arXiv Machine Learning

Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model

arXiv:2607. 22221v1 Announce Type: cross Abstract: Zermelo's algorithm is a classical method for computing the maximum likelihood estimator in the Bradley--Terry (BT) model, but its convergence can be slow in practice.

arXiv Machine Learning

Jun 15

Nonlinear Two-Time-Scale Stochastic Approximation: A Sharp Phase Transition and How to Beat It

arXiv:2606. 14488v1 Announce Type: cross Abstract: Recent finite-time analyses of nonlinear two-time-scale stochastic approximation show that under contractive assumptions the slow iterate $Y_k$ with stepsizes $\beta_k=\Theta(k^{-1})$ and $\alpha_k=\Theta(k^{-a})$, $a\in(1/2,1)$, generally satisfies a mean-square rate of order $k^{-a}$; decoupled $k^{-1}$ rates require strong local linearity.

By Dhruv Sarkar, Vaneet Aggarwal

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arXiv Machine Learning

Jun 4

Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent

arXiv:2606. 04031v1 Announce Type: new Abstract: Coupled gradient descent--where the update of one parameter block depends on another--underlies bilevel optimization, two-time-scale stochastic approximation, and adversarial training.

By Ahanaf Hasan Ariq

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arXiv Machine Learning

Jul 22

Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics

arXiv:2607. 18559v1 Announce Type: cross Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajectory of a dependent stochastic process.

By Vignesh Tirukkonda, Gautam Dasarathy

arXiv Machine Learning

Jul 16

Non-Expansive Two-Time-Scale Stochastic Approximation: A Fixed-Schedule One-Quarter Barrier and Bias-Corrected Acceleration

arXiv:2607. 13414v1 Announce Type: cross Abstract: Non-expansive two-time-scale stochastic approximation is governed by a slow stochastic Krasnoselskii--Mann fixed-point iteration rather than by contraction to a unique equilibrium.

By Dhruv Sarkar, Vaneet Aggarwal

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Hugging Face Trending Papers

Jun 4

Fast and Robust Convergence Rate for TD(0) with Linear Function Approximation, Universal Learning Steps and I.I.D. Samples

In this paper, we study the finite-time behavior of the TD(0) temporal-difference method with linear function approximation (LFA). We consider on-policy independent and identically distributed (i.

arXiv Machine Learning

Jun 5

Fast and Robust Convergence Rate for TD(0) with Linear Function Approximation, Universal Learning Steps and I.I.D. Samples

arXiv:2606. 05967v1 Announce Type: cross Abstract: In this paper, we study the finite-time behavior of the TD(0) temporal-difference method with linear function approximation (LFA).

By Ziad Kobeissi (L2S), \'Elo\"ise Berthier (U2IS)

arXiv Machine Learning

Jun 2

A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling

arXiv:2606. 01720v1 Announce Type: new Abstract: We study finite-sample generalization for a client-sampled distributed optimization scheme with matrix-valued parameters and orthogonalized momentum updates.

By Da Chang, Qiankun Shi, Lvgang Zhang, Yu Li, Ruijie Zhang

arXiv Machine Learning

Aug 12

Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization

arXiv:2310. 15976v4 Announce Type: replace Abstract: signSGD is attractive in nonconvex optimization because it communicates sign-valued rather than full-precision gradients.

By Zhen Qin, Zhishuai Liu, Pan Xu

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arXiv Machine Learning

Jun 26

\chisao{}: A GPU-Native Parallel Optimizer for Multimodal Black-Box Functions via Convergence-Anticonvergence Oscillation

arXiv:2606. 26164v1 Announce Type: new Abstract: Finding all modes of a multimodal black-box function is a fundamental challenge in optimization, Bayesian inference, and scientific computing.

By Ira Wolfson

multimodal benchmarks

Hugging Face Trending Papers

Jun 24

\chisao{}: A GPU-Native Parallel Optimizer for Multimodal Black-Box Functions via Convergence-Anticonvergence Oscillation

Finding all modes of a multimodal black-box function is a fundamental challenge in optimization, Bayesian inference, and scientific computing. Existing approaches -- basin-hopping, CMA-ES, multistart gradient descent -- operate sequentially and cannot exploit the massive parallelism of modern GPU hardware.

multimodal benchmarks

arXiv Machine Learning

Aug 3

Adaptivity via a Parallel Architecture for Stochastic Gradient Methods Adaptivity via a Parallel Architecture for Stochastic Gradient Methods Adaptivity via a Parallel Architecture for Stochastic Gradient Methods

arXiv:2607. 28902v1 Announce Type: new Abstract: We develop a parallel framework that assembles static gradient methods to achieve better adaptivity.

By Bin Fu

arXiv Machine Learning

Aug 4

Improved convergence rate of kNN graph Laplacians: differentiable self-tuned affinity

arXiv:2410. 23212v3 Announce Type: replace-cross Abstract: In graph-based data analysis, $k$-nearest neighbor ($k$NN) graphs are widely used due to their adaptivity to local data densities.

By Xiuyuan Cheng, Yixuan Tan, Nan Wu

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