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Found 8 papers, 5 papers with code
TIPI: Test Time Adaptation With Transformation Invariance
Test Time Adaptation offers a means to combat this problem, as it allows the model to adapt during test time to the new data distribution, using only unlabeled test data batches.
Task-Agnostic Robust Representation Learning
To tackle this problem, a great amount of research has been done to study the training procedure of a network to improve its robustness.
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
Moreover, we show that our method is more computationally efficient and has a better dependence on the effective dimension of the neural network than an online counterpart.
Task Conditioned Stochastic Subsampling
Deep Learning algorithms are designed to operate on huge volumes of high dimensional data such as images.
KL Guided Domain Adaptation
A common approach in the domain adaptation literature is to learn a representation of the input that has the same (marginal) distribution over the source and the target domain.
Domain Invariant Representation Learning with Domain Density Transformations
Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains.
Set Based Stochastic Subsampling
Deep models are designed to operate on huge volumes of high dimensional data such as images.
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning
Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss.
