Among existing baselines, replay-based methods show competitive results but requires extra memory for storing exemplars, while exemplar-free (i.e., data need not be stored for replay in production) methods are resource-friendly but often lack accuracy.

We give sharper bounds for uniformly stable randomized algorithms in a PAC-Bayesian framework, which improve the existing results by up to a factor of n (ignoring a log factor), where n is the sample size. The key idea is to bound the moment generating function of the generalization gap using concentration of weakly dependent random variables due to Bousquet et al (2020). We introduce an assumption of sub-exponential stability parameter, which allows a general treatment that we instantiate in two applications: stochastic gradient descent and randomized coordinate descent. Our results eliminate the requirement of strong convexity from previous results, and hold for non-smooth convex problems.

Users Meta's A.I. Smart Glasses Are Wreaking Havoc in Schools Across the Country. It's Only Going to Get Worse. As the discreet wearable cameras become more popular, students are saying they feel constantly watched and harassed--and professors are reshaping their classrooms in response. Joziah was tabling on campus for his peer mentor job at the end of last semester at Florida State University when he noticed something strange happening across the quad: A trio of men, wearing Meta AI glasses, were stopping every young woman who passed by and asking them for their social media contacts. "I recognized them from TikTok, because they're kind of big, especially in Miami," the 19-year-old told me.

We find that the ability of LLMs to maintain consistency varies across different moral scenarios.

Surprisingly, the analysis is short and elegant.

The framework of feedback graphs is a generalizationof sequential decisionmaking with bandit or full information feedback. In this work, we study an extension where the directed feedback graph is stochastic, following a distribution similar to the classical Erdős-Rényi model. Specifically, in each round every edge in the graph is either realized or not with a distinct probability for each edge.