Search Results for author: Dyah Adila

Found 7 papers, 3 papers with code

Zero-Shot Robustification of Zero-Shot Models

1 code implementation8 Sep 2023 Dyah Adila, Changho Shin, Linrong Cai, Frederic Sala

Additionally, we demonstrate that RoboShot is compatible with a variety of pretrained and language models and propose a way to further boost performance with a zero-shot adaptation variant.

Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision

1 code implementation24 Mar 2022 Mayee F. Chen, Daniel Y. Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, Christopher Ré

Despite the black-box nature of foundation models, we prove results characterizing how our approach improves performance and show that lift scales with the smoothness of label distributions in embedding space.

Mitigating Source Bias for Fairer Weak Supervision

1 code implementation NeurIPS 2023 Changho Shin, Sonia Cromp, Dyah Adila, Frederic Sala

Weak supervision enables efficient development of training sets by reducing the need for ground truth labels.

counterfactual Fairness

Understanding Out-of-distribution: A Perspective of Data Dynamics

no code implementations NeurIPS Workshop ICBINB 2021 Dyah Adila, Dongyeop Kang

Despite machine learning models' success in Natural Language Processing (NLP) tasks, predictions from these models frequently fail on out-of-distribution (OOD) samples.

BIG-bench Machine Learning

AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels

no code implementations30 Aug 2022 Nicholas Roberts, Xintong Li, Tzu-Heng Huang, Dyah Adila, Spencer Schoenberg, Cheng-Yu Liu, Lauren Pick, Haotian Ma, Aws Albarghouthi, Frederic Sala

While it has been used successfully in many domains, weak supervision's application scope is limited by the difficulty of constructing labeling functions for domains with complex or high-dimensional features.

Benchmarking

Multimodal Data Curation via Object Detection and Filter Ensembles

no code implementations5 Jan 2024 Tzu-Heng Huang, Changho Shin, Sui Jiet Tay, Dyah Adila, Frederic Sala

We propose an approach for curating multimodal data that we used for our entry in the 2023 DataComp competition filtering track.

Object object-detection +2

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