Search Results for author: Jessica Hoffmann

Found 9 papers, 2 papers with code

Robust estimation of tree structured Gaussian Graphical Model

no code implementations25 Jan 2019 Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis

If we observe realizations of the variables, we can compute the covariance matrix, and it is well known that the support of the inverse covariance matrix corresponds to the edges of the graphical model.

Learning Graphs from Noisy Epidemic Cascades

no code implementations6 Mar 2019 Jessica Hoffmann, Constantine Caramanis

Finally, we give a polynomial time algorithm for learning the weights of general bounded-degree graphs in the limited-noise setting.

Learning Mixtures of Graphs from Epidemic Cascades

no code implementations ICML 2020 Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis

When the conditions are met, i. e., when the graphs are connected with at least three edges, we give an efficient algorithm for learning the weights of both graphs with optimal sample complexity (up to log factors).

Fairness for Image Generation with Uncertain Sensitive Attributes

1 code implementation23 Jun 2021 Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alexandros G. Dimakis, Eric Price

This motivates the introduction of definitions that allow algorithms to be \emph{oblivious} to the relevant groupings.

Fairness Image Generation +3

Towards Agile Text Classifiers for Everyone

no code implementations13 Feb 2023 Maximilian Mozes, Jessica Hoffmann, Katrin Tomanek, Muhamed Kouate, Nithum Thain, Ann Yuan, Tolga Bolukbasi, Lucas Dixon

Text-based safety classifiers are widely used for content moderation and increasingly to tune generative language model behavior - a topic of growing concern for the safety of digital assistants and chatbots.

Language Modelling text-classification +1

Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics

no code implementations13 Mar 2024 Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain, Erin Van Liemt, Kathleen Meier-Hellstern, Lucas Dixon

We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia's Neutral Point of View (NPOV) principle: acknowledge the absence of a single true answer and surface multiple perspectives.

Hallucination Retrieval +1

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