Abstract: This article examines an online distributed optimization problem over an unbalanced digraph, in which a group of nodes in the network tries to collectively search for a minimizer of a ...

Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...

We consider the problem of fitting a reinforcement learning (RL) model to some given behavioral data under a multi-armed bandit environment. These models have received much attention in recent years ...

As Elon Musk previously announced, X has just published the latest version of its recommendation algorithm, Phoenix (source: https://github.com/xai-org/x-algorithm ...

🔥News: A PyTorch version of this package can be found in ULTRA_pytorch. This is an Unbiased Learning To Rank Algorithms (ULTRA) toolbox, which provides a codebase for experiments and research on ...

In a world rife with content overload and short attention spans, personalization is the antidote to fragmentation. From over 300 million Netflix users, to several hundred billion transactions on ...

Understanding how and why humans and other agents persist in repeating past choices—even when these lead to negative outcomes —has intrigued scientists across fields such as neuroscience, behavioral ...

In multi-agent systems 1, multiple agents aim to optimize their individual objectives, interacting with the others through these objective functions. Cooperative multi-agent systems 1,2 aim to ...

We propose a simple and nonparametric solution to this problem, Automatic Prompt Optimization (APO), which is inspired by numerical gradient descent to automatically improve prompts, assuming access ...