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Join me for a FREE Webinar: Training Tips that Work for Nonprofits on Jan.29th I’ll be sharing my best tips and secrets for designing and delivering training for nonprofit professionals that get results. 29th at 1:00 PM EST/10:00 AM PST. I use a simple structure to design: before, during, and after.
Published on February 20, 2025 11:54 PM GMT TLDR: We made substantial progress in 2024: We published a series of papers that verify key predictions of Singular LearningTheory (SLT) [ 1 , 2 , 3 , 4 , 5 , 6 ]. The S4 correspondence: Training data (and architecture) determine the loss landscape. in funding for 2025.
Kochenderfer Contact : philhc@stanford.edu Links: Paper Keywords : deep learning or neural networks, sparsity and feature selection, variational inference, (application) natural language and text processing Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss Authors : Jeff Z.
*Backdoors and other alignment stress tests: Past research has implanted backdoors in safety-trained LLMs and tested whether standard alignment techniques are capable of catching or removing them. Were interested in techniques like latent adversarial training and circuit breaking that might succeed where standard adversarial training falters.
A relevant question is now how these reward learning algorithms will behave, if they are applied to data which violates their underlying assumptions (in some specific way). For example, what happens if an IRL algorithm which assumes that the demonstrator policy discounts exponentially is shown data from an agent that discounts hyperbolically?
Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems ( NeurIPS 2022 ), the biggest machine learning conference of the year.
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