Modern conditional generative models face significant challenges when learning complex covariate dependencies. While sufficient dimension reduction (SDR) provides a principled approach to compress these dependencies, traditional SDR frameworks were not formulated for conditional generation. To bridge this gap, we propo...
W. Tan, Bing Li, Ling-Zhou Xue· arXiv.org· 0 citations
Decoupled Causal Discovery is proposed, a novel decoupling-based perspective that does not rely on conditional independence tests, structure scores, or restrictive functional assumptions, and establishes the theoretical identifiability, soundness, and completeness of DCD.
It is proved that the symmetric part of a bilinear form in an attention head is the sum of a hyperbolic form and a zero form for a Zariski-dense subset of query-key matrices.
We develop an index policy for finite-horizon Bernoulli multi-armed bandits from minimax solutions to single-arm bandit (SAB) problems. Each SAB problem involves choosing between an unknown Bernoulli arm and a known reward. We show that minimizing worst-case regret of SAB problems over all non-anticipative policies adm...
Hui-Kang Liu, Zheng-Chao Wang, Daniel Kuhn et al.· 0 citations
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Across three jurisdictions and more than 35,000 wells, under a shared-space, validation-frozen protocol, Physics-SIMS-TS is the most accurate diffusion forecaster in the comparison and is competitive with, but not superior to, ensembled transformer forecasters.
This work describes the distribution-free minimax rate of excess abstention risk, up to logarithmic factors, in terms of the complexity of the hypothesis class and the sample size and introduces surrogate-loss formulations to make the framework amenable to computation with models such as neural networks.
Mohammadreza M. Kalan, Yu-Yang Deng, Sanaz Hamidi· 1 citation
The purpose of this paper is to rigorously quantify the statistical and learning-theoretic properties of adversarial training models for classification. Equivalently, we establish the statistical properties of empirical optimal partial transport. Precisely, first we provide two types of central limit theorems (CLT): CL...
An AI-Oriented Comprehensive Normalized Assessment for healthy status is proposed and applied to optimize RRT strategies by using offline reinforcement learning (RL), demonstrating both methodological innovation and the potential of CNA-guided RL to improve RRT outcomes in nephrology.
Jiang Liu, Chan Zhou, Yujie Li et al.· Expert systems with applicat...· 0 citations
Global In-situ Observation (GIO) provides fine-scale, direct records of the global weather system from sparse point stations, making it an indispensable source for capturing localized and transient dynamics beyond the reach of satellite gridded data, and playing a critical role in key fields such as numerical weather p...
Songru Yang, Zili Liu, Tao Han et al.· 0 citations
Speculative decoding in Mixture-of-Experts (MoE) models faces the problem of unstable verification cost caused by input-dependent expert loading. To study the physics of this process, we formulate speculation-budget selection as an offline Stochastic Shortest Path (SSP) problem over reference sequences and build a diag...
This work proposes Think Short, Defer Smart (TSDS), a framework that synergistically integrates a lightweight convergence probe, which halts on-device reasoning once the intended action has stabilized, with a perplexity-based deferral rule that escalates uncertain actions to a cloud-side model.
It is shown that the matching-level Graves-Lai constraints reduce to finitely many pairwise exploration quotas and, at top-choice-separated instances, yield a polynomial-size marginal linear program.
Li-Sha-Ge-Yuan-Yuan Xu, Guo-Dong Ma, Peng-Cheng Weng et al.· 0 citations
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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