23 citations · 48 across the 6 of their papers we have counts for
5 papers · 1 filter
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Contrastive Identification of Covariate Shift in Image Data
Matthew L. Olson, Thuy-Vy Nguyen, Gaurav Dixit +3
Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data. Howe…
Generative Particle Variational Inference via Estimation of Functional Gradients
Neale Ratzlaff, Qinxun Bai, Li Fuxin +1
Recently, particle-based variational inference (ParVI) methods have gained interest because they can avoid arbitrary parametric assumptions that are common in variational inference…
Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff, Qinxun Bai, Li Fuxin +1
Efficient exploration remains a challenging problem in reinforcement learning, especially for those tasks where rewards from environments are sparse. A commonly used approach for e…
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Neale Ratzlaff, Li Fuxin
Standard neural networks are often overconfident when presented with data outside the training distribution. We introduce HyperGAN, a new generative model for learning a distributi…