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Distribution Shift

190 directly classified papers

Papers per year

Papers

DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation AAAI 2021
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder EMNLP 2020
Rethinking Importance Weighting for Deep Learning under Distribution Shift NIPS 2020
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions NIPS 2020
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision CVPR 2020
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law NIPS 2020
Stochastic Optimization for Performative Prediction NIPS 2020
Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets EMNLP 2020
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning NIPS 2020
REST: Performance Improvement of a Black Box Model via RL-Based Spatial Transformation AAAI 2020
Stable Prediction with Model Misspecification and Agnostic Distribution Shift AAAI 2020
Distributionally Robust Counterfactual Risk Minimization AAAI 2020
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing AAAI 2019
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction EMNLP 2019
To Annotate or Not? Predicting Performance Drop under Domain Shift EMNLP 2019
Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss CVPR 2019
On Target Shift in Adversarial Domain Adaptation AISTATS 2019
Differentially Private Distributed Data Summarization under Covariate Shift NIPS 2019
Conformalized Quantile Regression NIPS 2019
Toward a Characterization of Loss Functions for Distribution Learning NIPS 2019
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift NIPS 2019
Conformal Prediction Under Covariate Shift NIPS 2019
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models NIPS 2019
Hierarchical Optimal Transport for Multimodal Distribution Alignment NIPS 2019
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers NIPS 2019