We study the differentially private convex stochastic optimization (DP-SCO) problem with heavy-tailed gradients, where we assume a kth<annotation encoding="application/x-tex">k^{\text{th}}kth-momentum limit ...
We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this scenario, there is north<annotation encoding="application/x-tex">northnorth ...
Atmospheric science and meteorology have recently advanced the modeling of local weather and climate phenomena by capturing fine-scale dynamics crucial ...
This article is also available in Spanish. bitcoin investors are still excited in light of a recent breakout towards $66,000. ...
Large language models (LLMs) have demonstrated impressive capabilities in in-context learning (ICL), a form of supervised learning that does not ...
Classical randomness has emerged as an important tool to address the challenge of designing quantum algorithms and protocols. Current methods ...
Sequential propagation of chaos (SPoC) is a recent technique for solving stochastic mean-field differential equations (SDEs) and their associated nonlinear ...
In a detailed analysis of bitcoin market trends, a recent video by well-known YouTuber Steve from crypto Crew University titled ...
This paper presents a novel generative modeling framework based on phase space dynamics, drawing inspiration from the principles underlying critically ...
We study differentially private convex stochastic optimization (DP-SCO) under user-level privacy, where each user can have multiple data items. Existing ...
