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Found 38 papers, 6 papers with code
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
To address these issues, we first consider the problem in the $\epsilon$ non-interactive LDP model and provide a lower bound of $\Omega(\frac{\sqrt{dk\log d}}{\sqrt{n}\epsilon})$ on the $\ell_2$-norm estimation error for sub-Gaussian data, where $n$ is the sample size and $d$ is the dimension of the space.
Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach
However, generating images of novel concept provided by the user input image is still a challenging task.
Shifted Diffusion for Text-to-image Generation
Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion.
Ranked #3 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
Lafite2: Few-shot Text-to-Image Generation
The low requirement of the proposed method yields high flexibility and usability: it can be beneficial to a wide range of settings, including the few-shot, semi-supervised and fully-supervised learning; it can be applied on different models including generative adversarial networks (GANs) and diffusion models.
On Stability and Generalization of Bilevel Optimization Problem
In this paper, we conduct a thorough analysis on the generalization of first-order (gradient-based) methods for the bilevel optimization problem.
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 non-interactive local differential privacy model (NLDP).
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 because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs).
Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach
One observation is that the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the feature space.
Differentially Private $\ell_1$-norm Linear Regression with Heavy-tailed Data
Firstly, we study the case where the $\ell_2$ norm of data has bounded second order moment.
Towards Language-Free Training for Text-to-Image Generation
One of the major challenges in training text-to-image generation models is the need of a large number of high-quality text-image pairs.
A Generic Approach for Enhancing GANs by Regularized Latent Optimization
With the rapidly growing model complexity and data volume, training deep generative models (DGMs) for better performance has becoming an increasingly more important challenge.
LAFITE: Towards Language-Free Training for Text-to-Image Generation
One of the major challenges in training text-to-image generation models is the need of a large number of high-quality image-text pairs.
Ranked #2 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
Few-shot Learning via Dirichlet Tessellation Ensemble
One observation is that the widely embraced ProtoNet model is essentially a Dirichlet Tessellation (Voronoi Diagram) in the feature space.
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization
Deep Neural Networks (DNNs), despite their tremendous success in recent years, could still cast doubts on their predictions due to the intrinsic uncertainty associated with their learning process.
Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels
Learning high-dimensional distributions is an important yet challenging problem in machine learning with applications in various domains.
Meta-Learning with Neural Tangent Kernels
We achieve this goal by 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS; and 2) solving the adaptation analytically based on the NTK theory.
Meta-Learning in Reproducing Kernel Hilbert Space
Within this paradigm, we introduce two meta learning algorithms in RKHS, which no longer need an explicit inner-loop adaptation as in the MAML framework.
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
In our second attempt, we show that for any $1$-Lipschitz generalized linear convex loss function, there is an $(\epsilon, \delta)$-LDP algorithm whose sample complexity for achieving error $\alpha$ is only linear in the dimensionality $p$.
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
To address this issue, we propose in this paper the first DP version of (Gradient) EM algorithm with statistical guarantees.
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
For this case, we propose a method based on the sample-and-aggregate framework, which has an excess population risk of $\tilde{O}(\frac{d^3}{n\epsilon^4})$ (after omitting other factors), where $n$ is the sample size and $d$ is the dimensionality of the data.
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding
In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i. e.,} $d\gg n$) where the underlying parameter is assumed to be sparse.
Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably
To the best of our knowledge, this is the first work that studies and provides theoretical guarantees for the stochastic linear combination of non-linear regressions model.
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Manifold learning is a fundamental problem in machine learning with numerous applications.
Graph Neural Networks with Composite Kernels
We then propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space.
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness
Based on our framework, we assess the Gaussian and Exponential mechanisms by comparing the magnitude of additive noise required by these mechanisms and the lower bounds (criteria).
KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling
Learning with kernels is an important concept in machine learning.
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
In the second part of the paper, we extend our idea to the problem of estimating non-linear regressions and show similar results as in GLMs for both multivariate Gaussian and sub-Gaussian cases.
A Unified framework for randomized smoothing based certified defenses
We answer the above two questions by first demonstrating that Gaussian mechanism and Exponential mechanism are the (near) optimal options to certify the $\ell_2$ and $\ell_\infty$-normed robustness.
Differentially Private High Dimensional Sparse Covariance Matrix Estimation
In this paper, we study the problem of estimating the covariance matrix under differential privacy, where the underlying covariance matrix is assumed to be sparse and of high dimensions.
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
For the case of \emph{generalized linear losses} (such as hinge and logistic losses), we give an LDP algorithm whose sample complexity is only linear in the dimensionality $p$ and quasipolynomial in other terms (the privacy parameters $\epsilon$ and $\delta$, and the desired excess risk $\alpha$).
Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited
In this paper, we revisit the Empirical Risk Minimization problem in the non-interactive local model of differential privacy.
A Unified Framework for Clustering Constrained Data without Locality Property
To overcome the difficulty caused by the loss of locality, we present in this paper a unified framework, called {\em Peeling-and-Enclosing (PnE)}, to iteratively solve two variants of the constrained clustering problems, {\em constrained $k$-means clustering} ($k$-CMeans) and {\em constrained $k$-median clustering} ($k$-CMedian).
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
In this paper we study the differentially private Empirical Risk Minimization (ERM) problem in different settings.
Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case
In the case of constant or low dimensionality ($p\ll n$), we first show that if the ERM loss function is $(\infty, T)$-smooth, then we can avoid a dependence of the sample complexity, to achieve error $\alpha$, on the exponential of the dimensionality $p$ with base $1/\alpha$ (i. e., $\alpha^{-p}$), which answers a question in [smith 2017 interaction].
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning
In this paper, we revisit the large-scale constrained linear regression problem and propose faster methods based on some recent developments in sketching and optimization.
Deep Extreme Feature Extraction: New MVA Method for Searching Particles in High Energy Physics
Experimental data shows that, DEFE is able to train an ensemble of discriminative feature learners that boosts the overperformance of final prediction.
k-Prototype Learning for 3D Rigid Structures
In this paper, we study the following new variant of prototype learning, called {\em $k$-prototype learning problem for 3D rigid structures}: Given a set of 3D rigid structures, find a set of $k$ rigid structures so that each of them is a prototype for a cluster of the given rigid structures and the total cost (or dissimilarity) is minimized.
Gauging Association Patterns of Chromosome Territories via Chromatic Median
In this paper, we introduce a novel algorithmic tool for investigating association patterns of chromosome territories in a population of cells.
