A distributed framework protects privacy while maintaining fairness in machine learning.
problem Protecting personal demographic data while ensuring fair machine learning outcomes.
method A distributed framework with private third-party data communication, ensuring privacy and fairness.
result Four fair learning methods consistently outperform existing ones in fairness and accuracy across three real-world datasets.
Wide-AdGraph detects ads and trackers using a graph of resource requests.
problem Detecting and blocking ad trackers to protect user privacy.
method Combining a large-scale graph of resource requests from multiple websites to train a machine learning algorithm.
result High accuracy (96.1% biased, 90.9% unbiased) in detecting ads and trackers.
New algorithms improve signal processing in federated learning.
problem Efficiently process distributed signal samples with privacy and communication constraints.
method Proposes overpredictive signal approximations using convex optimization.
result Quantifies tradeoffs between communication cost, sampling rate, and approximation error.
TransNet improves community detection on target networks using privacy-preserved source networks.
problem Community detection on sensitive network data with privacy constraints.
method Spectral clustering framework leveraging locally distributed privacy-preserved auxiliary networks via randomized response and adaptive weighting.
result TransNet delivers strong gains in community detection across various privacy levels and heterogeneity patterns.
Secure neural network inference on untrusted platforms using holographic reduced representations.
problem Secure neural network inference on untrusted platforms.
method Connectionist Symbolic Pseudo Secrets using Holographic Reduced Representations (HRR).
result Empirical robustness to attack under various threat models.
We introduce CheckNet, a method for secure inference with deep neural networks on untrusted devices. CheckNet is like a checksum for neural network inference: it verifies the integrity of the inference computation performed by untrusted devices to 1) ensure the inference has actually been performed, and 2) ensure the i…
We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesia…
The paper shows supply chain features improve cyber risk prediction.
problem Predicting cyber risk from supply chain attributes.
method Machine learning, external supply chain features, AUC improvement.
result Supply chain network features improve AUC by 2.3%.
Sketching improves federated learning privacy without sacrificing performance or accuracy.
problem Privacy concerns in federated learning training.
method Sketching algorithms to enhance privacy.
result Sketching can provide strong privacy guarantees for federated learning.
We lay theoretical foundations for new database release mechanisms that allow third-parties to construct consistent estimators of population statistics, while ensuring that the privacy of each individual contributing to the database is protected. The proposed framework rests on two main ideas. First, releasing (an esti…
Asynchronous federated learning for vertically partitioned data improves efficiency and privacy.
problem Efficiently train models on vertically partitioned data without a trusted third party.
method Proposed AFSGD-VP and its SVRG and SAGA variants for asynchronous federated learning.
result AFSGD-VP and its variants achieve higher efficiency than synchronous algorithms.
We study the problem of portfolio insurance from the point of view of a fund manager, who guarantees to the investor that the portfolio value at maturity will be above a fixed threshold. If, at maturity, the portfolio value is below the guaranteed level, a third party will refund the investor up to the guarantee. In ex…
DADI framework dynamically discovers fair information using reinforcement learning.
problem Discovering fair information from third-party features with unknown objectives.
method Adversarial reinforcement learning agent that balances accuracy and fairness.
result Achieves group fairness by rewarding the agent with the adversary's loss.
Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers. Typical…
Lapse-supported life insurance exacerbates adverse selection risks.
problem Lapse-supported life insurance increases adverse selection costs.
method Modeling 'Term to 100' contracts and analyzing three methods of managing lapse surplus.
result Adverse selection losses can be almost unlimited under certain conditions.
We report on a technique based on multi-agent games which has potential use in the prediction of future movements of financial time-series. A third-party game is trained on a black-box time-series, and is then run into the future to extract next-step and multi-step predictions. In addition to the possibility of identif…
Paper proposes a new method to compute cryptocurrency prices securely.
problem Accurate price feeds without a third party.
method Algorithmic method to compute prices from potentially dishonest sources.
result The proposed method can report accurate prices even from dishonest sources.
This paper tackles task offloading in edge computing systems with dynamic interactions.
problem Dynamic interactions among mobile users in an edge computing system.
method Modelled as a non-cooperative stochastic game, transformed into a Markov decision process, and solved using online deep reinforcement learning.
result The proposed deep RL scheme balances age of information and energy consumption.
We show how to restructure the counterparty risk faced by the originator of a securitization or covered bond arising from an interest rate hedging swap assisted by a "one-way" collateral agreement. This risk emerges when the swap is negotiated between the special purpose vehicle and a third party that covers itself thr…
The atomic swap protocol allows for the exchange of cryptocurrencies on different blockchains without the need to trust a third-party. However, market participants who desire to hold derivative assets such as options or futures would also benefit from trustless exchange. In this paper I propose the atomic swaption, whi…
A digital euro protocol offers complete privacy and offline transactions using Groth-Sahai proofs.
problem Fragile digital payment solutions with privacy and offline transaction issues.
method Design and implementation of a Central Bank Digital Currency (CBDC) using Groth-Sahai zero-knowledge proofs.
result Complete privacy and offline transaction capability with retroactive double-spending detection.
Evidence acquisition costs influence disclosure behavior and preference.
problem How evidence acquisition costs affect disclosure behavior and preference.
method Analyzes sender-receiver interactions with covert and overt evidence acquisition, varying certification costs.
result Equilibria converge to the Pareto-worst free-learning equilibrium as costs vanish, and receivers prefer covert to overt acquisition.
Study privacy-utility trade-off in IoT time-series data sharing with RL.
problem Privacy concerns in IoT time-series data sharing with temporal correlations.
method Reformulated as MDP, solved with asynchronous actor-critic deep RL.
result Validated solution on synthetic and real GPS datasets.
This paper assesses risks in DeFi investments.
problem Risks in decentralized finance investments.
method Overview of DeFi components and risk quantification methodology.
result Proposes an allocation methodology to integrate and quantify risks.
Wrapper improves black-box model auditability and decision trustworthiness.
problem Lack of transparency and auditability in machine learning models used in complex applications.
method Integrates uncertainty measures into black-box models to enhance auditability and decision trustworthiness.
result Improves trust in machine learning models by providing actionable mechanisms to reject uncertain predictions.
In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream. Motivated by a scenario where learned representations are used by third parties with unknown objectives, we propose and explore adversarial representation learning as a natural method of ensuring those…
Federated Learning prioritizes client data contributions for better model quality.
problem Privacy concerns and reluctance to share private data in machine learning.
method Prioritizes client data contributions in Federated Learning by assigning scores based on defined criteria.
result The proposed approach yields a higher quality global model compared to standard Federated Learning.
The paper discusses building ETF risk models using a multilevel classification taxonomy.
problem Building accurate risk models for ETFs.
method First, build a multilevel classification taxonomy for ETFs. Then, use this taxonomy to define risk factors and build risk models.
result The approach can accurately classify and model ETF risks.
Paper finds a method to compute fair risk-sharing rules.
problem Finding a fair and understandable risk-sharing rule.
method Established a one-to-one correspondence with a fixed point approach.
result Fast numerical method for computing AFPO risk-sharing rules.
Orpheus simplifies deep learning deployment on edge devices.
problem Optimizing deep learning inference on edge devices for efficiency.
method Orpheus is a new framework with a small codebase, minimal dependencies, and easy integration.
result Preliminary results show the effectiveness of Orpheus for inference optimisations.
Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with var…
Study adversarial perturbations in classification, analyzing learning and certification.
problem Formal study of classification under adversarial perturbations from both learner and third-party perspectives.
method PAC-type semi-supervised learning framework, black-box certification under limited query budget, adversary analysis.
result Existence of a polynomial query complexity adversary implies the existence of a sample efficient robust learner.
Domain classification is the task of mapping spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants (IPDAs). This is a major component in mainstream IPDAs in industry. Apart from official domains, thousands of third-party domains are also created by ex…
A framework assesses the quality of crowdsourced weather data.
problem Quality control and assessment of crowdsourced weather data from third-party stations.
method Proposes a simple, scalable, and interpretable AI/Stats/ML framework to assess TPAWS data.
result Demonstrates the performance of the framework using synthetic and real data.
A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.
problem Real-world social networks have inter-community influence that is often overlooked in community-based IM approaches.
method Community-IM++ uses a heuristic based on community-based diffusion degree and progressive budgeting to model and prioritize cross-community diffusion.
result Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime than existing methods.
We introduce the concept of community trees that summarizes topological structures within a network. A community tree is a tree structure representing clique communities from the clique percolation method (CPM). The community tree also generates a persistent diagram. Community trees and persistent diagrams reveal topol…
Community detection improves stock market portfolio optimization.
problem Improving portfolio optimization in financial markets.
method Community detection in correlation-based networks of worldwide stock markets.
result Portfolios constructed using community detection outperform traditional methods.
We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, t…
Study exact community detection in k-community Gaussian mixtures with different intensities.
problem Community detection in k-community Gaussian mixtures with varying intensities.
method Explicitly find the threshold for exact recovery of maximum likelihood estimation.
result Threshold for exact recovery of maximum likelihood estimation is identified.
The paper introduces a method for detecting principal communities and embedding vertices.
problem Detecting and embedding vertices in graphs with community structure.
method Principal graph encoder embedding method that detects principal communities and produces vertex embeddings.
result The method successfully detects principal communities and produces accurate vertex embeddings.
Improved distributed learning with reduced communication costs.
problem Efficient communication in resource-constrained environments for distributed learning.
method Proposed a cost-effective partial communication protocol.
result Communication cost is reduced to O(logT), improving significantly on full communication. Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by…
New GNN model detects overlapping communities better than existing methods.
problem Detecting overlapping communities in graphs.
method Graph Neural Network (GNN) for overlapping community detection.
result The proposed GNN model outperforms existing baselines significantly.
MACC learns communication protocols by adapting counterfactual reasoning.
problem Credit assignment and non-stationarity in communication environments.
method Adapts counterfactual reasoning to overcome credit assignment and uses action policy and Q-function of other agents to handle non-stationarity.
result MACC outperforms state-of-the-art baselines in four scenarios.
A novel framework for adaptive multi-agent communication in reinforcement learning.
problem Manual specification of communication structures in multi-agent reinforcement learning.
method Learning Structured Communication (LSC) framework using hierarchical graph neural networks.
result Adaptive hierarchical formations and efficient message propagation among agents.
Paper proposes a fast method for approximate data deletion in generative models.
problem Efficient data deletion in unsupervised learning models is an open problem.
method Density-ratio-based framework for generative models, fast method for approximate data deletion, statistical test.
result Theoretical guarantees and empirical demonstrations of the proposed methods across various generative models.
A framework for multi-agent communication over noisy channels in reinforcement learning.
problem Effective communication between multiple agents in a noisy environment.
method A novel multi-agent partially observable Markov decision process (MA-POMDP) framework considering noisy communication channels.
result Jointly learned policies outperform separate learning of communication and decision making.
Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practioners to determine which method would be suitable to get insights into the structural information of…