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Found 8 papers, 5 papers with code
Gradient Coreset for Federated Learning
We conduct experiments using four real-world datasets and show that GCFL is (1) more compute and energy efficient than FL, (2) robust to various kinds of noise in both the feature space and labels, (3) preserves the privacy of the validation dataset, and (4) introduces a small communication overhead but achieves significant gains in performance, particularly in cases when the clients' data is noisy.
Using Early Readouts to Mediate Featural Bias in Distillation
Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks.
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training
Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost.
Adaptive Mixing of Auxiliary Losses in Supervised Learning
In several supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective.
Training Data Subset Selection for Regression with Controlled Generalization Error
First, we represent this problem with simplified constraints using the dual of the original training problem and show that the objective of this new representation is a monotone and alpha-submodular function, for a wide variety of modeling choices.
Effective Evaluation of Deep Active Learning on Image Classification Tasks
Issues in the current literature include sometimes contradictory observations on the performance of different AL algorithms, unintended exclusion of important generalization approaches such as data augmentation and SGD for optimization, a lack of study of evaluation facets like the labeling efficiency of AL, and little or no clarity on the scenarios in which AL outperforms random sampling (RS).
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.
