7.4k citations · 22.9k across the 86 of their papers we have counts for
59 papers · 1 filter
Integrating Present and Past in Unsupervised Continual Learning
Yipeng Zhang, Laurent Charlin, Richard Zemel +1
We formulate a unifying framework for unsupervised continual learning (UCL), which disentangles learning objectives that are specific to the present and the past data, encompassing…
Distribution-Free Statistical Dispersion Control for Societal Applications
Zhun Deng, Thomas P. Zollo, Jake C. Snell +2
Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning. Previous work has focused mainly on bounding either…
SURFSUP: Learning Fluid Simulation for Novel Surfaces
Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager +2
Modeling the mechanics of fluid in complex scenes is vital to applications in design, graphics, and robotics. Learning-based methods provide fast and differentiable fluid simulator…
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
Jake C. Snell, Thomas P. Zollo, Zhun Deng +2
Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expecte…
Deep Ensembles Work, But Are They Necessary?
Taiga Abe, E. Kelly Buchanan, Geoff Pleiss +2
Ensembling neural networks is an effective way to increase accuracy, and can often match the performance of individual larger models. This observation poses a natural question: giv…
Variational Model Inversion Attacks
Kuan-Chieh Wang, Yan Fu, Ke Li +3
Given the ubiquity of deep neural networks, it is important that these models do not reveal information about sensitive data that they have been trained on. In model inversion atta…