ML-RSA @NeurIPS 2020

Accepted Papers

These are papers which have been reviewed and accepted.

WILDS: A Survey and Benchmark of in-the-Wild Distribution Shifts

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Richard Lanas Phillips, Sara M Beery, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series: From Discretization to Attention and Invariance

Satya Narayan Shukla, Benjamin Marlin
Deconstructing the Structure of Sparse Neural Networks

Maxwell D Van Gelder, Mitchell Wortsman, Kiana Ehsani
Feature Removal Is a Unifying Principle For Model Explanation Methods

Ian Covert, Scott Lundberg, Su-In Lee
Revisiting "Qualitatively Characterizing Neural Network Optimization Problems"

Jonathan Frankle
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?

Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification

Edward Raff, Charles Nicholas
A Brief Survey of Loop Closure Detection: A Case for Rethinking Evaluation of Intelligent Systems

Samer Nashed
Towards falsifiable interpretability research

Matthew L Leavitt, Ari S Morcos
Indic-Transformers: An Analysis of Transformer Language Models for Indian Languages

Kushal Jain, Adwait Deshpande, Kumar Shridhar, Felix Laumann, Ayushman Dash
Survey on Modeling Intensity Function of Hawkes Process Using Neural Models

Jayesh Malaviya
The Hardware Lottery

Sara Hooker
Opportunities for a More Interdisciplinary Approach to Perceptions of Fairness in Machine Learning

Sophia Dasch, Vincent Rice, Venkat Lakshminarayanan, Taiwo A Togun, Sarah M Brown, C. Malik Boykin
Challenging common interpretability assumptions in feature attribution explanations

Jonathan Dinu
AI and the Everything in the Whole Wide World Benchmark

Deborah I Raji, Emily Denton, Alex Hanna, Emily M. Bender, Amandalynne Paullada
Data and its (dis)contents: A survey of dataset development and use in machine learning research

Amandalynne Paullada, Deborah I Raji, Emily M. Bender, Emily Denton, Alex Hanna
Challenges in Deploying Machine Learning: a Survey of Case Studies

Andrei Paleyes, Raoul-Gabriel Urma, Neil D Lawrence
A Survey of Deep Learning Approaches for OCR and Document Understanding

Nishant Subramani, Alexandre Matton, Malcolm Greaves, Adrian Lam
Counterfactual Explanations for Machine Learning: A Review

Sahil Verma, John Dickerson, Keegan Hines
Beyond Methods Reproducibility in Machine Learning

Leif Hancox-Li
Understanding Attention: In Minds and Machines

Shriraj P Sawant, Shruti Singh
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, Isabel Valera