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…
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48 results for “personalized machine learning”
Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of th…
The emerging paradigm of federated learning strives to enable collaborative training of machine learning models on the network edge without centrally aggregating raw data and hence, improving data privacy. This sharply deviates from traditional machine learning and necessitates the design of algorithms robust to variou…
This paper surveys techniques to personalize federated learning models.
problem Personalized models outperform shared models for some clients, reducing participation.
method Surveys recent research on personalizing federated learning models.
result Personalization techniques improve model performance for individual clients.
PerSense assesses personality traits from text for commonsense reasoning.
problem Estimating human personality traits from text for mental health analysis.
method Aggregated Probability Density Functions (PDF) and Machine Learning (ML) models.
result PerSense algorithms achieve comparable results to ground truth data, with high accuracy for personality assessment and commonsense prediction.
PsychFM predicts individual gambling choices using psychological and machine learning models.
problem Predicting individual gambling choices with high precision.
method PsychFM combines machine learning and psychological theories.
result PsychFM outperforms existing models like random forest and factorization machines.
Study shows how online personalization can lead to unfair models due to biased user responses.
problem Fairness issues in online personalization systems due to biased user responses.
method Formulated a regularization-based approach to mitigate biases in machine learning models.
result Demonstrated that online personalization can cause models to learn unfair behavior from biased user responses.
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…
We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we i…
Personalized models using group attributes reduce performance, study finds.
problem Reducing performance of models using group attributes like race or gender.
method Formal conditions and collective preference guarantees to ensure fair use.
result Models personalized with group attributes reduce performance at a group level.
pFedMe uses Moreau envelopes to improve personalized FL performance.
problem Statistical diversity among clients limits FL performance.
method pFedMe employs Moreau envelopes as clients' regularized loss functions in a bi-level optimization problem.
result pFedMe achieves state-of-the-art convergence rates and superior empirical performance.
Survey on ML advances for personalized prediction considering entity characteristics.
problem Sub-optimal performance in personalized prediction due to heterogeneity in data across entities.
method Organized current literature on entity-aware modeling based on characteristics availability and training data amount.
result Recent innovations in uncertainty quantification, fairness, and knowledge-guided machine learning can improve entity-aware modeling.
FedSplit improves federated learning for heterogeneous data.
problem Data heterogeneity in federated learning degrades convergence and performance.
method FedSplit splits data into shared and personalized groups, optimizing a novel objective function.
result FedSplit converges faster and performs better than standard federated learning.
AI framework uses multi-omics data to personalize cancer treatment suggestions.
problem Leveraging AI for personalized cancer treatment based on complex patient characteristics.
method Modular machine learning framework trained on diverse multi-omics technologies.
result Superior performance in personalized counterfactual treatment suggestions.
The standard objective in machine learning is to train a single model for all users. However, in many learning scenarios, such as cloud computing and federated learning, it is possible to learn a personalized model per user. In this work, we present a systematic learning-theoretic study of personalization. We propose a…
A novel Federated Learning scheme using Optimal Transport for personalized model training.
problem Training models with data from clients having non-identically distributed data.
method Personalized Federated Learning scheme based on Optimal Transport (FedOT).
result FedOT scheme effectively transfers data from multiple distributions to a common domain and optimizes the prediction model.
AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunitiesstat.ME
AI enhances personalized drug development and decision-making in pharma.
problem Traditional drug development lacks personalized treatment plans.
method Application of AI in drug discovery, clinical trials, and post-marketing assessment.
result AI improves personalized medicine, optimizing health outcomes.
Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a pract…
Enhances demand models with deep learning for personalized pricing.
problem Capturing rich heterogeneity in demand models.
method Integrates deep neural networks into structural models with economic structure.
result Captures rich heterogeneity and creates personalized pricing.
We consider the fully decentralized machine learning scenario where many users with personal datasets collaborate to learn models through local peer-to-peer exchanges, without a central coordinator. We propose to train personalized models that leverage a collaboration graph describing the relationships between user per…
The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, th…
Causal ML predicts treatment outcomes, aiding personalized medicine.
problem Predicting individualized treatment effects for personalized medicine.
method Flexible, data-driven methods using causal inference with clinical trial and real-world data.
result Causal ML allows for estimating individualized treatment effects.
This study optimizes model averaging for personalized collaborative learning.
problem Differences in data or objectives between nodes in federated learning.
method Weighted averaging between local and global models for scalar mean estimation.
result There is always some positive model averaging that reduces expected squared error.
Causal ML methods failed to validate their personalized treatment effects in two large trials.
problem Validating causal machine learning methods for personalized treatment effects in precision medicine.
method Assessed 17 mainstream causal heterogeneity ML methods using two large randomized controlled trials.
result None of the ML methods reliably validated their performance, internal or external, showing significant discrepancies between training and test data.
LotteryFL improves federated learning efficiency and personalization.
problem Statistical heterogeneity and communication efficiency in federated learning.
method Exploiting Lottery Ticket hypothesis to learn compact lottery networks.
result LotteryFL achieves better personalization and reduces communication cost.
Simplifies complex pricing models for better interpretability and revenue.
problem Complex pricing models are hard to interpret and not widely adopted.
method Model distillation to create interpretable pricing policies.
result Maximizes revenue while maintaining interpretability.
DNA sequencing to identify genetic variants is becoming increasingly valuable in clinical settings. Assessment of variants in such sequencing data is commonly implemented through Bayesian heuristic algorithms. Machine learning has shown great promise in improving on these variant calls, but the input for these is still…
Vanguard uses AI to create personalized financial plans.
problem Challenges in choosing features for complex financial planning.
method Reinforcement learning for identifying optimal savings rates.
result Trains algorithms to model financial success trajectories.
A new method models user-specific parameters as a low-rank plus sparse component for efficient personalization.
problem Efficient personalization of machine learning models for individual users.
method Meta-learning approach that models network weights as a sum of low-rank and sparse components.
result The proposed method, AMHT-LRS, achieves nearly optimal sample complexity for estimating the low-rank and sparse components.
Federated MTL learns personalized models under mixed distributions.
problem Heterogeneity of local data distributions leads to poor global model performance.
method Proposes federated MTL under mixture of distributions, using penalized optimization and federated EM-like algorithms.
result Models with higher accuracy and fairness than state-of-the-art methods.
Paper proposes personalized climate control for driver comfort.
problem Limited research on in-vehicle climate control and driver preferences.
method IoT platform for data collection, machine learning for driver behavior recognition, and personalized preference recommendation.
result Prototype demonstrates effective and accurate climate control for driver comfort.
Generative models create personalized patient health simulations.
problem Creating accurate digital twins for personalized medicine.
method Neural network architecture for conditional generative models of clinical trajectories.
result Same architecture generates accurate twins across 13 indications.
There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide…
A signature-based machine learning model for bipolar disorder and borderline personality disorderstat.ML
Mobile technologies offer opportunities for higher resolution monitoring of health conditions. This opportunity seems of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, getting actionable information from these rather complex time series is cha…
A new algorithm improves federated learning by combining knowledge distillation and weighted combination loss.
problem Non-IID client data in federated learning leads to model drift and poor generalization.
method pFedKD-WCL integrates knowledge distillation with bi-level optimization to address non-IID challenges.
result pFedKD-WCL outperforms state-of-the-art algorithms in accuracy and convergence speed.
POSL is an online learning algorithm for personalized predictions.
problem Real-time personalized predictions for streaming data.
method Online Super Learner algorithm that optimizes predictions with respect to baseline covariates.
result POSL provides reliable predictions and adapts to changing data environments.
FOCaL meta-learner estimates functional treatment effects robustly.
problem Estimating heterogeneous treatment effects from functional outcomes.
method Doubly robust meta-learner FOCaL integrating functional regression.
result Direct and robust estimation of F-CATE.
This paper improves privacy and fairness in federated learning by protecting sensitive data and ensuring group fairness.
problem Privacy and fairness issues in federated learning.
method Introduces group privacy through -privacy, a localized form of differential privacy.
result The method provides better group fairness than a global model in federated learning.
Study uses machine learning to predict future health from various health data types.
problem Predicting future health using diverse health data types.
method Applied machine learning (neural networks and XGBoost) to longitudinal data from 6830 individuals.
result Health-related measures were the strongest predictors of future health status, while genetic data performed poorly.
Who will dropout from university? Academic risk prediction based on interpretable machine learningcs.LG
Predicts academic risk in college students using interpretable machine learning.
problem Predicting academic risk from high-dimensional, unbalanced student data.
method Binary classification task using LightGBM model and Shapley value.
result 8 predictors for academic risk identified, including quality of academic partners and dormitory study atmosphere.
In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient po…
Protects user privacy in models using optional personal data.
problem Ensuring fairness for users who opt-out of data sharing.
method Formalizes protection requirements, introduces Protected User Consent (PUC), devises data augmentation strategy.
result PUC-compliant models can improve performance without disadvantaging opt-out users.
Adaptive personalized federated learning improves local model personalization.
problem Maximizing global model performance limits local model personalization.
method APFL algorithm trains local models while contributing to global model, with optimal mixing parameter and communication-efficient optimization.
result Demonstrates effectiveness of personalization schema and correctness of generalization theories.
Paper improves privacy-accuracy balance in federated learning.
problem Privacy-accuracy tradeoffs in federated learning.
method Personalized federated learning with joint differential privacy.
result Coordination of local and centralized learning improves accuracy while maintaining privacy.
Clust-PSI-PFL uses PSI to improve accuracy and fairness in federated learning.
problem Non-IID data biases federated learning performance.
method Clust-PSI-PFL uses clustering and PSI to form homogeneous groups of clients.
result Clust-PSI-PFL delivers up to 18% higher global accuracy and improves client fairness.
A Language-independent and Compositional Model for Personality Trait Recognition from Short Textscs.CL
Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, …
We demonstrate that effortlessly accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits including: life satisfaction, cultural ethnicity, political views, age, gender and personality traits. The analysis pre…
Smartwatches are increasingly being used to recognize human daily life activities. These devices may employ different kind of machine learning (ML) solutions. One of such ML models is Gradient Boosting Machine (GBM) which has shown an excellent performance in the literature. The GBM can be trained on available data set…