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08/02/2023
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
We introduce OpenFlamingo, a family of autoregressive vision-language mo...
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06/26/2023
Are aligned neural networks adversarially aligned?
Large language models are now tuned to align with the goals of their cre...
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06/07/2023
Proximity-Informed Calibration for Deep Neural Networks
Confidence calibration is central to providing accurate and interpretabl...
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05/23/2023
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Evaluating the factuality of long-form text generated by large language ...
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04/27/2023
DataComp: In search of the next generation of multimodal datasets
Large multimodal datasets have been instrumental in recent breakthroughs...
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02/23/2023
Out-of-Domain Robustness via Targeted Augmentations
Models trained on one set of domains often suffer performance drops on u...
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12/22/2022
Impossibility Theorems for Feature Attribution
Despite a sea of interpretability methods that can produce plausible exp...
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11/25/2022
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Distribution shift occurs when the test distribution differs from the tr...
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12/09/2021
Extending the WILDS Benchmark for Unsupervised Adaptation
Machine learning systems deployed in the wild are often trained on a sou...
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07/19/2021
Just Train Twice: Improving Group Robustness without Training Group Information
Standard training via empirical risk minimization (ERM) can produce mode...
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07/09/2021
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 pe...
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12/14/2020
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
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10/27/2020
Selective Classification Can Magnify Disparities Across Groups
Selective classification, in which models are allowed to abstain on unce...
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07/09/2020
Concept Bottleneck Models
We seek to learn models that we can interact with using high-level conce...
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05/09/2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
We study why overparameterization – increasing model size well beyond th...
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05/05/2020
ExpBERT: Representation Engineering with Natural Language Explanations
Suppose we want to specify the inductive bias that married couples typic...
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04/15/2020
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has com...
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11/20/2019
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Overparameterized neural networks can be highly accurate on average on a...
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09/14/2019
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations
Learning representations that accurately capture long-range dependencies...
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05/30/2019
On the Accuracy of Influence Functions for Measuring Group Effects
Influence functions estimate the effect of removing particular training ...
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11/02/2018
Stronger Data Poisoning Attacks Break Data Sanitization Defenses
Machine learning models trained on data from the outside world can be co...
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07/12/2018
Inferring Multi-Dimensional Rates of Aging from Cross-Sectional Data
Modeling how individuals evolve over time is a fundamental problem in th...
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03/14/2017