On the other hand, a learning algorithm biasing towards old tasks will interfere with the learning of the new task. Several lines of methods are proposed recently to address this issue.

A person holds a fake $1,000 bill signed by former Democratic presidential candidate Andrew Yang following a campaign event in Iowa City, Iowa, on 29 January 2020. A person holds a fake $1,000 bill signed by former Democratic presidential candidate Andrew Yang following a campaign event in Iowa City, Iowa, on 29 January 2020. Why universal basic income still can't meet the challenges of an AI economy Andrew Yang's revived pitch suits the automation debate, but UBI can't fix inequalities concentrated tech wealth drives Universal basic income (UBI) is back, like a space zombie in a sci-fi movie, resurrected from policy oblivion, hungry for policymakers' attention: brains! Andrew Yang, whose "Yang Gang" enthusiasm briefly shook up the Democratic presidential nomination in 2020 promoting a "Freedom Dividend" to save workers from automation - $1,000 a month for every American adult - is again the main carrier of the bug: offering UBI to save the nation when robots eat all our jobs. This time Chat GPT, Yang hopes, will help his argument land: if artificial intelligence truly makes human labor redundant, as so many citizens of the tech bubble in Silicon Valley expect, society will need something other than employment for all of us to make ends meet.

Machine Learning-assisted directed evolution (MLDE) is a powerful tool for efficiently navigating antibody fitness landscapes. Many structure-aware MLDE pipelines rely on a single conformation or a single committee across all conformations, limiting their ability to separate conformational uncertainty from epistemic uncertainty. Here, we introduce a rank -conditioned committee (RCC) framework that leverages ranked conformations to assign a deep neural network committee per rank. This design enables a principled separation between epistemic uncertainty and conformational uncertainty. We validate our RCC-MLDE approach on SARS-CoV-2 antibody docking, demonstrating significant improvements over baseline strategies. Our results offer a scalable route for therapeutic antibody discovery while directly addressing the challenge of modeling conformational uncertainty.

In particular, it remains an open question how to quantify the improvement in ADMM's theoretical convergence by using adaptive penalty parameters. Of course, the answer to this question depends on the adaptive scheme being used.

The HOPS algorithm employs Nesterov's smoothing

However, the independent sampling for dropout could be suboptimal for the sake of convergence. In this paper, we propose to use multinomial sampling for dropout, i.e.,

Recent studies have shown that proximal gradient (PG) method and accelerated gradient method (APG) with restarting can enjoy a linear convergence under a weaker condition than strong convexity, namely a quadratic growth condition (QGC). However, the faster convergence of restarting APG method relies on the potentially unknown constant in QGC to appropriately restart APG, which restricts its applicability.

A common practice for solving non-strongly convex finite-sum problems by SVRG is to add a small strongly convex regularizer (e.g.,