arXiv research

This paper addresses privacy issues in personalized pricing using nonparametric demand models.

Paper develops privacy-preserving dynamic pricing policy for e-commerce.

Generative framework unifies and improves personalized learning and estimation methods.

PGFL framework learns personalized models with differential privacy.

This paper protects rankings from differential privacy breaches.

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully…

Study improves privacy in cross-silo federated learning by personalizing data.

Private RL algorithm with privacy guarantees for personalized medicine decisions.

This paper improves privacy and fairness in federated learning by protecting sensitive data and ensuring group fairness.

New algorithms protect user data while optimizing personalized decisions.

A new method for tighter privacy loss accounting in adaptive analyses.

FMP improves personalized ASR models on private devices.

Paper improves privacy-accuracy balance in federated learning.

Private mean estimation with multiple samples requires a certain number of people to maintain privacy.

The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to pri…

Paper proposes FedPer to combat statistical heterogeneity in federated learning for personalized tasks.

Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's p…

New algorithm for personalized healthcare with privacy guarantees.

The paper analyzes and improves privacy in machine learning through importance sampling.

Proposes first privacy-preserving method for estimating Hawkes processes.

DP-CDA generates synthetic data to enhance privacy in high-dimensional datasets.

LP-FT improves personalized model training in FL by balancing generalization and personalization.

FGPR uses averaging and SGD for federated $\mathcal{GP}$ regression, excelling in personalization and multi-fidelity modeling.

Collaborative personalization, such as through learned user representations (embeddings), can improve the prediction accuracy of neural-network-based models significantly. We propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and resource-efficient way to utilize existing neur…

This paper enhances privacy-preserving randomized power method for large datasets.

FedLog reduces communication in federated learning by sharing data summaries.

Differentially private graph learning via bounded sensitivity PPR.

Protects user privacy in models using optional personal data.

New method preserves privacy while improving machine learning accuracy.

In machine learning, classification models need to be trained in order to predict class labels. When the training data contains personal information about individuals, collecting training data becomes difficult due to privacy concerns. Local differential privacy is a definition to measure the individual privacy when th…

Voice-enabled interactions provide more human-like experiences in many popular IoT systems. Cloud-based speech analysis services extract useful information from voice input using speech recognition techniques. The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional sta…

Improved privacy in RL with near-optimal regret bounds.

The paper analyzes and proposes methods for privately sharing individual privacy losses using per-instance differential privacy.

Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personali…

Study proposes BFEL framework for privacy-preserving FL in personalized healthcare.

Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.

A new algorithm improves federated learning by combining knowledge distillation and weighted combination loss.

This paper surveys techniques to personalize federated learning models.

User releases data to service provider while balancing privacy and utility.

A new method models user-specific parameters as a low-rank plus sparse component for efficient personalization.

Missing data enhances privacy in differential privacy.

A new DP algorithm for weighted ERM protects sensitive data in predictive models.

This work addresses privacy issues in IoT data sharing by balancing information disclosure and user privacy.

The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model upd…

Federated approach protects privacy while learning user patterns.

Group personalization improves FL performance in heterogeneous client data.

AI platforms disrupt investment by personalizing deal sourcing and insights.

Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to hav…