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3,017 papers
This work analyzes the convergence of the noisy power method and Oja's algorithm, both studied for the stationary data generating model, and argues that the noisy power method is rate-optimal in this setting.
Canon, a risk-averse method designed to optimize Value-at-Risk (VaR) constrained reinforcement learning (RL) problems, is introduced and Cantelli's inequality is employed to obtain a tractable, conservative and smooth bound on the VaR constraint based on the first two moments of the cost return.
A novel sampling algorithm is presented that uses observed label frequencies to estimate multivariate Bernoulli distribution parameters and calculates weights for each label combination, to preserve category frequency order, reduce frequency differences between most and least common categories, and account for category...
On a 124M-parameter language model and selected short training windows, the method can lower simulated ledger cost relative to the tested baselines when fleet work is sufficiently cheap, and can lower simulated ledger cost relative to the tested baselines when fleet work is sufficiently cheap.
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The anytime lower bound shows that the rate of non-anytime silver schedules (Altschuler and Parrilo, 2025; Grimmer et al., 2025) is unattainable in the anytime setting, and establishes a strict separation between the two settings.
It is proved that in noiseless Gaussian compressed sensing, the full-dimensional linear prior attains the minimum expected reconstruction error over the entire family of linear priors, indicating that the experimental benefits of tunability in compressed sensing with neural network priors arises due to nonlinearities i...
A three-regime batch-size phase diagram that reveals how the role of momentum changes with batch size is revealed, with Polyak enlarging the critical batch size, the largest batch size preserving the best small-batch data-scaling exponent, thereby enabling greater parallelism without sacrificing data efficiency.
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.
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...
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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