AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,618 papers
These results identify conditions under which the resulting predictor exploits local geometry and attains the aggregate minimax rate, and derive an in-context generalization bound for near empirical risk minimizers over this class.
Bayesian TAGs are demonstrated to be well suited to quantifying uncertainty in equation discovery for dynamical systems and to fitting physics-informed models.
We study geometric moment contraction (GMC) of the constant-parameter stochastic Nesterov recursion \[ Y_k=\Theta_k+\beta(\Theta_k-\Theta_{k-1}),\qquad \Theta_{k+1}=Y_k-\gamma G(Y_k,X_{k+1}). \] Under mean strong monotonicity and stochastic $L^p$ Lipschitz continuity, an explicit Perron comparison proves synchronous $L...
The flatness of the loss landscape at a minimizer is a widely used heuristic for reasoning about neural-network generalization, yet evidence for this relation is mostly empirical and controversial. We study this relation in a teacher-student tree committee machine, where both the ERM estimator and the Hessian spectrum...
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This work uses Exponential Tilt Reweighting Alignment (ExTRA), introduced for classification by Maity et al. (2023), to estimate structured distribution shifts and compares using its estimated weights in conformal calibration with additionally tilting the source predictive distribution.
We consider stochastic nonlinear optimization problems with deterministic equality constraints. While unconstrained stochastic optimization is well understood, the interplay between optimality and feasibility in the constrained setting poses significant challenges. Moreover, existing theoretical guarantees for constrai...
The technique of empirical localization makes it possible to drastically reduce computational cost of the calculation of the normalization constant, and appropriate choice of the deformation for the Bregman divergence can invest the proposed estimator with various kinds of favorable statistical properties, such as effi...
A score-based DAG learning method, named MARCEDES, formulated as an unconstrained score minimization problem, which can be efficiently solved using gradient-based optimization techniques, thereby circumventing the challenges associated with constrained optimization.
A learning-based framework is proposed to replace this MCMC step by reformulating both Gibbs updates as Gaussian denoising problems and implementing them through ODE diffusion, resulting in an alternative to MCMC-based split Gibbs at lower likelihood-update cost.
Stochastic gradient descent for smooth, lower-bounded, possibly nonconvex objectives under distance-dependent conditional moments is studied and unchanged SGD attains the minimax stochastic complexity in this second-moment class.
Together these findings provide concrete, scanner-specific guidance for protocol optimization, with direct implications for reducing acquisition costs and improving the reliability of connectivity-based clinical biomarkers, and provide code enabling researchers to derive principled uncertainty bounds from their own dat...
An end-to-end causal machine learning pipeline designed for real-world applications with continuous treatments that offers a more computationally efficient approach and produces less biased estimates compared to standard methods for problems with continuous treatment and high-dimensional data is presented.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
As director, Bourg has focused on digital access, open and equitable scholarly publishing, and expanded support for data-intensive research.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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