However, the underlying optimization principles and convergence of interactive teaching lack theoretical analysis, and in this regard co-teaching serves as a notable prototype.

Improving this capability requires high-quality vision-language data, which is costly and labor-intensive to acquire.

Work done during an internship at ByteDance San Jose, CA.

We propose a novel approach to sequential Bayesian inference based on variational Bayes (VB).

Local Bayesian optimization is a promising practical approach to solve high dimensional black-box function optimization problem. Among them is the approximated gradient class of methods, which implements a strategy similar to gradient descent. These methods have achieved good experimental results and theoretical guarantees.

Motivated by the strategic participation of electricity producers in electricity day-ahead market, we study the problem of online learning in repeated multi-unit uniform price auctions focusing on the adversarial opposing bid setting. The main contribution of this paper is the introduction of a new modeling of the bid space.

MDLM's objective correspond to a principled variational lower bound, and supports generation via