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3,046 papers
This work introduces the general class of so-called Chernoff-neural operators and shows in a universal approximation theorem that they can approximate the Chernoff one-step operators arbitrarily well, and introduces the more specialized class of envelope-neural operators for envelope semigroups which allows for quantit...
This work addresses the computation of the MLT for CGGMs by focusing on its geometric formulation: finding the minimum rank of a sample covariance matrix such that its projection lies almost surely within the interior of the cone of sufficient statistics.
By bridging graph topology learning and graph generation, this review provides a broader cross-disciplinary perspective on the field and outlines promising directions for future research.
Experiments on logistic regression, probabilistic matrix factorization, and Bayesian neural networks show that HyperMC improves posterior approximation or predictive calibration relative to MAMBA, grid search, and heuristic baselines, while Robust HyperMC yields more stable and reproducible tuning results.
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This work introduces a variant of the Thompson Sampling algorithm that uses a fractional or $\alpha$-posterior instead of the standard posterior, and identifies general regularity conditions on the prior and reward distributions that enable a regret analysis of $\alpha$-TS without assuming any tractable approximation o...
This work proves that on generated data, the state channel cuts regret by up to 79 percent against action-level weighting and, on the funnel family, by 32 to 68 percent against a tuned minimax-optimal method.
The FeeBee framework is extended, originally proposed for evaluating Bayes-error estimators, to the optimal BER and AUC, and the resulting procedure provides practical evaluation scores without requiring knowledge of the optimum.
This work characterize models from this family of methods as JEPA-WMs and investigates the technical choices that make algorithms from this class work, and proposes a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks.
Generative modeling directly on geometric manifolds can avoid errors introduced by flattening non-Euclidean data, repeated ambient projection, and coordinate inconsistency in Euclidean representations. Schrodinger bridges provide a probabilistic generative framework for entropy-regularized transport between prescribed...
This work studies online maximization of nonnegative, non-monotone DR-submodular functions over compact convex down-closed subsets of the $d$-dimensional unit cube and shows that the best known constructive offline approximation factor is also achievable online.
This study designed and empirically validated a three-layer Zero Trust architecture for Microsoft SQL Server that natively integrates instance-level authentication governance, enhanced Role-Based Access Control with Row-Level Security and Just-in-Time privilege elevation, and metadata-driven Attribute-Based Access Cont...
Over-parameterized linear regression has been widely studied over the last decade. However, most existing works assume that the covariates are independent and that their covariance matrices are non-degenerate. In this paper, we relax both assumptions and derive deterministic equivalents for the prediction risk in a van...
Accelerating the search for novel antimicrobial peptides using Google Antigravity for Science.
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.
General Science
General Science
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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