565 citations · 1.2k across the 20 of their papers we have counts for
20 papers
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…
STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map
Ammar Sherif, Abubakar Abid, Mustafa Elattar +1
Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often c…
AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages
Chris Chinenye Emezue, Sanchit Gandhi, Lewis Tunstall +10
The advancement of speech technologies has been remarkable, yet its integration with African languages remains limited due to the scarcity of African speech corpora. To address thi…
Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
Kailas Vodrahalli, Justin Ko, Albert S. Chiou +7
Telemedicine utilization was accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, the quality of photographs sent by patients remains a ma…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Clustering Plotted Data by Image Segmentation
Tarek Naous, Srinjay Sarkar, Abubakar Abid +1
Clustering algorithms are one of the main analytical methods to detect patterns in unlabeled data. Existing clustering methods typically treat samples in a dataset as points in a m…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations
Abubakar Abid, Mert Yuksekgonul, James Zou
Understanding and explaining the mistakes made by trained models is critical to many machine learning objectives, such as improving robustness, addressing concept drift, and mitiga…
Persistent Anti-Muslim Bias in Large Language Models
Abubakar Abid, Maheen Farooqi, James Zou
It has been observed that large-scale language models capture undesirable societal biases, e.g. relating to race and gender; yet religious bias has been relatively unexplored. We d…
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
Kexin Huang, Tianfan Fu, Dawood Khan +7
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable progress in predicting drug ef…
Improving Training on Noisy Stuctured Labels
Abubakar Abid, James Zou
Fine-grained annotations---e.g. dense image labels, image segmentation and text tagging---are useful in many ML applications but they are labor-intensive to generate. Moreover ther…
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Abubakar Abid, Ali Abdalla, Ali Abid +3
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to valida…
Contrastive Variational Autoencoder Enhances Salient Features
Abubakar Abid, James Zou
Variational autoencoders are powerful algorithms for identifying dominant latent structure in a single dataset. In many applications, however, we are interested in modeling latent…
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction
Abubakar Abid, Muhammad Fatih Balin, James Zou
We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and…
Contrastive Multivariate Singular Spectrum Analysis
Abdi-Hakin Dirie, Abubakar Abid, James Zou
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data. By utilizin…
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
Abubakar Abid, James Zou
Measuring similarities between unlabeled time series trajectories is an important problem in domains as diverse as medicine, astronomy, finance, and computer vision. It is often un…
Stochastic EM for Shuffled Linear Regression
Abubakar Abid, James Zou
We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known. Such datasets naturally arise…
Interpretation of Neural Networks is Fragile
Amirata Ghorbani, Abubakar Abid, James Zou
In order for machine learning to be deployed and trusted in many applications, it is crucial to be able to reliably explain why the machine learning algorithm makes certain predict…
Contrastive Principal Component Analysis
Abubakar Abid, Martin J. Zhang, Vivek K. Bagaria +1
We present a new technique called contrastive principal component analysis (cPCA) that is designed to discover low-dimensional structure that is unique to a dataset, or enriched in…
Linear Regression with Shuffled Labels
Abubakar Abid, Ada Poon, James Zou
Is it possible to perform linear regression on datasets whose labels are shuffled with respect to the inputs? We explore this question by proposing several estimators that recover…