156 citations · 502 across the 18 of their papers we have counts for
4 papers · 1 filter
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Huaxiu Yao, Caroline Choi, Bochuan Cao +3
Distribution shift occurs when the test distribution differs from the training distribution, and it can considerably degrade performance of machine learning models deployed in the…
On Divergence Measures for Bayesian Pseudocoresets
Balhae Kim, Jungwon Choi, Seanie Lee +3
A Bayesian pseudocoreset is a small synthetic dataset for which the posterior over parameters approximates that of the original dataset. While promising, the scalability of Bayesia…
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
Yoonho Lee, Annie S. Chen, Fahim Tajwar +4
A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the n…
Diversify and Disambiguate: Learning From Underspecified Data
Yoonho Lee, Huaxiu Yao, Chelsea Finn
Many datasets are underspecified: there exist multiple equally viable solutions to a given task. Underspecification can be problematic for methods that learn a single hypothesis be…