It is shown how epistemic and aleatoric uncertainty measures can be derived via decomposition of a subjective risk, based on a strictly proper loss, and considered a first step towards a full learning-theoretic framework for uncertainty quantification.
R. Alamri, Michele Caprio, Gavin Brown· arXiv.org· 1 citation· ⚡1
This note studies the geometry of full conformal prediction (FullCP) regions generated by an empirical energy-form pairwise score and finds modest but systematic tightening of the generic certificate and frequent robust nonconvexity witnesses.
DiscoverPhysics, an interactive benchmark that asks a LLM agent to discover the laws of motion of a simulated world whose physics deliberately deviates from the authors' own, is introduced.
M. Wiemann, Lindsay M. Smith, Peter Melchior et al.· arXiv.org· 12 citations· ⚡2
Simulations on EEG motor imagery benchmarks show that FedSPDnet outperforms federated EEGnet in F1 score and robustness to federation and partial participation, while using fewer parameters per communication round.
An MNAR-aware latent state-space model that combines linear traffic dynamics with a Bernoulli missingness channel whose probability depends on the latent state is studied, achieving lower P95 error, lower long-blackout RMSE, and orders of magnitude fewer stored scalar entries.
Aman Sunesh, Alan Ma, Siddarth Nilol· arXiv.org· 1 citation
A new error metric is introduced that precisely captures model vulnerability to consistent adversarial attacks -- perturbations that preserve the ground-truth labels, offering theoretical insight into the mechanisms underlying model sensitivity to adversarial attacks.
Matteo Vilucchio, Lenka Zdeborová, Bruno Loureiro· arXiv.org· 1 citation
This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the score function with a bandwidth tied to the learning rate, we obtain estimates that are monotone in the quantile level at every iteration, while retaining the memory and computati...
Li-Kai Chen, Georg Keilbar, Wei-Biao Wu· 2 citations
This work builds on a principled continuous-time approximation of Markov algorithms and introduces a new, exact entropy flow formula for such processes, and establishes novel connections to a well-studied family of modified logarithmic Sobolev inequalities.
Benjamin Dupuis, Maxime Haddouche, George Deligiannidis et al.· 2 citations
A deep learning based approximation algorithm for solutions of stochastic partial differential equations (SPDEs) using a deep neural network for every realization of the driving noise process of a SPDE to approximate the solution process of the SPDE under consideration.
C. Beck, S. Becker, Patrick Cheridito et al.· arXiv.org· 16 citations· ⚡2
This work investigates how the model parameters -- mainly attention heads and head dimensions -- should be allocated across layers to balance expressivity and efficiency, and proposes principled strategies for allocating attention heads and dimensions across Transformers' layers.
Ruoxi Yu, Hao-Tian Jiang, Jingpu Cheng et al.· 1 citation
It is shown decoder-only MDMs, despite a larger modeling space, can achieve significant inference speedups and comparable perplexity with techniques like temperature annealing, offering a path to reduced inference compute.
Shuchen Xue, Tianyu Xie, Tianyang Hu et al.· arXiv.org· 17 citations· ⚡1
A multi-view linear Structural Equation Model (SEM) is proposed that extends the well-known framework of non-Gaussian disturbances by alternatively leveraging correlation over views and proves the identifiability of the model for acyclic SEMs.
Ambroise Heurtebise, Omar Chehab, Pierre Ablin 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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