言栖资讯收录 190355 篇 AI、开发与建站文章，按发布时间排序，支持搜索与按语言筛选。
arXiv:2606.13796v2 Announce Type: replace Abstract: Recursive training of generative models on their own outputs can lead to model collapse, a compounding drift…
arXiv:2605.08866v2 Announce Type: replace Abstract: Inverse optimization (IO) seeks to infer the parameters of a decision-maker's objective from observed contex…
arXiv:2602.03168v2 Announce Type: replace Abstract: Online conformal prediction (OCP) seeks prediction intervals that achieve long-run $1-\alpha$ coverage for a…
arXiv:2511.17783v2 Announce Type: replace Abstract: Node popularity is recognized as a key factor in modeling real-world networks, capturing heterogeneity in co…
arXiv:2509.14039v2 Announce Type: replace Abstract: In this paper, we consider the problem of Gaussian approximation for the online linear regression task. We d…
arXiv:2611.08414v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) is commonly used in both supervised fine-tuning (SFT) and reinforcement learning (R…
arXiv:2611.08409v1 Announce Type: cross Abstract: Uncertainty quantification is crucial in scientific machine learning, where models inform safety-critical task…
arXiv:2611.08182v1 Announce Type: cross Abstract: We identify an intriguing phenomenon when linearly interpolating between permutation-aligned neural networks: …
arXiv:2611.08077v1 Announce Type: cross Abstract: We propose a doubly-robust method to estimate the causal effects of multi-valued treatment regimes on patient …
arXiv:2611.07773v1 Announce Type: cross Abstract: We study learning in zero-sum games with delayed feedback. We introduce Multi-Anchor Optimistic Online Mirror …
arXiv:2611.07536v1 Announce Type: cross Abstract: The joint expectation of functions of potential outcomes $(Y_1,Y_0)$ is a fundamental quantity in causal infer…
arXiv:2611.07399v1 Announce Type: cross Abstract: We study distribution-free uncertainty quantification for a changepoint estimate in an offline setting. We obs…
arXiv:2611.07334v1 Announce Type: cross Abstract: Last-iterate convergence guarantees for stochastic gradient methods have attracted significant attention as pr…
arXiv:2611.07287v1 Announce Type: cross Abstract: High-frequency volatility forecasting typically separates measurement from prediction: intraday observations a…
arXiv:2611.07270v1 Announce Type: cross Abstract: Animals and humans often do not maximize; they act relative to goals (aspiration levels) that are frequently n…
arXiv:2611.07249v1 Announce Type: cross Abstract: This paper examines robustness and causal interpretation of panel estimates using double/debiased machine lear…
arXiv:2611.08358v1 Announce Type: new Abstract: At the edge of stability, the iterates of gradient descent oscillate along the sharp directions of the Hessian. …
arXiv:2611.08252v1 Announce Type: new Abstract: We consider the problem of cross-validation (CV) in time series forecasting, with a focus on global forecasting …
arXiv:2611.08160v1 Announce Type: new Abstract: Attention-based time series forecasters commonly group consecutive observations into patches. Patch length is kn…
arXiv:2611.08113v1 Announce Type: new Abstract: State-space models (SSMs) recover low-dimensional latent dynamics from high-dimensional observations, but latent…