arXiv:2512.02214v1 Announce Type: new Abstract: We study the problem of online model selection in reinforcement learning, where the selector has access to a class of reinforcement learning agents and learns to adaptively select the agent with the right configuration. Our…
arXiv:2506.14987v3 Announce Type: replace-cross Abstract: Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a CNN-based dynamic priority prediction mechanism for improved interference…
arXiv:2512.02217v1 Announce Type: new Abstract: Artificial intelligence (AI) systems increasingly influence safety-critical aspects of society, from medical diagnosis to autonomous mobility, making uncertainty awareness a central requirement for trustworthy AI. We present a photonic Bayesian machine that leverages the inherent…
arXiv:2511.06040v4 Announce Type: replace-cross Abstract: We study the computational task of detecting and estimating correlated signals in a pair of spiked matrices $$ X=tfrac{lambda}{sqrt{n}} xu^{top}+W, quad Y=tfrac{mu}{sqrt{n}} yv^{top}+Z $$ where the spikes $x,y$ have correlation $rho$. Specifically, we consider two…
arXiv:2512.02223v1 Announce Type: new Abstract: Inferring the phylogenetic relationships among a sample of organisms is a fundamental problem in modern biology. While distance-based hierarchical clustering algorithms achieved early success on this task, these have been supplanted by Bayesian and maximum…
arXiv:2512.03025v1 Announce Type: cross Abstract: Achievement. We introduce LORE, a systematic framework for Large Generative Model-based relevance in e-commerce search. Deployed and iterated over three years, LORE achieves a cumulative +27% improvement in online GoodRate metrics. This report shares the…
arXiv:2512.02265v1 Announce Type: new Abstract: Trustworthy machine learning in healthcare requires strong predictive performance, fairness, and explanations. While it is known that improving fairness can affect predictive performance, little is known about how fairness improvements influence explainability, an essential ingredient…
arXiv:2406.12693v3 Announce Type: replace Abstract: Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal patterns in recent years. However, current public datasets have limitations in reflecting the…
arXiv:2512.02279v1 Announce Type: new Abstract: Membership queries (MQ) often yield speedups for learning tasks, particularly in the distribution-specific setting. We show that in the emph{testable learning} model of Rubinfeld and Vasilyan [RV23], membership queries cannot decrease the time complexity of…
arXiv:2502.05684v3 Announce Type: replace Abstract: How can we effectively remove or ”unlearn” undesirable information, such as specific features or the influence of individual data points, from a learning outcome while minimizing utility loss and ensuring rigorous guarantees? We introduce a…
