Search Results for author:
Found 7 papers, 5 papers with code
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive.
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Modern machine learning models deployed in the wild can encounter both covariate and semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and OOD detection respectively.
One for All: Simultaneous Metric and Preference Learning over Multiple Users
This paper investigates simultaneous preference and metric learning from a crowd of respondents.
A Low-complexity Brain-computer Interface for High-complexity Robot Swarm Control
We construct a scalable dictionary of robotic behaviors that can be searched simply and efficiently by a BCI user, as we demonstrate through a large-scale user study testing the feasibility of our interaction algorithm, a user test of the full BCI system on (virtual and real) robot swarms, and simulations that verify our results against theoretical models.
Feedback Coding for Active Learning
The iterative selection of examples for labeling in active machine learning is conceptually similar to feedback channel coding in information theory: in both tasks, the objective is to seek a minimal sequence of actions to encode information in the presence of noise.
Generative causal explanations of black-box classifiers
Our objective function encourages both the generative model to faithfully represent the data distribution and the latent factors to have a large causal influence on the classifier output.
Active Ordinal Querying for Tuplewise Similarity Learning
Many machine learning tasks such as clustering, classification, and dataset search benefit from embedding data points in a space where distances reflect notions of relative similarity as perceived by humans.
