December 6th
William
RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism.
https://arxiv.org/abs/1608.05745
November 29th
Nurilla Avazov (presenter)
Machine Learning based Channel Modeling for Molecular MIMO Communications
https://arxiv.org/abs/1704.00870
Nov 22nd
Katerina
The Mythos of Model Interpretability
Zachary C. Lipton
(https://arxiv.org/abs/1606.03490)
Oct 25th
Diana
Lifelong Learning with Dynamically Expandable Networks
https://arxiv.org/abs/1708.01547
Oct 18th
Pat
A New Model for Learning in Graph Domains
Oct 11th
Alex
Causal Inference on Multivariate and Mixed-Type Data
http://www.ecmlpkdd2018.org/wp-content/uploads/2018/09/444.pdf
Oct 4th
Jörg
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
https://arxiv.org/pdf/1712.03141.pdf
September 13th
Matthew Egbert
Associative Learning on a Continuum in Evolved
Dynamical Neural Networks
Eduardo Izquierdo,1 Inman Harvey,1 Randall D. Beer2
August 23rd
Remco
Troubling Trends in Machine Learning Scholarship
Zachary C. Lipton & Jacob Steinhardt
21/6
Mike Barley
GBFHS: A Generalized Breadth-First Heuristic Search Algorithm
Remco
Best paper award ICML 2017
Understanding Black-box Predictions via Influence Functions —- Pang Wei Koh, Percy Liang
https://arxiv.org/pdf/1703.04730.pdf
7/6
Diana
ELLA: An Efficient Lifelong Learning Algorithm
http://proceedings.mlr.press/v28/ruvolo13.pdf
May 24th
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
April 19th
Robin
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
Video:
How we should evaluate machine learning for AI
https://vimeo.com/channels/aaai2018/page:1
Meeting March 29th
Remco
The paper
Computer-based personality judgments are more accurate than those made by humans
by Wu Youyou, Michal Kosinski and David Stillwell
http://www.pnas.org/content/112/4/1036
The implications:
Uses and abuses of AI in election campaigns
by Alistair Knott
https://ai-and-society.wiki.otago.ac.nz/images/0/0f/Ai-and-elections.pdf