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Found 17 papers, 6 papers with code
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
In this paper, we focus on the problem of exactly recovering the communities in a binary symmetric SBM, where a graph of $n$ vertices is partitioned into two equal-sized communities and the vertices are connected with probability $p = \alpha\log(n)/n$ within communities and $q = \beta\log(n)/n$ across communities for some $\alpha>\beta>0$.
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR).
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks
Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints.
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description.
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research.
Knowledge-Injected Federated Learning
Federated learning is an emerging technique for training models from decentralized data sets.
Revealing Unfair Models by Mining Interpretable Evidence
The key idea is to find solid evidence in the form of a group of data instances discriminated most by the model.
Fair and efficient contribution valuation for vertical federated learning
We show that VerFedSV not only satisfies many desirable properties for fairness but is also efficient to compute, and can be adapted to both synchronous and asynchronous vertical federated learning algorithms.
Improving Fairness for Data Valuation in Horizontal Federated Learning
The success of federated learning depends largely on the participation of data owners.
Achieving Model Fairness in Vertical Federated Learning
To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds.
FedFair: Training Fair Models In Cross-Silo Federated Learning
In this paper, we first propose a federated estimation method to accurately estimate the fairness of a model without infringing the data privacy of any party.
An Optimal Resource Allocator of Elastic Training for Deep Learning Jobs on Cloud
Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs.
Personalized Cross-Silo Federated Learning on Non-IID Data
Non-IID data present a tough challenge for federated learning.
Non-Convex Exact Community Recovery in Stochastic Block Model
Community detection in graphs that are generated according to stochastic block models (SBMs) has received much attention lately.
A Unified Approach to Error Bounds for Structured Convex Optimization Problems
In this paper, we present a new framework for establishing error bounds for a class of structured convex optimization problems, in which the objective function is the sum of a smooth convex function and a general closed proper convex function.
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization
Motivated by various applications in machine learning, the problem of minimizing a convex smooth loss function with trace norm regularization has received much attention lately.
