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Found 10 papers, 7 papers with code
Focus on the Common Good: Group Distributional Robustness Follows
We consider the problem of training a classification model with group annotated training data.
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
In several real world applications, machine learning models are deployed to make predictions on data whose distribution changes gradually along time, leading to a drift between the train and test distributions.
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations
Our goal is to evaluate the accuracy of a black-box classification model, not as a single aggregate on a given test data distribution, but as a surface over a large number of combinations of attributes characterizing multiple test data distributions.
An Analysis of Frame-skipping in Reinforcement Learning
In this paper, we investigate the role of the parameter $d$ in RL; $d$ is called the "frame-skip" parameter, since states in the Atari domain are images.
NLP Service APIs and Models for Efficient Registration of New Clients
Each client uses an unsupervised, corpus-based sketch to register to the service.
Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint
It is believed that by processing augmented inputs in tandem with the original ones, the model learns a more robust set of features which are shared between the original and augmented counterparts.
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
The domain specific components are discarded after training and only the common component is retained.
Ranked #1 on
Domain Generalization
on LipitK
Parallel Iterative Edit Models for Local Sequence Transduction
We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC).
Topic Sensitive Attention on Generic Corpora Corrects Sense Bias in Pretrained Embeddings
Given a small corpus $\mathcal D_T$ pertaining to a limited set of focused topics, our goal is to train embeddings that accurately capture the sense of words in the topic in spite of the limited size of $\mathcal D_T$.
Generalizing Across Domains via Cross-Gradient Training
We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains.
Ranked #58 on
Domain Generalization
on PACS
