54 citations · 89 across the 9 of their papers we have counts for
9 papers
Investigating Vision Foundational Models for Tactile Representation Learning
Ben Zandonati, Ruohan Wang, Ruihan Gao +1
Tactile representation learning (TRL) equips robots with the ability to leverage touch information, boosting performance in tasks such as environment perception and object manipula…
Robust Meta-Representation Learning via Global Label Inference and Classification
Ruohan Wang, Isak Falk, Massimiliano Pontil +1
Few-shot learning (FSL) is a central problem in meta-learning, where learners must efficiently learn from few labeled examples. Within FSL, feature pre-training has recently become…
Schedule-Robust Online Continual Learning
Ruohan Wang, Marco Ciccone, Giulia Luise +3
A continual learning (CL) algorithm learns from a non-stationary data stream. The non-stationarity is modeled by some schedule that determines how data is presented over time. Most…
The Role of Global Labels in Few-Shot Classification and How to Infer Them
Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto
Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data. Recently, feature pre-training has become a ubi…
Support-weighted Adversarial Imitation Learning
Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori +1
Adversarial Imitation Learning (AIL) is a broad family of imitation learning methods designed to mimic expert behaviors from demonstrations. While AIL has shown state-of-the-art pe…
Structured Prediction for Conditional Meta-Learning
Ruohan Wang, Yiannis Demiris, Carlo Ciliberto
The goal of optimization-based meta-learning is to find a single initialization shared across a distribution of tasks to speed up the process of learning new tasks. Conditional met…