Education
Value Imprint: AT echnique for Auditing the Human Values Embedded in RLHF Datasets
Our analysis involved a two-phase process.
Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers
By employing object identifiers, we transform diverse 3D scene-language tasks into a unified question-answering format, facilitating joint training without the need for additional task-specific heads.
Information-theoretic Limits of Online Classification with Noisy Labels
Learning from noisy data is a fundamental problem in many machine learning applications.
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
We demonstrate that plasticity loss is pervasive under domain shift in this regime, and that a number of methods developed to resolve it in other settings fail, sometimes even performing worse than applying no intervention at all. In contrast, we find that a class of "regenerative" methods are able to consistently mitigate plasticity loss in a variety of contexts, including in gridworld tasks and
cdf6f8e9fd9aeaf79b6024caec24f15b-Paper-Datasets_and_Benchmarks_Track.pdf
To ensure quality, all charts and questions are handpicked, curated, and verified by human experts.
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models
The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on different subgroups.
Private Online Learning via Lazy Algorithms
We study the problem of private online learning, focusing on online prediction from experts (OPE) and online convex optimization (OCO).