Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2603.06957v2 Announce Type: replace Abstract: We study post-training linear autoregressive models with outcome and process rewards. Given a context $boldsymbol{x}$, the model must predict the response…
arXiv:2502.11665v3 Announce Type: replace Abstract: The classical kernel ridge regression problem aims to find the best fit for the output $Y$ as a function of the…
arXiv:2604.27742v2 Announce Type: replace-cross Abstract: A fundamental dichotomy in the theory of classification sets smoothness against statistical efficiency: smooth surrogate losses such as the logistic loss…
arXiv:2412.18081v3 Announce Type: replace Abstract: We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets. Such feature mismatch arises when some variables available…
arXiv:2608.12100v1 Announce Type: cross Abstract: In high-stakes applications, reliable confidence estimates are as important as the predictions themselves. Confidence calibration ensures that predicted probabilities reflect the…
arXiv:2608.11831v1 Announce Type: cross Abstract: Learning mappings between infinite-dimensional objects is a central challenge in scientific machine learning. We introduce a general kernel-based encoder-decoder framework for…
arXiv:2401.04013v3 Announce Type: replace-cross Abstract: Deep learning models, such as wide neural networks, can be conceptualized as nonlinear dynamical physical systems characterized by a multitude of…
arXiv:2608.11784v1 Announce Type: cross Abstract: Outcomes are increasingly regressed on a calibrated probability vector for unobserved class membership, and that vector is often coarsened to a…
arXiv:2608.11321v1 Announce Type: cross Abstract: We study spectral clustering in the presence of a confounding latent geometry. The leading eigenvectors may then be dominated by the…
arXiv:2608.11486v1 Announce Type: cross Abstract: We study the implementable finite-batch particle algorithm for mean-field variational inference as a fully discrete stochastic approximation of the projected Wasserstein…
arXiv:2608.11648v1 Announce Type: cross Abstract: We study the power of quantum examples, as compared to classical examples, in the PAC learning framework. Here, we have two…
arXiv:2608.11749v1 Announce Type: cross Abstract: Multi-objective optimization (MOO) has demonstrated significant success in multi-task learning by mitigating task conflicts through gradient manipulation. However, most existing methods…
arXiv:2608.11690v1 Announce Type: cross Abstract: Continual learning must absorb new tasks without erasing old ones, and replay---mixing a small buffer of past examples into current training---is…
arXiv:2510.17543v3 Announce Type: replace-cross Abstract: Edge intelligence enables low-latency inference via compact on-device models, but assuring reliability remains challenging. We study edge-cloud cascades that must preserve…
arXiv:2608.11760v1 Announce Type: cross Abstract: We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem. Despite extensive literature on the global convergence of SK and…
arXiv:2604.27733v2 Announce Type: replace-cross Abstract: Aligning Large Language Models (LLMs) with human intent, whether through explicit reward modeling or direct methods such as DPO, fundamentally relies…
arXiv:2602.17554v3 Announce Type: replace-cross Abstract: Training large-scale generative models is resource-intensive and relies heavily on heuristic dataset weighting. We address two fundamental questions: Can we train…
arXiv:2608.00316v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has become the standard tool for sample-efficient optimization and owes its efficiency to uncertainty-aware search driven by generic…
arXiv:2607.12501v3 Announce Type: replace-cross Abstract: The Forward-Forward algorithm trains each layer locally, so that a scalar goodness - the sum of squared activations - is high…
arXiv:2603.24304v3 Announce Type: replace Abstract: Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios. Under distribution…
arXiv:2608.11223v1 Announce Type: cross Abstract: Entry-only automatic fare collection systems record boardings but not alightings, preventing direct construction of origin-destination (OD) matrices. This study develops a…
arXiv:2608.11506v1 Announce Type: new Abstract: Adaptive behavior under partial observability depends on internal organization that carries information beyond the current observation. Drawing on Barrett and Miller's…
arXiv:2608.11865v1 Announce Type: new Abstract: Self-attention has become central to spiking vision transformers, yet its query-key scoring is still largely inherited from dense networks. Existing spiking…
arXiv:2608.11258v1 Announce Type: cross Abstract: Gradient injection helps Particle Swarm Optimization (PSO) only when the swarm has identified a basin with smooth local structure, not universally.…