546 citations · 1.4k across the 15 of their papers we have counts for
9 papers · 1 filter
CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships
Rebecca Roelofs, Liting Sun, Ben Caine +4
As machine learning models become increasingly prevalent in motion forecasting for autonomous vehicles (AVs), it is critical to ensure that model predictions are safe and reliable.…
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre +8
The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-o…
Spectral Bias in Practice: The Role of Function Frequency in Generalization
Sara Fridovich-Keil, Raphael Gontijo-Lopes, Rebecca Roelofs
Despite their ability to represent highly expressive functions, deep learning models seem to find simple solutions that generalize surprisingly well. Spectral bias -- the tendency…
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning
Anders Andreassen, Yasaman Bahri, Behnam Neyshabur +1
Although machine learning models typically experience a drop in performance on out-of-distribution data, accuracies on in- versus out-of-distribution data are widely observed to fo…
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
David Berthelot, Rebecca Roelofs, Kihyuk Sohn +2
We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one…
Mitigating Bias in Calibration Error Estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens +1
For an AI system to be reliable, the confidence it expresses in its decisions must match its accuracy. To assess the degree of match, examples are typically binned by confidence an…
Do Image Classifiers Generalize Across Time?
Vaishaal Shankar, Achal Dave, Rebecca Roelofs +3
We study the robustness of image classifiers to temporal perturbations derived from videos. As part of this study, we construct two datasets, ImageNet-Vid-Robust and YTBB-Robust ,…
Do CIFAR-10 Classifiers Generalize to CIFAR-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt +1
Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing mod…
Large Scale Kernel Learning using Block Coordinate Descent
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman +1
We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points. Armed with this capability, w…