7 citations · 12 across the 6 of their papers we have counts for
8 papers
A General Framework for Auditing Differentially Private Machine Learning
Fred Lu, Joseph Munoz, Maya Fuchs +5
We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward…
Neural Bregman Divergences for Distance Learning
Fred Lu, Edward Raff, Francis Ferraro
Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some vari…
Continuously Generalized Ordinal Regression for Linear and Deep Models
Fred Lu, Francis Ferraro, Edward Raff
Ordinal regression is a classification task where classes have an order and prediction error increases the further the predicted class is from the true class. The standard approach…
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech
Gaoussou Youssouf Kebe, Luke E. Richards, Edward Raff +2
Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primaril…
Discriminative and Generative Transformer-based Models For Situation Entity Classification
Mehdi Rezaee, Kasra Darvish, Gaoussou Youssouf Kebe +1
We re-examine the situation entity (SE) classification task with varying amounts of available training data. We exploit a Transformer-based variational autoencoder to encode senten…
Neural Variational Learning for Grounded Language Acquisition
Nisha Pillai, Cynthia Matuszek, Francis Ferraro
We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a…
Sampling Approach Matters: Active Learning for Robotic Language Acquisition
Nisha Pillai, Edward Raff, Francis Ferraro +1
Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning ap…
Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning
Patrick Jenkins, Rishabh Sachdeva, Gaoussou Youssouf Kebe +7
Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI. In practice the da…