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Found 20 papers, 16 papers with code
Overparameterization hurts worst-group accuracy with spurious correlations
Increasing model capacity well beyond the point of zero training error has been observed to improve average test accuracy.
Impossibility Theorems for Feature Attribution
Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods.
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Temporal shifts -- distribution shifts arising from the passage of time -- often occur gradually and have the additional structure of timestamp metadata.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Just Train Twice: Improving Group Robustness without Training Group Information
Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
Selective Classification Can Magnify Disparities Across Groups
In this paper, we find that while selective classification can improve average accuracies, it can simultaneously magnify existing accuracy disparities between various groups within a population, especially in the presence of spurious correlations.
Concept Bottleneck Models
We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
We study why overparameterization -- increasing model size well beyond the point of zero training error -- can hurt test error on minority groups despite improving average test error when there are spurious correlations in the data.
ExpBERT: Representation Engineering with Natural Language Explanations
Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text.
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Distributionally robust optimization (DRO) allows us to learn models that instead minimize the worst-case training loss over a set of pre-defined groups.
Ranked #1 on
Out-of-Distribution Generalization
on UrbanCars
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations
Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning.
Ranked #2 on
Audio Super-Resolution
on Voice Bank corpus (VCTK)
On the Accuracy of Influence Functions for Measuring Group Effects
Influence functions estimate the effect of removing a training point on a model without the need to retrain.
Stronger Data Poisoning Attacks Break Data Sanitization Defenses
In this paper, we develop three attacks that can bypass a broad range of common data sanitization defenses, including anomaly detectors based on nearest neighbors, training loss, and singular-value decomposition.
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data.
Certified Defenses for Data Poisoning Attacks
Machine learning systems trained on user-provided data are susceptible to data poisoning attacks, whereby malicious users inject false training data with the aim of corrupting the learned model.
Understanding Black-box Predictions via Influence Functions
How can we explain the predictions of a black-box model?
