Tootfinder

Guru Puppy - I'm Learning From My Dog
#BeBetter
https://muz4now.com/2022/guru-puppy-im-learning-from-my-dog/

Mapping Student-AI Interaction Dynamics in Multi-Agent Learning Environments: Supporting Personalised Learning and Reducing Performance Gaps
Zhanxin Hao, Jie Cao, Ruimiao Li, Jifan Yu, Zhiyuan Liu, Yu Zhang
https://arxiv.org/abs/2506.02993

Mapping Student-AI Interaction Dynamics in Multi-Agent Learning Environments: Supporting Personalised Learning and Reducing Performance Gaps
Multi-agent AI systems, which simulate diverse instructional roles such as teachers and peers, offer new possibilities for personalized and interactive learning. Yet, student-AI interaction patterns and their pedagogical implications remain unclear. This study explores how university students engaged with multiple AI agents, and how these interactions influenced cognitive outcomes (learning gains) and non-cognitive factors (motivation, technology acceptance). Based on MAIC, an online learning p…

Object-centric 3D Motion Field for Robot Learning from Human Videos
Zhao-Heng Yin, Sherry Yang, Pieter Abbeel
https://arxiv.org/abs/2506.04227 https://

Object-centric 3D Motion Field for Robot Learning from Human Videos
Learning robot control policies from human videos is a promising direction for scaling up robot learning. However, how to extract action knowledge (or action representations) from videos for policy learning remains a key challenge. Existing action representations such as video frames, pixelflow, and pointcloud flow have inherent limitations such as modeling complexity or loss of information. In this paper, we propose to use object-centric 3D motion field to represent actions for robot learning …

Heute, jetzt gleich demnächst: Abschlussworkshop zum @… Projekt „Potentials of Advanced Text Technologies. Auf maschinellem Lernen basierende Texterkennung (PATT)“ in der Hochschulbibliothek der #ZHAW
Mit dabei: meine

Is Cooper DeJean Eagles' answer at safety? Why CB learning a new position isn't a permanent move

https://www.cbssports.com/nfl/news/is-coop

Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
Gabriele Digregorio, Francesco Bleggi, Federico Caroli, Michele Carminati, Stefano Zanero, Stefano Longari
https://arxiv.org/abs/2506.02679

Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency offered by blockchain-based systems. While these approaches are promising, they often lack flexible tools to evaluate system robustness under adversarial conditions. To fill this gap, we present FedBlockParadox, a modular framework for modeling and evaluating decentralized federated learning systems built on blockcha…

Another of my forays into AI ethics is just out! This time the focus is on the ethics (or lack thereof) of Reinforcement Learning Feedback (RLF) techniques aimed at increasing the 'alignment' of LLMs.
The paper is fruit of the joint work of a great team of collaborators, among whom @… and @…

Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback - Ethics and Information Technology
This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical critique, we examine both the theoretical underpinni…

Raiders coordinator Patrick Graham embraces learning from Pete Carroll's defensive expertise https://www.foxsports.com/articles/nfl/raiders-coordinator-patrick-graham-embraces-learning-from-pete-carro…

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Xiyu Zhao, Qimei Cui, Ziqiang Du, Weicai Li, Xi Yu, Wei Ni, Ji Zhang, Xiaofeng Tao, Ping Zhang
https://arxiv.org/abs/2506.02422

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is another challenge resulting from communication bottlenecks in WPFL. This paper exploits quantization errors to enhance the privacy of WPFL and proposes a novel quantization-assisted Gaussian differ…

This https://arxiv.org/abs/2503.10503 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Sample Compression for Continual Learning
Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existing continual learning approaches in the literature rely on heuristics and do not provide learning guarantees. In this paper, we present a new method called Continual Pick-to-Learn (CoP2L), which is able to retain the most representative samples for each task in an efficient way. CoP2L combines the Pick-to-Learn algorithm (rooted in the sample compression th…