3 citations · 3 across the 7 of their papers we have counts for
7 papers
Making Scalable Meta Learning Practical
Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn +4
Despite its flexibility to learn diverse inductive biases in machine learning programs, meta learning (i.e., learning to learn) has long been recognized to suffer from poor scalabi…
Importance-aware Co-teaching for Offline Model-based Optimization
Ye Yuan, Can Chen, Zixuan Liu +2
Offline model-based optimization aims to find a design that maximizes a property of interest using only an offline dataset, with applications in robot, protein, and molecule design…
Kernelized Offline Contextual Dueling Bandits
Viraj Mehta, Ojash Neopane, Vikramjeet Das +3
Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning…
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching
Lantao Yu, Tianhe Yu, Jiaming Song +2
Offline imitation learning (IL) promises the ability to learn performant policies from pre-collected demonstrations without interactions with the environment. However, imitating be…
Modular Conformal Calibration
Charles Marx, Shengjia Zhao, Willie Neiswanger +1
Uncertainty estimates must be calibrated (i.e., accurate) and sharp (i.e., informative) in order to be useful. This has motivated a variety of methods for recalibration, which use…
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling
Chenlin Meng, Enci Liu, Willie Neiswanger +4
Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitori…