arXiv:2512.04333v1 Announce Type: new Abstract: Early detection of cancer plays a key role in improving survival rates, but identifying reliable biomarkers from RNA-seq data is still a major challenge. The data are high-dimensional, and conventional statistical methods often fail to…
arXiv:2502.15522v3 Announce Type: replace Abstract: Machine learning methods are commonly used to solve inverse problems, wherein an unknown signal must be estimated from few indirect measurements generated via a known acquisition procedure. In particular, neural networks perform well empirically but…
arXiv:2512.04341v1 Announce Type: new Abstract: Popular offline reinforcement learning (RL) methods rely on conservatism, either by penalizing out-of-dataset actions or by restricting planning horizons. In this work, we question the universality of this principle and instead revisit a complementary one:…
arXiv:2506.09508v2 Announce Type: replace Abstract: We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to…
arXiv:2512.04351v1 Announce Type: new Abstract: Detecting when large language models (LLMs) are uncertain is critical for building reliable systems, yet existing methods are overly complicated, relying on brittle semantic clustering or internal states. We introduce textbf{Radial Dispersion Score (RDS)}, a…
arXiv:2509.04734v2 Announce Type: replace Abstract: The Information Contrastive (I-Con) framework revealed that over 23 representation learning methods implicitly minimize KL divergence between data and learned distributions that encode similarities between data points. However, a KL-based loss may be misaligned with…
arXiv:2512.04354v1 Announce Type: new Abstract: Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts…
arXiv:2510.25121v2 Announce Type: replace Abstract: Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial…
arXiv:2512.04385v1 Announce Type: new Abstract: Fine-grained air pollution forecasting is crucial for urban management and the development of healthy buildings. Deploying portable sensors on mobile platforms such as cars and buses offers a low-cost, easy-to-maintain, and wide-coverage data collection solution.…
arXiv:2511.15371v2 Announce Type: replace Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model’s decision-making process. While numerous methods exist, the lack of a definitive ground truth for comparison highlights the need for alternative,…
