1 code implementation • 23 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.
1 code implementation • 8 May 2020 • Moein Khajehnejad, Ahmad Asgharian Rezaei, Mahmoudreza Babaei, Jessica Hoffmann, Mahdi Jalili, Adrian Weller
We believe we are the first to use embeddings for the task of fair influence maximization.
no code implementations • 25 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.
no code implementations • 6 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.
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).
no code implementations • 13 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.
no code implementations • 5 Feb 2024 • Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
Aligning language models with human preferences is crucial for reducing errors and biases in these models.
no code implementations • 13 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.
no code implementations • 15 Mar 2024 • Hakim Sidahmed, Samrat Phatale, Alex Hutcheson, Zhuonan Lin, Zhang Chen, Zac Yu, Jarvis Jin, Roman Komarytsia, Christiane Ahlheim, Yonghao Zhu, Simral Chaudhary, Bowen Li, Saravanan Ganesh, Bill Byrne, Jessica Hoffmann, Hassan Mansoor, Wei Li, Abhinav Rastogi, Lucas Dixon
We investigate the setup of "Parameter Efficient Reinforcement Learning" (PERL), in which we perform reward model training and reinforcement learning using LoRA.