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Saachi Jain

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4 papers

2 author rows

NeurIPS Conference 2024 Conference Paper

Improving Subgroup Robustness via Data Selection

Saachi Jain
Kimia Hamidieh
Kristian Georgiev
Andrew Ilyas
Marzyeh Ghassemi
Aleksander Mądry

Machine learning models can often fail on subgroups that are underrepresentedduring training. While dataset balancing can improve performance onunderperforming groups, it requires access to training group annotations and canend up removing large portions of the dataset. In this paper, we introduceData Debiasing with Datamodels (D3M), a debiasing approachwhich isolates and removes specific training examples that drive the model'sfailures on minority groups. Our approach enables us to efficiently traindebiased classifiers while removing only a small number of examples, and doesnot require training group annotations or additional hyperparameter tuning.

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ICLR Conference 2023 Conference Paper

Distilling Model Failures as Directions in Latent Space

Saachi Jain
Hannah Lawrence
Ankur Moitra
Aleksander Madry

Existing methods for isolating hard subpopulations and spurious correlations in datasets often require human intervention. This can make these methods labor-intensive and dataset-specific. To address these shortcomings, we present a scalable method for automatically distilling a model's failure modes. Specifically, we harness linear classifiers to identify consistent error patterns, and, in turn, induce a natural representation of these failure modes as directions within the feature space. We demonstrate that this framework allows us to discover and automatically caption challenging subpopulations within the training dataset. Moreover, by combining our framework with off-the-shelf diffusion models, we can generate images that are especially challenging for the analyzed model, and thus can be used to perform synthetic data augmentation that helps remedy the model's failure modes.

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ICML Conference 2022 Conference Paper

Combining Diverse Feature Priors

Saachi Jain
Dimitris Tsipras
Aleksander Madry

To improve model generalization, model designers often restrict the features that their models use, either implicitly or explicitly. In this work, we explore the design space of leveraging such feature priors by viewing them as distinct perspectives on the data. Specifically, we find that models trained with diverse sets of explicit feature priors have less overlapping failure modes, and can thus be combined more effectively. Moreover, we demonstrate that jointly training such models on additional (unlabeled) data allows them to correct each other’s mistakes, which, in turn, leads to better generalization and resilience to spurious correlations.

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ICLR Conference 2022 Conference Paper

Missingness Bias in Model Debugging

Saachi Jain
Hadi Salman
Eric Wong 0001
Pengchuan Zhang
Vibhav Vineet
Sai Vemprala
Aleksander Madry

Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools. However, in computer vision, pixels cannot simply be removed from an image. One thus tends to resort to heuristics such as blacking out pixels, which may in turn introduce bias into the debugging process. We study such biases and, in particular, show how transformer-based architectures can enable a more natural implementation of missingness, which side-steps these issues and improves the reliability of model debugging in practice.

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