling Trends in Machine Learning Scholarship
Zachary C. Lipton & Jacob Steinhardt
GBFHS: A Generalized Breadth-First Heuristic Search Algorithm
Best paper award ICML 2017
Understanding Black-box Predictions via Influence Functions —- Pang Wei Koh, Percy Liang
ELLA: An Efficient Lifelong Learning Algorithm
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. “Why should i trust you?: Explaining the predictions of any classifier.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016.
Link to the tool specified in the paper: https://github.com/marcotcr/lime
Link to a tool built on top of the one above: https://github.com/datascienceinc/Skater
Inferring single-trial neural population dynamics using sequential auto-encoders by Pandarinath et al, preprint 2017.
There’s a video of a presentation of it here.
Meeting April 12th
Invited Lecture from AAAI 2018
How we should evaluate machine learning for AI
Meeting March 29th
Computer-based personality judgments are more accurate than those made by humans
by Wu Youyou, Michal Kosinski and David Stillwell
Uses and abuses of AI in election campaigns
by Alistair Knott