Search Results for author:
Found 12 papers, 7 papers with code
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
Active Learning (AL) is a human-in-the-loop framework to interactively and adaptively label data instances, thereby enabling significant gains in model performance compared to random sampling.
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models
A salient characteristic of pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing model capacity and pre-training dataset size.
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Training deep networks and tuning hyperparameters on large datasets is computationally intensive.
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Our central insight is that using an informative subset of the dataset for model training runs involved in hyper-parameter optimization, allows us to find the optimal hyper-parameter configuration significantly faster.
GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
To address this, replay-based CL approaches maintain and repeatedly retrain on a small buffer of data selected across encountered tasks.
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
These LFs, in turn, have been used to generate a large amount of additional noisy labeled data, in a paradigm that is now commonly referred to as data programming.
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
In this work, we propose RETRIEVE, a coreset selection framework for efficient and robust semi-supervised learning.
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
We show rigorous theoretical and convergence guarantees of the proposed algorithm and, through our extensive experiments on real-world datasets, show the effectiveness of our proposed framework.
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Finally, we propose Glister-Active, an extension to batch active learning, and we empirically demonstrate the performance of Glister on a wide range of tasks including, (a) data selection to reduce training time, (b) robust learning under label noise and imbalance settings, and (c) batch-active learning with several deep and shallow models.
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal.
Semi-Supervised Data Programming with Subset Selection
The first contribution of this work is an introduction of a framework, \model which is a semi-supervised data programming paradigm that learns a \emph{joint model} that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space.
