5 citations · 7 across the 10 of their papers we have counts for
5 papers
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models
Masoud Monajatipoor, Liunian Harold Li, Mozhdeh Rouhsedaghat +2
Large-scale language models have shown the ability to adapt to a new task via conditioning on a few demonstrations (i.e., in-context learning). However, in the vision-language doma…
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning
Dingwen Kong, Lin F. Yang
An appropriate reward function is of paramount importance in specifying a task in reinforcement learning (RL). Yet, it is known to be extremely challenging in practice to design a…
Does Sparsity Help in Learning Misspecified Linear Bandits?
Jialin Dong, Lin F. Yang
Recently, the study of linear misspecified bandits has generated intriguing implications of the hardness of learning in bandits and reinforcement learning (RL). In particular, Du e…
From Local to Global: Spectral-Inspired Graph Neural Networks
Ningyuan Huang, Soledad Villar, Carey E. Priebe +4
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs are message-passing algorithms (MPNNs) that aggregate and combine signals in a…
Warmth Elevating the Depths: Shallower Voids with Warm Dark Matter
Lin F. Yang, Mark C. Neyrinck, Miguel A. Aragon-Calvo +2
Warm dark matter (WDM) has been proposed as an alternative to cold dark matter (CDM), to resolve issues such as the apparent lack of satellites around the Milky Way. Even if WDM is…