no code implementations • ICML 2020 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

Graph matching, also known as network alignment, aims at recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs.

no code implementations • 15 Feb 2024 • Zhichao Wang, Denny Wu, Zhou Fan

Many recent works have studied the eigenvalue spectrum of the Conjugate Kernel (CK) defined by the nonlinear feature map of a feedforward neural network.

no code implementations • 14 Feb 2024 • Michael J. Curry, Zhou Fan, David C. Parkes

The role of a market maker is to simultaneously offer to buy and sell quantities of goods, often a financial asset such as a share, at specified prices.

no code implementations • 14 Nov 2023 • Michael Celentano, Zhou Fan, Licong Lin, Song Mei

In settings where it is conjectured that no efficient algorithm can find this local neighborhood, we prove analogous geometric properties for a local minimizer of the TAP free energy reachable by AMP, and show that posterior inference based on this minimizer remains correctly calibrated.

no code implementations • 28 Sep 2023 • Zhou Fan, Xinran Han, Zi Wang

Bayesian optimization (BO) is a popular black-box function optimization method, which makes sequential decisions based on a Bayesian model, typically a Gaussian process (GP), of the function.

1 code implementation • 20 Dec 2022 • Zhou Fan, Xinran Han, Zi Wang

However, those prior learning methods typically assume that the input domains are the same for all tasks, weakening their ability to use observations on functions with different domains or generalize the learned priors to BO on different search spaces.

no code implementations • 21 Jun 2021 • Michael Celentano, Zhou Fan, Song Mei

This provides a rigorous foundation for variational inference in high dimensions via minimization of the TAP free energy.

1 code implementation • 21 Dec 2020 • Xinyi Zhong, Chang Su, Zhou Fan

When the dimension of data is comparable to or larger than the number of data samples, Principal Components Analysis (PCA) may exhibit problematic high-dimensional noise.

no code implementations • 31 May 2020 • Sheng Xu, Zhou Fan, Sahand Negahban

We study estimation of a gradient-sparse parameter vector $\boldsymbol{\theta}^* \in \mathbb{R}^p$, having strong gradient-sparsity $s^*:=\|\nabla_G \boldsymbol{\theta}^*\|_0$ on an underlying graph $G$.

no code implementations • NeurIPS 2020 • Zhou Fan, Zhichao Wang

We study the eigenvalue distributions of the Conjugate Kernel and Neural Tangent Kernel associated to multi-layer feedforward neural networks.

no code implementations • 20 Jul 2019 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

Departing from prior spectral approaches that only compare top eigenvectors, or eigenvectors of the same order, GRAMPA first constructs a similarity matrix as a weighted sum of outer products between all pairs of eigenvectors of the two graphs, with weights given by a Cauchy kernel applied to the separation of the corresponding eigenvalues, then outputs a matching by a simple rounding procedure.

no code implementations • 20 Jul 2019 • Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu

We analyze a new spectral graph matching algorithm, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs.

1 code implementation • NeurIPS 2019 • Ganlin Song, Zhou Fan, John Lafferty

When initialized with random parameters $\theta_0$, we show that the objective $f_{\theta_0}(x)$ is "nice'' and easy to optimize with gradient descent.

no code implementations • 15 May 2019 • Sheng Xu, Zhou Fan

We consider estimating a piecewise-constant image, or a gradient-sparse signal on a general graph, from noisy linear measurements.

1 code implementation • 4 Mar 2019 • Zhou Fan, Rui Su, Wei-Nan Zhang, Yong Yu

In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks.

no code implementations • 17 Oct 2016 • Zhou Fan, Andrea Montanari

Several probabilistic models from high-dimensional statistics and machine learning reveal an intriguing --and yet poorly understood-- dichotomy.

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