arXiv:2505.24843v2 Announce Type: replace Abstract: Models that learn spurious correlations from training data often fail when deployed in new environments. While many methods aim to learn invariant representations to address this, they often underperform standard empirical risk minimization (ERM). We…
arXiv:2511.05591v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy by keeping raw data local. However, FL suffers from significant communication overhead due to the frequent exchange of high-dimensional model updates…
arXiv:2509.08625v2 Announce Type: replace Abstract: The silhouette coefficient quantifies, for each observation, the balance between within-cluster cohesion and between-cluster separation, taking values in [-1, 1]. The average silhouette width (ASW) is a widely used internal measure of clustering quality, with…
arXiv:2511.05592v1 Announce Type: new Abstract: Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph tasks and domains. However, existing GFMs struggle with unstable few-shot…
arXiv:2511.03239v2 Announce Type: replace Abstract: Modern AI systems are increasingly constrained not by model capacity but by the quality and diversity of their data. Despite growing emphasis on data-centric AI, most datasets are still gathered in an open-loop manner which…
arXiv:2511.05593v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized model training across multiple clients while optionally preserving data privacy. However, communication efficiency remains a critical bottleneck, particularly for large-scale models. In this work, we introduce two complementary algorithms: ProjFL,…
arXiv:2502.05390v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL), where models adapt to new tasks through example-based prompts without requiring parameter updates. However, understanding how tasks are internally encoded and generalized remains…
arXiv:2510.17486v2 Announce Type: replace Abstract: We introduce a methodology for analyzing neural networks through the lens of layer-wise Hessian matrices. The local Hessian of each functional block (layer) is defined as the matrix of second derivatives of a scalar function…
arXiv:2407.10807v2 Announce Type: replace-cross Abstract: Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area. Due to the temporal constraints, it…
arXiv:2504.00467v2 Announce Type: replace Abstract: This paper presents the Min-Cut Bayesian Network Consensus (MCBNC) algorithm, a greedy method for structural consensus of Bayesian Networks (BNs), with applications in federated learning and model aggregation. MCBNC prunes weak edges from an initial…
