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Found 50 papers, 18 papers with code
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
We leverage COPA to certify three RL environments trained with different algorithms and conclude: (1) The proposed robust aggregation protocols such as temporal aggregation can significantly improve the certifications; (2) Our certification for both per-state action stability and cumulative reward bound are efficient and tight; (3) The certification for different training algorithms and environments are different, implying their intrinsic robustness properties.
Vertical Federated Learning: Challenges, Methodologies and Experiments
As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients.
On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems
Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to each individual user's local data distribution.
Federated Submodel Averaging
We theoretically proved the convergence rate of FedSubAvg by deriving an upper bound under a new metric called the element-wise gradient norm.
Normal Learning in Videos with Attention Prototype Network
With models trained on the normal data, the reconstruction errors of anomalous scenes are usually much larger than those of normal ones.
Data-Free Evaluation of User Contributions in Federated Learning
We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset.
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis
We show that these DP GCN mechanisms are not always resilient against LinkTeller empirically under mild privacy guarantees ($\varepsilon>5$).
Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack
To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects.
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
We then develop a local smoothing algorithm for policies derived from Q-functions to guarantee the robustness of actions taken along the trajectory; we also develop a global smoothing algorithm for certifying the lower bound of a finite-horizon cumulative reward, as well as a novel local smoothing algorithm to perform adaptive search in order to obtain tighter reward certification.
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.
Implicit Regularization in Matrix Sensing via Mirror Descent
We study discrete-time mirror descent applied to the unregularized empirical risk in matrix sensing.
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives.
Nearly Minimax-Optimal Rates for Noisy Sparse Phase Retrieval via Early-Stopped Mirror Descent
This paper studies early-stopped mirror descent applied to noisy sparse phase retrieval, which is the problem of recovering a $k$-sparse signal $\mathbf{x}^\star\in\mathbb{R}^n$ from a set of quadratic Gaussian measurements corrupted by sub-exponential noise.
Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields.
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
In this paper, we aim to explore the power of generative models and gradient sparsity, and propose a scalable privacy-preserving generative model DATALENS.
Game-based Pricing and Task Offloading in Mobile Edge Computing Enabled Edge-Cloud Systems
In this paper, we formulate a distributed mechanism to analyze the interaction between OSPs and IoT MDs in the MEC enabled edge-cloud system by appling multi-leader multi-follower two-tier Stackelberg game theory.
Edge-computing
Computer Science and Game Theory
FamDroid: Learning-Based Android Malware Family Classification Using Static Analysis
At present, the main problem of existing research works on Android malware family classification lies in that the extracted features are inadequate to represent the common behavior characteristics of the malware in malicious families, and leveraging a single classifier or a static ensemble classifier is restricted to further improve the accuracy of classification.
Malware Detection
Cryptography and Security
Direct Observation of Thermalization to a Rayleigh-Jeans Distribution in Multimode Optical Fibers
Recent years have witnessed a resurgence of interest in nonlinear multimode optical systems where a host of intriguing effects have been observed that are impossible in single-mode settings.
Optics
Toward Understanding the Influence of Individual Clients in Federated Learning
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server.
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
Factor Graph Neural Networks
Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks.
The Twelvefold Way of Non-Sequential Lossless Compression
Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions.
Information Theory Information Theory
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
We analyze continuous-time mirror descent applied to sparse phase retrieval, which is the problem of recovering sparse signals from a set of magnitude-only measurements.
Better Model Selection with a new Definition of Feature Importance
Feature importance aims at measuring how crucial each input feature is for model prediction.
IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
In this paper, we introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm to accelerate the overall optimisation process of tuning machine learning models in classification problems.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
Retrieve Synonymous keywords for Frequent Queries in Sponsored Search in a Data Augmentation Way
Firstly, we devise a Trie-based translation model to make a data increment.
Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning in 6G Networks
Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the core to the edge including end devices.
Networking and Internet Architecture
Hadamard Wirtinger Flow for Sparse Phase Retrieval
We consider the problem of reconstructing an $n$-dimensional $k$-sparse signal from a set of noiseless magnitude-only measurements.
Cryptocurrency Trading: A Comprehensive Survey
This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e. g., cryptocurrency trading systems, bubble and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others).
Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks
In cognitive radio (CR) networks, fast and accurate spectrum sensing plays a fundamental role in achieving high spectral efficiency.
Distributed Optimization over Block-Cyclic Data
We consider practical data characteristics underlying federated learning, where unbalanced and non-i. i. d.
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process.
Ascertaining price formation in cryptocurrency markets with DeepLearning
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world.
Online Pricing with Reserve Price Constraint for Personal Data Markets
The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption.
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates.
Secure Federated Submodel Learning
Nevertheless, the "position" of a client's truly required submodel corresponds to her private data, and its disclosure to the cloud server during interactions inevitably breaks the tenet of federated learning.
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities
Given the complete query, we fine tune a BERT embedding for estimating probabilities of a broad set of instances.
From Server-Based to Client-Based Machine Learning: A Comprehensive Survey
Another benefit is the bandwidth reduction because various kinds of local data can be involved in the training process without being uploaded.
Factor Graph Neural Network
Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks.
DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image
Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data.
A Multi-Agent-Based Rolling Optimization Method for Restoration Scheduling of Electrical Distribution Systems with Distributed Generation
A multi-agent-based rolling optimization method for EDS restoration scheduling is proposed in this paper.
Signal Processing
Global Semantic Consistency for Zero-Shot Learning
In this paper, we propose an end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes, to support effective zero-shot learning.
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Ranked #1 on
3D Face Reconstruction
on Florence
An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning
We propose an end-to-end approach to the natural language object retrieval task, which localizes an object within an image according to a natural language description, i. e., referring expression.
