Search Results for author: Tegan Maharaj

Found 14 papers, 6 papers with code

Revealing the Incentive to Cause Distributional Shift

no code implementations29 Sep 2021 David Krueger, Tegan Maharaj, Jan Leike

We use these unit tests to demonstrate that changes to the learning algorithm (e. g. introducing meta-learning) can cause previously hidden incentives to be revealed, resulting in qualitatively different behaviour despite no change in performance metric.

Meta-Learning

Predicting Infectiousness for Proactive Contact Tracing

1 code implementation ICLR 2021 Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).

Hidden Incentives for Auto-Induced Distributional Shift

no code implementations19 Sep 2020 David Krueger, Tegan Maharaj, Jan Leike

We introduce the term auto-induced distributional shift (ADS) to describe the phenomenon of an algorithm causing a change in the distribution of its own inputs.

Meta-Learning Q-Learning

ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

1 code implementation NeurIPS 2017 Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal

We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.

A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering

2 code implementations CVPR 2017 Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, Christopher Pal

In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models' predictions, and compare these with human performance.

Language Modelling Object Detection +1

Surprisal-Driven Zoneout

no code implementations24 Oct 2016 Kamil Rocki, Tomasz Kornuta, Tegan Maharaj

We propose a novel method of regularization for recurrent neural networks called suprisal-driven zoneout.

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