research
∙
05/23/2023
Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach
Recent text-to-image generation models have demonstrated impressive capa...
share
research
∙
11/24/2022
Shifted Diffusion for Text-to-image Generation
We present Corgi, a novel method for text-to-image generation. Corgi is ...
research
∙
10/25/2022
Lafite2: Few-shot Text-to-Image Generation
Text-to-image generation models have progressed considerably in recent y...
research
∙
10/03/2022
On Stability and Generalization of Bilevel Optimization Problem
(Stochastic) bilevel optimization is a frequently encountered problem in...
research
∙
09/17/2022
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data
In this paper, we study the problem of PAC learning halfspaces in the no...
research
∙
07/28/2022
Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning
Exemplar-free Class-incremental Learning (CIL) is a challenging problem ...
research
∙
06/15/2022
In-Range Farthest Point Queries and Related Problem in High Dimensions
Range-aggregate query is an important type of queries with numerous appl...
research
∙
02/05/2022
Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach
Few-shot learning (FSL) is the process of rapid generalization from abun...
research
∙
01/10/2022
Differentially Private ℓ_1-norm Linear Regression with Heavy-tailed Data
We study the problem of Differentially Private Stochastic Convex Optimiz...
research
∙
12/07/2021
A Generic Approach for Enhancing GANs by Regularized Latent Optimization
With the rapidly growing model complexity and data volume, training deep...
research
∙
11/27/2021
LAFITE: Towards Language-Free Training for Text-to-Image Generation
One of the major challenges in training text-to-image generation models ...
research
∙
06/25/2021
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization
Deep Neural Networks (DNNs), despite their tremendous success in recent ...
research
∙
05/10/2021
Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels
Learning high-dimensional distributions is an important yet challenging ...
research
∙
02/07/2021
Meta-Learning with Neural Tangent Kernels
Model Agnostic Meta-Learning (MAML) has emerged as a standard framework ...
research
∙
11/11/2020
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
In this paper, we study the Empirical Risk Minimization (ERM) problem in...
research
∙
10/22/2020
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
(Gradient) Expectation Maximization (EM) is a widely used algorithm for ...
research
∙
10/21/2020
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
In this paper, we consider the problem of designing Differentially Priva...
research
∙
10/19/2020
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding
In this paper, we study the problem of estimating latent variable models...
research
∙
10/19/2020
Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably
Recently, many machine learning and statistical models such as non-linea...
research
∙
10/05/2020
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Manifold learning is a fundamental problem in machine learning with nume...
research
∙
05/16/2020
Graph Neural Networks with Composite Kernels
Learning on graph structured data has drawn increasing interest in recen...
research
∙
05/15/2020
Towards Assessment of Randomized Mechanisms for Certifying Adversarial Robustness
As a certified defensive technique, randomized smoothing has received co...
research
∙
05/15/2020
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness
As a certified defensive technique, randomized smoothing has received co...
research
∙
12/02/2019
KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling
Learning with kernels is an often resorted tool in modern machine learni...
research
∙
10/01/2019
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
In this paper, we study the problem of estimating smooth Generalized Lin...
research
∙
01/18/2019
Differentially Private High Dimensional Sparse Covariance Matrix Estimation
In this paper, we study the problem of estimating the covariance matrix ...
research
∙
12/17/2018
Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation
In this paper, we study the Empirical Risk Minimization problem in the n...
research
∙
10/02/2018
A Unified Framework for Clustering Constrained Data without Locality Property
In this paper, we consider a class of constrained clustering problems of...
research
∙
02/14/2018
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
In this paper we study the differentially private Empirical Risk Minimiz...
research
∙
02/12/2018
Efficient Empirical Risk Minimization with Smooth Loss Functions in Non-interactive Local Differential Privacy
In this paper, we study the Empirical Risk Minimization problem in the n...
research
∙
02/09/2018
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning
In this paper, we revisit the large-scale constrained linear regression ...
research
∙
03/24/2016
