arXiv:2601.10015v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) enables collaboratively model training on decentralized, heterogeneous data while tailoring them to each client’s unique distribution. However, existing PFL methods produce static models with a fixed tradeoff between accuracy and efficiency,…
arXiv:2504.03716v2 Announce Type: replace Abstract: Medical institutions are considering the use of LLMs in high-stakes clinical decision-making, such as organ allocation. In such sensitive use cases, evaluating fairness is imperative. However, existing evaluation methods often fall short; benchmarks are too…
arXiv:2601.10019v1 Announce Type: new Abstract: This paper studies time aggregation features for XGBoost models in click-through rate prediction. The setting is the Avazu click-through rate prediction dataset with strict out-of-time splits and a no-lookahead feature constraint. Features for hour H…
arXiv:2506.05310v3 Announce Type: replace Abstract: Learning probability models from data is at the heart of many machine learning endeavors, but is notoriously difficult due to the curse of dimensionality. We introduce a new framework for learning emph{normalized} energy (log probability)…
arXiv:2601.10024v1 Announce Type: new Abstract: Ensemble learning is widely recognized as a pivotal strategy for pushing the boundaries of predictive performance. Traditional static ensemble methods, such as Stacking, typically assign weights by treating each base learner as a holistic entity,…
arXiv:2510.01755v2 Announce Type: replace Abstract: In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insights. The proposed methods differ in their architectural design and training…
arXiv:2601.10058v1 Announce Type: new Abstract: Large language models (LLMs) exhibit impressive in-context learning (ICL) capabilities, yet the quality of their predictions is fundamentally limited by the few costly labeled demonstrations that can fit into a prompt. Meanwhile, there exist vast…
Delia Wendel’s new book illuminates a painful and painstaking effort by citizens to bear witness to atrocities.
arXiv:2410.03055v3 Announce Type: replace Abstract: Large Language Models (LLMs) are rapidly becoming commodity components of larger software systems. This poses natural security and privacy problems: poisoned data retrieved from one component can change the model’s behavior and compromise the entire…
arXiv:2601.04377v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most existing RAG strategies treat retrieved passages in a flat and unstructured way,…
