AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,952 papers
Neural differential equations offer a powerful approach for learning dynamical systems from data. However, they do not inherently respect known constraints, such as conservation laws, that should be obeyed by the learned dynamics. It is well known that enforcing constraints in data-driven models can enhance their gener...
An online variant of the hard thresholding algorithm that performs the sparse estimation in an online manner is developed and embedded in a primal-dual scheme: every knapsack constraint is paired with a dual variable, which is updated by an online learning rule to keep the cumulative resource consumption within budget.
Two new algorithms for estimating the parameters of a latent class model for ordered categorical data with polytomous responses are introduced based on a newly defined regularized Laplacian matrix derived from the response matrix.
The equivalent loss is introduced, whose gradient is the original loss gradient multiplied by the query probability, which derives a finite-sample excess-risk bound with a fixed learning rate and an explicit constant controlling the tradeoff between initial error and query-weighted gradient variance.
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Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the Hamming slice $\mathcal{X}_k^d:=\{\mathbf{x}\in\{\pm 1\}^d:|\{i:\mathbf{x}_i=1\}|=k\}$, in high-dimensional regimes where $k\ll d$ (i.e., where $\mathcal{X}_k^d$ is \emp...
In this paper, we consider the scalar-on-function linear regression model under a realistic sampling scheme in which the functional covariates are observed on a regular grid and contaminated by additive noise. We propose a two-step estimation procedure: first, the underlying curves are reconstructed from the discrete n...
This work identifies the minimum interval budget needed to retain the unrestricted 1-bit minimax sample rate and determines the minimax sample complexity among non-adaptive 1-bit estimators when every one-set $Q^{-1}(1)$ is restricted to a union of at most $s$ intervals.
We study the time-uniform convergence of the raw iterate of standard stochastic gradient descent (SGD) for unconstrained smooth convex objectives. We prove that, under standard noise assumptions, the time-uniform convergence rate gets arbitrarily close to $\sqrt{\log n / n}$ but never reaches it. More specifically, we...
Suppose we are given an ordered sequence of independent data whose distribution changes $K$ times at unknown locations, for some unknown $K \geq 0$. In this paper, we study the problem of performing distribution-free inference on $K$. First, we show an impossibility result: any distribution-free upper confidence bound...
A Fisher-information flow speed limit is derived that bounds the rate at which trainable parameters can acquire information about latent variables in the data-generating process and establishes Fisher-information speed limits as a quantitative framework for diagnosing when and how different aspects of the data-generati...
Simulations demonstrate that the tuning schemes reliably yield near-optimal slice-adaptive tuning with essentially no dependence on the initial setting of $w$, and develop automated, slice-adaptive tuning schemes along with suboptimality bounds and asymptotic convergence guarantees.
This paper moves beyond point estimation and discusses frequentist and Bayesian approaches to uncertainty quantification for fair machine learning, with practical examples and implications for simulated and real data.
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.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
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