Search Results for author:
Found 37 papers, 5 papers with code
Graph-based Aspect Representation Learning for Entity Resolution
Entity Resolution (ER) identifies records that refer to the same real-world entity.
Fast OSCAR and OWL with Safe Screening Rules
Ordered Weight $L_{1}$-Norms (OWL) is a new family of regularizers for high-dimensional sparse regression.
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm
Based on that, we propose a novel and practical VFL framework with black-box models, which is inseparably interconnected to the promising properties of ZOO.
Multi-Level Contrastive Learning for Cross-Lingual Alignment
Cross-language pre-trained models such as multilingual BERT (mBERT) have achieved significant performance in various cross-lingual downstream NLP tasks.
A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse
Combining our new formulation with the alternative update of the inner and outer variables, we propose an efficient fully single loop algorithm.
Efficient Semi-Supervised Adversarial Training without Guessing Labels
To adapt to semi-supervised learning problems, they need to estimate labels for unlabeled data in advance, which inevitably degenerates the performance of the learned model due to the bias on the estimation of labels for unlabeled data.
Perturbation Diversity Certificates Robust Generalisation
It is possibly due to the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way, so that the adversarial samples are highly biased towards the decision boundary, resulting in an inhomogeneous data distribution.
Efficient Bi-level Optimization for Non-smooth Optimization
Then, we transform the smoothed bi-level optimization to an unconstrained penalty problem by replacing the smoothed sub-problem with its first-order necessary conditions.
Accelerated Gradient-Free Method for Heavily Constrained Nonconvex Optimization
In this paper, to solve the nonconvex problem with a large number of white/black-box constraints, we proposed a doubly stochastic zeroth-order gradient method (DSZOG).
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, under which three algorithms, i. e. AsySQN-SGD, -SVRG and -SAGA, are proposed.
An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm for First-order and Zeroth-order Optimization
The conditional gradient algorithm (also known as the Frank-Wolfe algorithm) has recently regained popularity in the machine learning community due to its projection-free property to solve constrained problems.
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients
Although a wide range of researches have been done in recent years to improve the adversarial robustness of learning models, but most of them are limited to deep neural networks (DNNs) and the work for kernel SVM is still vacant.
Topic Classification on Spoken Documents Using Deep Acoustic and Linguistic Features
In this paper, instead of using the ASR transcripts, the fusion of deep acoustic and linguistic features is used for topic classification on spoken documents.
Learning Sampling Policy for Faster Derivative Free Optimization
To explore this problem, in this paper, we propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling.
Bidirectional Multiscale Feature Aggregation for Speaker Verification
In this paper, we propose a novel bidirectional multiscale feature aggregation (BMFA) network with attentional fusion modules for text-independent speaker verification.
Improved Meta-learning training for Speaker Verification
By combining these two methods, we can observe further improvements on these two databases.
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage.
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability.
Improved Bilevel Model: Fast and Optimal Algorithm with Theoretical Guarantee
In this paper, we propose an improved bilevel model which converges faster and better compared to the current formulation.
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data
In this paper, we focus on nonlinear learning with kernels, and propose a federated doubly stochastic kernel learning (FDSKL) algorithm for vertically partitioned data.
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning
To the best of our knowledge, AFSGD-VP and its SVRG and SAGA variants are the first asynchronous federated learning algorithms for vertically partitioned data.
Fast OSCAR and OWL Regression via Safe Screening Rules
Moreover, we prove that the algorithms with our screening rule are guaranteed to have identical results with the original algorithms.
Faster On-Device Training Using New Federated Momentum Algorithm
Mobile crowdsensing has gained significant attention in recent years and has become a critical paradigm for emerging Internet of Things applications.
Large Batch Training Does Not Need Warmup
Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications.
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization
Semi-supervised ordinal regression (S$^2$OR) problems are ubiquitous in real-world applications, where only a few ordered instances are labeled and massive instances remain unlabeled.
Safe Sample Screening for Robust Support Vector Machine
To address this challenge, in this paper, we propose two safe sample screening rules for RSVM based on the framework of concave-convex procedure (CCCP).
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization
To address this problem, in this paper, we propose a novel scalable quadruply stochastic gradient algorithm (QSG-S2AUC) for nonlinear semi-supervised AUC optimization.
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Specifically, to handle two types of data instances involved in S$^3$VM, TSGS$^3$VM samples a labeled instance and an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient.
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization
Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems.
Training Neural Networks Using Features Replay
Training a neural network using backpropagation algorithm requires passing error gradients sequentially through the network.
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Asynchronous parallel stochastic gradient optimization has been playing a pivotal role to solve large-scale machine learning problems in big data applications.
Decoupled Parallel Backpropagation with Convergence Guarantee
The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully leveraging the computing resources.
Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization
To the best of our knowledge, our method admits the fastest convergence rate for stochastic composition optimization: for strongly convex composition problem, our algorithm is proved to admit linear convergence; for general composition problem, our algorithm significantly improves the state-of-the-art convergence rate from $O(T^{-1/2})$ to $O((n_1+n_2)^{{2}/{3}}T^{-1})$.
Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization
The theoretical results show that our inexact proximal gradient algorithms can have the same convergence rates as the ones of exact proximal gradient algorithms in the non-convex setting.
Zeroth-order Asynchronous Doubly Stochastic Algorithm with Variance Reduction
The convergence rate of existing asynchronous doubly stochastic zeroth order algorithms is $O(\frac{1}{\sqrt{T}})$ (also for the sequential stochastic zeroth-order optimization algorithms).
Asynchronous Stochastic Block Coordinate Descent with Variance Reduction
In this paper, we focus on a composite objective function consisting of a smooth convex function $f$ and a block separable convex function, which widely exists in machine learning and computer vision.
Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction
In this paper, we propose a faster method, decoupled asynchronous proximal stochastic variance reduced gradient descent method (DAP-SVRG).
