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3,822 papers
A general framework for implementing NMMs using Template Model Builder (TMB), which automatically integrates out random effects and evaluates the marginal objective function alongside its exact gradients, eliminating the need for manual derivations or ad hoc approximations.
This work presents a framework for learning continuous latent representations of admissible partial differential equations by embedding a scientific inductive bias directly into the training distribution, and shows that embedding a scientific inductive bias in the training distribution enables the learning of compact a...
A classification-weighted generalized-eigenvalue criterion is developed under which informative partial classification may have smaller asymptotic classification risk without globally dominating complete classification in Fisher information.
Direct first-order jackknife cancellation and exact-LOO concentration control deletion-to-full risk transfer and fluctuation, respectively, completing recovery of the conditional population-risk curve are estimated.
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This work proposes a new design of fair classifiers for multi-class classification problems in the presence of vector-valued sensitive attributes and proposes a specialized numerical method for solving the resulting optimization problem.
A statistical framework connecting token prediction with representation geometry, encoder approximation, and downstream performance is developed, introducing a self-consistency principle showing that repeated applications of a shared representation block can progressively refine the contextual representation without in...
A unified probabilistic framework that jointly addresses missing data, measurement error, and population heterogeneity utilizing deep latent variable representation is proposed that integrates a novel hierarchical tree-routed variational autoencoder with pattern-aware latent representations and calibration-based denois...
The Neural ODE-LMM is proposed, which embeds a Neural Ordinary Differential Equation (Neural ODE) within the linear mixed-effects framework: a learned vector field encodes covariate trajectories into a continuous-time latent state that drives both the fixed- and random-effect design, while preserving the standard LMM o...
This work proposes \texttt{RADAR} (Regret-based Assessment of Decision Adequacy and Risk), a decision-focused framework that uses inverse optimization to infer latent preferences and tests the deployed decision's optimality gap under the current distribution.
This work analyzes four experiments on a major short-video platform, establishing a positive creator response, measuring the gross corpus flow visible within three weeks, and showing the design and duration needed to identify total value.
The signed random Fourier features (SRFF) technique is introduced, a generalization of RFF compatible with indefinite kernels whose inverse Fourier transform is absolutely integrable and speed up KDE in the case of multivariate compact kernels, which are generally not positive definite.
We study the empirical Sinkhorn estimator of the entropic optimal transport potentials under the uniform loss. Since the potentials are only unique up to additive constants, we measure the error using the quotient supremum norm, defined as $d_\infty([u],[v]) = \inf_{a\in\mathbb{R}}\|u-v-a\|_\infty$. For a fixed regular...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
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