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Found 8 papers, 5 papers with code
Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations
As ERM models can be good spurious attribute predictors, CNC works by (1) using a trained ERM model's outputs to identify samples with the same class but dissimilar spurious features, and (2) training a robust model with contrastive learning to learn similar representations for same-class samples.
BARACK: Partially Supervised Group Robustness With Guarantees
Theoretically, we provide generalization bounds for our approach in terms of the worst-group performance, which scale with respect to both the total number of training points and the number of training points with group labels.
Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories
For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs.
Mandoline: Model Evaluation under Distribution Shift
If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target.
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps.
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
As the subclass labels are frequently unavailable, models trained using only the coarser-grained class labels often exhibit highly variable performance across different subclasses.
Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond
This function class, which we call the class of smooth quasar-convex functions, is parameterized by a constant $\gamma \in (0, 1]$, where $\gamma = 1$ encompasses the classes of smooth convex and star-convex functions, and smaller values of $\gamma$ indicate that the function can be "more nonconvex."
Low-Memory Neural Network Training: A Technical Report
In this paper we study a fundamental question: How much memory is actually needed to train a neural network?
