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Related Articles from SNS
GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework
arXiv:2504.17471v2 Announce Type: replace Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers. Recent approaches rely on dynamic communication graphs built using Random Peer Sampling (RPS) protocols which have been proven to accelerate convergence. However, we show that these approaches are vulnerable to a dual attack: Byzantine nodes can poison models and manipulate peer sampling to...
Robustly estimating heterogeneity in factorial data using Rashomon Partitions
arXiv:2404.02141v5 Announce Type: replace-cross Abstract: In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest varies with combinations of observable covariates. Choosing a model that is too simple can obfuscate important heterogeneity in outcomes between covariate groups, while too much complexity risks identifying spurious patterns. In this paper, we propose a novel Bayesian framework for model uncertainty...
From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory
Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in long-running settings where improving through experience at test time becomes important. A common approach is to update an explicit memory after each interaction to guide future decisions. However, most existing methods rely on hand-designed prompting rules, making it difficult to align memory updates with downstream objectives over multi-step horizons consistently.