36 citations · 41 across the 3 of their papers we have counts for
10 papers
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Ramneet Kaur, Susmit Jha, Anirban Roy +4
Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence…
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Leshang Chen, Hongyi Wang +2
There has been a growing need to provide Byzantine-resilience in distributed model training. Existing robust distributed learning algorithms focus on developing sophisticated robus…
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition
Yaodong Yu, Zitong Yang, Edgar Dobriban +2
Adversarially trained models exhibit a large generalization gap: they can interpolate the training set even for large perturbation radii, but at the cost of large test error on cle…
Sparse sketches with small inversion bias
Michał Dereziński, Zhenyu Liao, Edgar Dobriban +1
For a tall matrix and a random sketching matrix , the sketched estimate of the inverse covariance matrix is typically biased: $E[(\…
DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, Susan B. Davidson
Machine learning models are not static and may need to be retrained on slightly changed datasets, for instance, with the addition or deletion of a set of data points. This has many…
How to reduce dimension with PCA and random projections?
Fan Yang, Sifan Liu, Edgar Dobriban +1
In our "big data" age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension red…