arXiv:2511.07472v1 Announce Type: new Abstract: We present the Multivariate Variational Autoencoder (MVAE), a VAE variant that preserves Gaussian tractability while lifting the diagonal posterior restriction. MVAE factorizes each posterior covariance, where a emph{global} coupling matrix $mathbf{C}$ induces dataset-wide latent correlations…
arXiv:2511.07471v1 Announce Type: new Abstract: Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum federated learning (QFL) overcomes these concerns…
arXiv:2506.03237v3 Announce Type: replace-cross Abstract: The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and evaluation metrics are confronted with several key challenges: (1)…
arXiv:2511.07486v1 Announce Type: new Abstract: We study the problem of learning policies that maximize cumulative reward while satisfying safety constraints, even when the real environment differs from a simulator or nominal model. We focus on robust constrained Markov decision processes…
arXiv:2511.07500v1 Announce Type: new Abstract: The integration of advanced analytical tools, including Machine Learning (ML) and massive data processing, has revolutionized health research, promising unprecedented accuracy in diagnosis and risk prediction. However, the rigor of these complex methods is fundamentally…
arXiv:2511.08401v1 Announce Type: cross Abstract: We prove a fundamental misalignment in transfer learning: the source regularization that minimizes source risk almost never coincides with the regularization maximizing transfer benefit. Through sharp phase boundaries for L2-SP ridge regression, we characterize the…
arXiv:2511.07559v1 Announce Type: new Abstract: Activation functions are fundamental for enabling nonlinear representations in deep neural networks. However, the standard rectified linear unit (ReLU) often suffers from inactive or “dead” neurons caused by its hard zero cutoff. To address this…
arXiv:2305.17327v3 Announce Type: replace Abstract: Imperfect Information Games (IIGs) offer robust models for scenarios where decision-makers face uncertainty or lack complete information. Counterfactual Regret Minimization (CFR) has been one of the most successful family of algorithms for tackling IIGs. The…
arXiv:2511.07572v1 Announce Type: new Abstract: Mechanistic interpretability aims to decompose neural networks into interpretable features and map their connecting circuits. The standard approach trains sparse autoencoders (SAEs) on each layer’s activations. However, SAEs trained in isolation don’t encourage sparse cross-layer…
arXiv:2505.01386v4 Announce Type: replace Abstract: Machine learning solutions are rapidly adopted to enable a variety of key use cases, from conversational AI assistants to scientific discovery. This growing adoption is expected to increase the associated lifecycle carbon footprint, including both…
