Search Results for author: Mayee F. Chen

Found 9 papers, 8 papers with code

Resonant Anomaly Detection with Multiple Reference Datasets

no code implementations20 Dec 2022 Mayee F. Chen, Benjamin Nachman, Frederic Sala

An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal.

Anomaly Detection

Ask Me Anything: A simple strategy for prompting language models

3 code implementations5 Oct 2022 Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, Christopher Ré

Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task.

Coreference Resolution Natural Language Inference +2

TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

1 code implementation Findings (ACL) 2022 Megan Leszczynski, Daniel Y. Fu, Mayee F. Chen, Christopher Ré

Entity retrieval--retrieving information about entity mentions in a query--is a key step in open-domain tasks, such as question answering or fact checking.

Entity Retrieval Fact Checking +3

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.

Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation

1 code implementation3 Mar 2021 Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré

We apply our decomposition framework to three scenarios -- well-specified, misspecified, and corrected models -- to 1) choose between labeled and unlabeled data and 2) learn from their combination.

Network disruption: maximizing disagreement and polarization in social networks

2 code implementations18 Mar 2020 Mayee F. Chen, Miklos Z. Racz

Motivated by this reality, we introduce a simple model of network disruption where an adversary can take over a limited number of user profiles in a social network with the aim of maximizing disagreement and/or polarization in the network.

Social and Information Networks Data Structures and Algorithms Computer Science and Game Theory Physics and Society

Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

1 code implementation ICML 2020 Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré

In this work, we show that, for a class of latent variable models highly applicable to weak supervision, we can find a closed-form solution to model parameters, obviating the need for iterative solutions like stochastic gradient descent (SGD).

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