arXiv:2510.26723v2 Announce Type: replace-cross Abstract: The goal of policy learning is to train a policy function that recommends a treatment given covariates to maximize population welfare. There are two major approaches in policy learning: the empirical welfare maximization (EWM) approach…
arXiv:2502.08436v2 Announce Type: replace-cross Abstract: We present Label Space Reduction (LSR), a novel method for improving zero-shot classification performance of Large Language Models (LLMs). LSR iteratively refines the classification label space by systematically ranking and reducing candidate classes, enabling the…
arXiv:2506.20334v2 Announce Type: replace-cross Abstract: This paper investigates the design of output-feedback schemes for systems described by a class of recurrent neural networks. We propose a procedure based on linear matrix inequalities for designing an observer and a static state-feedback…
arXiv:2509.16875v3 Announce Type: replace Abstract: Attention mechanisms have achieved significant empirical success in multiple fields, but their underlying optimization objectives remain unclear yet. Moreover, the quadratic complexity of self-attention has become increasingly prohibitive. Although interpretability and efficiency are two mutually…
arXiv:2511.00804v2 Announce Type: replace Abstract: Erasing harmful or proprietary concepts from powerful text to image generators is an emerging safety requirement, yet current “concept erasure” techniques either collapse image quality, rely on brittle adversarial losses, or demand prohibitive retraining cycles.…
arXiv:2503.05622v3 Announce Type: replace Abstract: Optimal allocation of scarce resources is a common problem for decision makers faced with choosing a limited number of locations for intervention. Spatiotemporal prediction models could make such decisions data-driven. A recent performance metric called…
arXiv:2506.23210v3 Announce Type: replace Abstract: Federated learning (FL) collaboratively trains artificial intelligence (AI) models to ensure user data privacy. Sharing only model updates generated from local training on client data with the server enhances user data privacy. However, model performance…
arXiv:2511.02957v1 Announce Type: new Abstract: Pavement infrastructure monitoring is challenged by complex spatial dependencies, changing environmental conditions, and non-linear deterioration across road networks. Traditional Pavement Management Systems (PMS) remain largely reactive, lacking real-time intelligence for failure prevention and optimal maintenance…
arXiv:2511.02966v1 Announce Type: new Abstract: We study the problem of aligning a generative model’s response with a user’s preferences. Recent works have proposed several different formulations for personalized alignment; however, they either require a large amount of user preference queries…
arXiv:2511.02936v1 Announce Type: new Abstract: Efforts have increased in recent years to identify associations between specific datasets and the scientific literature that incorporates them. Knowing that a given publication cites a given dataset, the next logical step is to explore…
