Search Results for author:
Found 9 papers, 3 papers with code
Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels
In this work we propose OpenFSP, a framework that allows for easy creation of new domains from a handful of simple labels that can be generated without specific NLP knowledge.
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Model compression is important in federated learning (FL) with large models to reduce communication cost.
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task.
Federated Learning via Synthetic Data
Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data.
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning
We generalize Stone's Theorem in the noise free setting, proving consistency for well known classifiers such as $k$-NN, histogram and kernel estimators under conditions which mirror classical results.
Federated User Representation Learning
Furthermore, we show that user embeddings learned in FL and the centralized setting have a very similar structure, indicating that FURL can learn collaboratively through the shared parameters while preserving user privacy.
Active Federated Learning
To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client to maximize efficiency.
Active Learning for Non-Parametric Regression Using Purely Random Trees
Active learning is the task of using labelled data to select additional points to label, with the goal of fitting the most accurate model with a fixed budget of labelled points.
Online Multiclass Boosting
Recent work has extended the theoretical analysis of boosting algorithms to multiclass problems and to online settings.
