Rebecca Roelofs
14 papers · 2017–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Renaissance Researcher (5) π Interdisciplinary Bridge π Academic Marathon (8) π Conference Polyglot (6) πΊοΈ Taxonomy Completionist (25)
π
Academic Marathon
(8)
π§
Keyword Pioneer
πΊοΈ
Taxonomy Completionist
(25)
π₯
Mega-Team
(22)
π
Triple Crown
β
The Questioner
(3)
π₯
Unstoppable
(5)
ποΈ
Keyword Collector
(51)
π
Century Club
(14)
β‘
Prolific Year
(7)
Conferences
NIPS (5)
ICLR (3)
ICML (3)
AISTATS (1)
CVPR (1)
ICCV (1)
Top co-authors
Keywords
image classification
(4)
model ensemble
(2)
stochastic gradient descent
(1)
benchmark evaluation
(1)
neural network training
(1)
imitation learning
(1)
object recognition
(1)
neural network optimization
(1)
video classification
(1)
data augmentation
(1)
model evaluation
(1)
multi-label classification
(1)
autonomous driving
(1)
confidence calibration
(1)
model merging
(1)
transfer learning
(1)
loss landscape
(1)
distribution shift
(1)
classification accuracy
(1)
zero-shot learning
(1)
Papers
Training Language Models to Self-Correct via Reinforcement Learning
ICLR 2025
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
NIPS 2023
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet
NIPS 2022
Spectral Bias in Practice: The Role of Function Frequency in Generalization
NIPS 2022
Mitigating Bias in Calibration Error Estimation
AISTATS 2022
Robust Fine-Tuning of Zero-Shot Models
CVPR 2022
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
ICLR 2022
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
ICLR 2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
ICML 2022
Do Image Classifiers Generalize Across Time?
ICCV 2021
Evaluating Machine Accuracy on ImageNet
ICML 2020
Do ImageNet Classifiers Generalize to ImageNet?
ICML 2019
A Meta-Analysis of Overfitting in Machine Learning
NIPS 2019
The Marginal Value of Adaptive Gradient Methods in Machine Learning
NIPS 2017