1 code implementation • EMNLP (MRQA) 2021 • Andrew Mao, Naveen Raman, Matthew Shu, Eric Li, Franklin Yang, Jordan Boyd-Graber
We develop two sets of questions for closed and open domain questions respectively, which use ambiguous questions to probe QA models for bias.
no code implementations • 2 Jan 2024 • Naveen Raman, Mateo Espinosa Zarlenga, Juyeon Heo, Mateja Jamnik
Deep learning models trained under this paradigm heavily rely on the assumption that neural networks can learn to predict the presence or absence of a given concept independently of other concepts.
no code implementations • 22 Mar 2023 • Katherine M. Collins, Matthew Barker, Mateo Espinosa Zarlenga, Naveen Raman, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, Krishnamurthy Dvijotham
We study how existing concept-based models deal with uncertain interventions from humans using two novel datasets: UMNIST, a visual dataset with controlled simulated uncertainty based on the MNIST dataset, and CUB-S, a relabeling of the popular CUB concept dataset with rich, densely-annotated soft labels from humans.
no code implementations • 18 Dec 2021 • Naveen Raman, Michael Yee
We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets.
1 code implementation • 7 Oct 2021 • Naveen Raman, Sanket Shah, John Dickerson
Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers.