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...
Yasin Khadem Charvadeh, Grace Y. Yi, M. Gönen et al.· 0 citations
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...
Zhe Li, Q. Clairon, C. Samieri et al.· 0 citations
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.
Minxing Zheng, H. Wiberg, Shixiang Zhu· 0 citations
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.
Yuanyuan Shen, Yiren Yan, Wenjie Li et al.· 0 citations
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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...
We determine the sharp restricted isometry threshold for recovery at global minima of the rank-restricted matrix LASSO. For target rank $r_{\star}$, if the rank-$k$ RIP constant satisfies $\delta<\delta_{\mathrm{sharp}}(k/r_{\star})$, where $\delta_{\mathrm{sharp}}(t)=t/(4-t)$ for $0<t<4/3$ and $\delta_{\mathrm{sharp}}...
This work proposes Jigsaw-CRL, a framework for recovering global latent causal order from a fragmented multi-client setting, where multiple clients interact with the same global latent causal system but each client only accesses and intervenes on a subset of the latent variables.
The aggregate IGC mass admits bounds in terms of total correlation and dual total correlation, thereby connecting the pathwise geometry to classical measures of multivariate dependence and connecting the pathwise geometry to classical measures of multivariate dependence.
This work establishes quantum signal processing (QSP) as a solvable quantum model of the representation-learning regime, and proves a sparse-data guarantee for the full nonlinear gradient flow without freezing or ensemble-averaging the kernel.
We establish quantitative convergence to the target and uniform-in-time propagation of chaos for Langevin-regularized Stein variational gradient descent. The Stein interaction need not be small relative to the confining Langevin drift and does not generally yield a contractive particle coupling. At the mean-field level...
The result shows that even a fixed-dimensional latent space suffices to achieve vanishing approximation error as the network budget increases, and it is proved that the optimal worst-case uniform approximation error over the unit ball ofolder functions on $[0,1]^d$ has the sharp order.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
