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08/13/2023
MDB: Interactively Querying Datasets and Models
As models are trained and deployed, developers need to be able to system...
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07/12/2023
Stability Guarantees for Feature Attributions with Multiplicative Smoothing
Explanation methods for machine learning models tend to not provide any ...
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06/01/2023
TopEx: Topic-based Explanations for Model Comparison
Meaningfully comparing language models is challenging with current expla...
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05/25/2023
Rectifying Group Irregularities in Explanations for Distribution Shift
It is well-known that real-world changes constituting distribution shift...
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03/02/2023
Do Machine Learning Models Learn Common Sense?
Machine learning models can make basic errors that are easily hidden wit...
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02/21/2023
In-context Example Selection with Influences
In-context learning (ICL) is a powerful paradigm emerged from large lang...
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02/08/2023
Adversarial Prompting for Black Box Foundation Models
Prompting interfaces allow users to quickly adjust the output of generat...
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06/16/2021
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
Analyzing the worst-case performance of deep neural networks against inp...
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05/11/2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks
We show how fitting sparse linear models over learned deep feature repre...
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07/16/2020
Learning perturbation sets for robust machine learning
Although much progress has been made towards robust deep learning, a sig...
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06/30/2020
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications
Recent work has shown that it is possible to learn neural networks with ...
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02/26/2020
Overfitting in adversarially robust deep learning
It is common practice in deep learning to use overparameterized networks...
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01/12/2020
Fast is better than free: Revisiting adversarial training
Adversarial training, a method for learning robust deep networks, is typ...
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09/09/2019
Adversarial Robustness Against the Union of Multiple Perturbation Models
Owing to the susceptibility of deep learning systems to adversarial atta...
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02/21/2019
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
A rapidly growing area of work has studied the existence of adversarial ...
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05/31/2018
Scaling provable adversarial defenses
Recent work has developed methods for learning deep network classifiers ...
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11/02/2017