SPACR (Single-Pass Adaptive Conformal Regressor), a novel method for directly training uncertainty-aware regressors within a differentiable loss, jointly optimizes accuracy, efficiency, and validity without batch-splitting or a predefined confidence level during training.
S. Messoudi, Sylvain Rousseau, Sébastien Destercke· arXiv.org· 0 citations
This work proposes that the objective for learning simulators should be strategic robustness rather than predictive accuracy, and formulate this as a zero-sum minimax game between a model player and an adversarial policy player, which yields a provably convergent active data selection algorithm.
Christoph Dann, Yishay Mansour, M. Mohri· arXiv.org· 0 citations
Distinguishing profile-shape equivalence from fixed-parameter operator equivalence separates new normalization shapes from adaptive rescalings and organizes the operators by support, tail behavior, and realization complexity.
This paper extends the cooperative multi-task SemCom framework to jointly handle heterogeneous classification and regression tasks on the complex Cityscapes dataset and adopts the information maximization (InfoMax) principle so that it accommodates mixed discrete and continuous semantic variables.
Ahmad Halimi Razlighi, M. Rahman, Maximilian H. V. Tillmann et al.· 0 citations
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It is shown that the critical scaling and asymptotic limit are uniquely determined by the decay of correlations together with the Hermite rank of the feature function, which represents meaningful hyperparameters in the asymptotic regime.
This paper derives oracle risk bounds for learning dense and sparse routing with evolving experts, separating approximation, expert-learning, and router-estimation errors, and characterize how sparse Top-K routing can retain the benefits of localized aggregation while controlling per-input computation.
Si-Yuan He, Bo-Kai Yang, Jie Hu et al.· 0 citations
This work proposes Adaptive Local Relational Alignment (ALRA), a position-specific framework combining student proposals with teacher guidance, which replaces the teacher-mass coefficients of the two conditional terms with unit coefficients, preventing either term from being downweighted solely because its region has l...
Quang Hoang Trung, Quang Huu Hieu, Phuc-Thanh Nguyen et al.· 0 citations
A high-probability uniform ($L^{\infty}$) recovery guarantee that jointly controls approximation and statistical errors while enjoying an optimization error of zero is introduced.
Rui-Yang Hong, Hrad Ghoukasian, Anastasis Kratsios· 0 citations
Occupancy-based Quantile Risk Control (OQRC), a novel method that provides tight risk control bounds with finite-sample validity and establishes a finite-sample guarantee showing that OQRC yields tight risk control bounds that converge to the optimal bounds at a provable rate of $\mathcal{O}_ p(n^{-1/2})$.
Zi-Hao Shi, Hua-Jun Xi, Bing-Yi Jing et al.· 0 citations
The Conformal Relevance framework is introduced which uses in-context learning example curation and ensembling to create a score function which maintains coverage while improving conciseness with minimal manual input.
Xiao-Shi Huang, Chen Lin, Bruce Kuwahara et al.· 0 citations
A statistical feature augmentation method that explicitly encodes behavioral interaction statistics into the input feature space and consistently improves detection performance is proposed, showcasing a promising approach for merging classical network analysis with deep learning.
Philipp Schlinge, Jean-Luc Schnipper, M. Atzmueller· 0 citations
A mean-field RL framework is introduced in which the rewards and transition dynamics depend on the population only through an unknown low-dimensional aggregate statistic, and it is shown that under a fixed neural-network parameter count and optimization budget, learning a low-dimensional population representation impro...
Aditya Makkar, B. Unger, Jeongyeol Kwon 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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