December 6th


RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism.

November 29th

Nurilla Avazov (presenter)

Machine Learning based Channel Modeling for Molecular MIMO Communications

Nov 22nd


The Mythos of Model Interpretability

Zachary C. Lipton


Oct 25th


Lifelong Learning with Dynamically Expandable Networks

Oct 18th


A New Model for Learning in Graph Domains

Oct 11th


Causal Inference on Multivariate and Mixed-Type Data

Oct 4th


Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

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


Troubling Trends in Machine Learning Scholarship

Zachary C. Lipton & Jacob Steinhardt


Mike Barley

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

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:

Link to a tool built on top of the one above:

April 19th


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


The paper

Computer-based personality judgments are more accurate than those made by humans

by Wu Youyou, Michal Kosinski and David Stillwell

The implications:

Uses and abuses of AI in election campaigns

by Alistair Knott