Search Results for author:
Found 48 papers, 11 papers with code
Bayesian crack detection in ultra high resolution multimodal images of paintings
The preservation of our cultural heritage is of paramount importance.
Minimax Optimal Bayesian Aggregation
It is generally believed that ensemble approaches, which combine multiple algorithms or models, can outperform any single algorithm at machine learning tasks, such as prediction.
Randomized sketches for kernels: Fast and optimal non-parametric regression
Kernel ridge regression (KRR) is a standard method for performing non-parametric regression over reproducing kernel Hilbert spaces.
On the Computational Complexity of High-Dimensional Bayesian Variable Selection
We study the computational complexity of Markov chain Monte Carlo (MCMC) methods for high-dimensional Bayesian linear regression under sparsity constraints.
Joint estimation of quantile planes over arbitrary predictor spaces
In spite of the recent surge of interest in quantile regression, joint estimation of linear quantile planes remains a great challenge in statistics and econometrics.
Communication-Efficient Distributed Statistical Inference
CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation and Bayesian inference.
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood
In this article, we investigate large sample properties of model selection procedures in a general Bayesian framework when a closed form expression of the marginal likelihood function is not available or a local asymptotic quadratic approximation of the log-likelihood function does not exist.
Statistical inference for high dimensional regression via Constrained Lasso
The proposed estimator, called Constrained Lasso (CLasso) estimator, is obtained by simultaneously solving two estimating equations---one imposing a zero-bias constraint for the low-dimensional parameter and the other forming an $\ell_1$-penalized procedure for the high-dimensional nuisance parameter.
Frequentist coverage and sup-norm convergence rate in Gaussian process regression
By developing a comparison inequality between two GPs, we provide exact characterization of frequentist coverage probabilities of Bayesian point-wise credible intervals and simultaneous credible bands of the regression function.
$α$-Variational Inference with Statistical Guarantees
We propose a family of variational approximations to Bayesian posterior distributions, called $\alpha$-VB, with provable statistical guarantees.
On Statistical Optimality of Variational Bayes
The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation.
Diffusion $K$-means clustering on manifolds: provable exact recovery via semidefinite relaxations
We show that exact recovery of the localized diffusion $K$-means is fully adaptive to the local probability density and geometric structures of the underlying submanifolds.
High-Dimensional Linear Regression via Implicit Regularization
Many statistical estimators for high-dimensional linear regression are M-estimators, formed through minimizing a data-dependent square loss function plus a regularizer.
Cutting the Unnecessary Long Tail: Cost-Effective Big Data Clustering in the Cloud
A novel approach is proposed to achieve cost-effective big data clustering in the cloud.
Statistical Inference in Mean-Field Variational Bayes
We conduct non-asymptotic analysis on the mean-field variational inference for approximating posterior distributions in complex Bayesian models that may involve latent variables.
Cutoff for exact recovery of Gaussian mixture models
We determine the information-theoretic cutoff value on separation of cluster centers for exact recovery of cluster labels in a $K$-component Gaussian mixture model with equal cluster sizes.
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples.
Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis
To abandon this eigengap assumption, we consider a new route in our analysis: instead of exactly identifying the top-$L$-dim eigenspace, we show that our estimator is able to cover the targeted top-$L$-dim population eigenspace.
Hyperspectral Images Classification Based on Multi-scale Residual Network
Because hyperspectral remote sensing images contain a lot of redundant information and the data structure is highly non-linear, leading to low classification accuracy of traditional machine learning methods.
MFL_COVID19: Quantifying Country-based Factors affecting Case Fatality Rate in Early Phase of COVID-19 Epidemic via Regularised Multi-task Feature Learning
The fused sparse group Lasso (FSGL) method allows the simultaneous selection of a common set of country-based factors for multiple time points of COVID-19 epidemic and also enables incorporating temporal smoothness of each factor over the whole early phase period.
Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization
CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network.
Multi-Knowledge Fusion for New Feature Generation in Generalized Zero-Shot Learning
Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL).
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification
The approach also applies a semi-supervised learning process to re-train knowledge-to-visual model.
Accumulations of Projections--A Unified Framework for Random Sketches in Kernel Ridge Regression
Building a sketch of an n-by-n empirical kernel matrix is a common approach to accelerate the computation of many kernel methods.
Fast Statistical Leverage Score Approximation in Kernel Ridge Regression
Nystr\"om approximation is a fast randomized method that rapidly solves kernel ridge regression (KRR) problems through sub-sampling the n-by-n empirical kernel matrix appearing in the objective function.
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario.
Hypernetwork Dismantling via Deep Reinforcement Learning
Network dismantling aims to degrade the connectivity of a network by removing an optimal set of nodes.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
AdvDrop: Adversarial Attack to DNNs by Dropping Information
Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e. g. cartoon.
Regret Lower Bound and Optimal Algorithm for High-Dimensional Contextual Linear Bandit
This new lower bound unifies existing regret bound results that have different dependencies on T due to the use of different values of margin parameter $\alpha$ explicitly implied by their assumptions.
Revisiting Layer-wise Sampling in Fast Training for Graph Convolutional Networks
To accelerate the training of graph convolutional networks (GCN), many sampling-based methods have been developed for approximating the embedding aggregation.
Learning Topic Models: Identifiability and Finite-Sample Analysis
Our theory introduces a new set of geometric conditions for topic model identifiability, conditions that are weaker than conventional separability conditions, which typically rely on the existence of pure topic documents or of anchor words.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nyström Method
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences
Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules.
N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks
We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction.
Sketch-and-Lift: Scalable Subsampled Semidefinite Program for $K$-means Clustering
Semidefinite programming (SDP) is a powerful tool for tackling a wide range of computationally hard problems such as clustering.
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
This paper revisits the approach from a matrix approximation perspective, and identifies two issues in the existing layer-wise sampling methods: suboptimal sampling probabilities and estimation biases induced by sampling without replacement.
Mean-field Variational Inference via Wasserstein Gradient Flow
Variational inference, such as the mean-field (MF) approximation, requires certain conjugacy structures for efficient computation.
Wasserstein $K$-means for clustering probability distributions
Clustering is an important exploratory data analysis technique to group objects based on their similarity.
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Clustering is a widely deployed unsupervised learning tool.
TFormer: A Transmission-Friendly ViT Model for IoT Devices
The high performance and small number of model parameters and FLOPs of TFormer are attributed to the proposed hybrid layer and the proposed partially connected feed-forward network (PCS-FFN).
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions
In this paper, our goal is to design a simple learning paradigm for long-tail visual recognition, which not only improves the robustness of the feature extractor but also alleviates the bias of the classifier towards head classes while reducing the training skills and overhead.
Ranked #1 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
In contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm widely used by machine learning practitioners, but it lacks a solid statistical underpinning and theoretical guarantees.
CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network
We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network.
On the Convergence of Coordinate Ascent Variational Inference
As a computational alternative to Markov chain Monte Carlo approaches, variational inference (VI) is becoming more and more popular for approximating intractable posterior distributions in large-scale Bayesian models due to its comparable efficacy and superior efficiency.
A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
The study includes a set of experiments to support the theory and method, including approximating the GW distance, preserving the graph spectrum, classifying graphs using spectral information, and performing regression using graph convolutional networks.
Bayesian Model Selection via Mean-Field Variational Approximation
This article considers Bayesian model selection via mean-field (MF) variational approximation.
A Practical Beamforming Design for Active RIS-assisted MU-MISO Systems
Motivated by this fact, in this paper we first investigate the amplification principle of typical active RIS and propose a more accurate amplification model based on amplifier hardware characteristics.
