A new federated multi-armed bandit framework with personalization balances generalization and personalization.
problem Balancing generalization and personalization in federated multi-armed bandits.
method Proposed a Personalized Federated Upper Confidence Bound (PF-UCB) algorithm to achieve a O(log(T)) regret. result PF-UCB achieves an O(log(T)) regret regardless of personalization degree and has similar instance dependency to lower bound. 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.
Model predicts personality traits from Facebook Likes.
problem Predicting personality traits from social media data.
method Mapped Facebook Likes to page categories, used machine learning.
result 83% accuracy distinguishing religious vs non-religious.
The paper compares clustering techniques for personalized food kits.
problem Designing food kits that match individual consumer preferences.
method Comparative analysis of clustering techniques using conjoint analysis data.
result SVD clustering outperforms k-means in personalized food kit design.
A new personality-based recommender system tackles data sparsity without feedback.
problem Data sparsity without common feedback among users.
method Implicitly identifying users' personality type and incorporating it with personal interests and knowledge level.
result The model's effectiveness, especially in data sparsity situations, demonstrated on a real-world dataset.
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.
This paper analyzes the trade-off between accuracy and communication in personalized federated learning.
problem The accuracy-communication trade-off in personalized federated learning.
method The paper provides a quantitative characterization of the personalization degree on the trade-off, establishing minimax optimality.
result The paper offers theoretical insights for choosing the personalization degree and validates the results on synthetic and real-world datasets.
A new system combines vision and language for person re-identification.
problem Real-world surveillance lacks visual data for person re-identification.
method Two-stream CNN framework with shared logits, CCA for modalities, multi-modal testing protocol.
result 22% improvement in re-identification performance with multi-modal queries.
New framework analyzes LLM personalization trade-offs under congestion.
problem Tension between personalization and resource sharing in LLMs.
method Developed a statistical-economic framework to model user incentives.
result Congestion can flip rankings of SFT and ICL, and offers both methods never hurt profits.
LP-FT improves personalized model training in FL by balancing generalization and personalization.
problem Federated Learning struggles with balancing global generalization and local personalization due to non-identical data distributions.
method Adapting Linear Probing followed by full Fine-Tuning (LP-FT) to the FL setting.
result LP-FT outperforms standard fine-tuning in balancing personalization and generalization across various datasets and PFT variants.
Decentralized learning of personalized models and collaboration graphs without central coordination.
problem Training personalized models and collaboration graphs in a decentralized manner without a central coordinator.
method Alternates between training models given the graph and updating the graph given the models, using peer-to-peer exchanges.
result Communication-efficient approach that avoids exchanging personal data, with benefits demonstrated on synthetic and real datasets.
Develops c-GNF for personalized social science policy analysis.
problem Challenges in estimating causal effects and counterfactual inference in social sciences.
method causal-Graphical Normalizing Flow (c-GNF) method.
result c-GNF performs well in estimating causal effects and counterfactual inference.
Study shows auditing fairness of personalized interventions is impossible due to unknown ground truths.
problem Auditing fairness of personalized interventions in social services, education, and healthcare.
method Point-identification of quantities under monotone treatment response assumption, providing sensitivity analysis for violations.
result Proves impossibility of auditing fairness using standard metrics and provides methods for auditing using partially-identified ROC and xROC curves.
PCL tackles collaborative learning for diverse agents, reducing sample complexity.
problem Balancing collaborative speedup with personalization for heterogeneous agents.
method AffPCL, with bias and importance correction mechanisms.
result AffPCL reduces sample complexity by a factor of max{n−1,δ}, where n is the number of agents and δ∈[0,1] measures heterogeneity. DeepCoDA provides personalized interpretability for complex health data.
problem Interpreting complex health data, especially compositional data, is challenging.
method DeepCoDA framework for high-dimensional compositional data, personalized interpretability through patient-specific weights.
result DeepCoDA maintains state-of-the-art performance and provides coherent, personalized interpretations.
Study uses machine learning to detect pain from brain signals.
problem No validated objective measure of pain exists.
method Multi-task multiple kernel learning for personalized pain recognition.
result Supports use of fNIRS and machine learning for objective pain detection.
Study identifies personality traits from dance movements in music.
problem Predicting individual differences from music-induced movement.
method Identified Big Five personality traits and EQ/SQ scores from dance movements.
result Successfully explored unseen space for personality and EQ/SQ.
PGFL framework learns personalized models with differential privacy.
problem Privacy-preserving personalized learning for diverse data.
method Exploits model similarities and differential privacy (zero-concentrated).
result Algorithm converges to optimal solutions with linear time complexity.
Develops a method to learn personalized attribute preferences from diverse annotators.
problem Learning attribute preferences from a wide spectrum of annotators with varying interests.
method Multi-task approach with AUC-based loss function and closed-form solution.
result Empirically validated method outperforms traditional consensus-based methods.
A new method for online personalized learning reduces gradient variance by dynamically selecting peers.
problem Online personalized decentralized learning with statistically heterogeneous clients.
method Gradient-based collaboration criterion allowing clients to dynamically select peers with similar gradients.
result The method acts as a variance reduction method, achieving optimal performance in certain conditions.
Boosting-inspired framework for federated learning with progressive model personalization.
problem Statistical heterogeneity across clients in federated learning.
method Construct an ensemble of personalized models, progressively increasing the depth of the personalized component while controlling its effective complexity.
result Consistently outperforms state-of-the-art PFL methods under heterogeneous data distributions.
Proposes a method to learn from historical data for personalized decision-making.
problem Sample hunger in sequential decision-making algorithms for personalized medicine.
method Identifiable latent bandit framework using nonlinear independent component analysis.
result Optimal decision-making with shorter exploration time than classical bandits.
Much has been said lately about entrepreneurship, so it seems important to leave here some personal analysis on this topic. The issues outlined here result from a work in about a year in which because a personal and professional obligations it was doing some research on these issues. This is an interesting topic that h…
Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorizatio…
We design, conduct and present the results of a highly personalized baseline emotion recognition experiment, which aims to set reliable ground-truth estimates for the subject's emotional state for real-life prediction under similar conditions using a small number of physiological sensors. We also propose an adaptive st…
The paper develops methods to optimize personalized policies in complex causal pathways.
problem Optimizing policies in healthcare settings with entangled causal effects.
method Combining mediation analysis and dynamic treatment regime ideas for longitudinal data.
result Derived methods for learning high-quality policies from observational data.
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.
This paper addresses privacy issues in personalized pricing using nonparametric demand models.
problem Privacy violation in personalized pricing algorithms with unknown nonparametric demand models.
method Develops algorithms to make pricing decisions and learn demand while ensuring central and local differential privacy.
result Proves near-optimal regret bounds for algorithms with CDP and LDP guarantees.
PhysioMTL learns personalized HRV rhythms from wearable data, improving prediction and counterfactual analysis.
problem Challenges in interpreting HRV measurements due to variability and stressors.
method PhysioMTL combines Optimal Transport and MTL to learn personalized diurnal rhythms from heterogeneous data.
result PhysioMTL outperforms other methods in predicting unseen subjects and generating counterfactual effects.
A new method for tighter privacy loss accounting in adaptive analyses.
problem Ensuring individual privacy in adaptive analyses while staying within a privacy budget.
method A personalized privacy loss estimate and a Rényi differential privacy filter.
result Personalized privacy loss accounting can be practical and tighter than existing methods.
Personalized time series models improve drug response prediction.
problem Fitting time series models to cohorts ignoring individual variation.
method Multi-task learning applied to time series data with and without control inputs.
result Improved predictive accuracy and uncertainty estimation.
ConCare personalizes healthcare predictions by capturing EMR features.
problem Predicting patient outcomes from EMR data with personalization.
method Captures personal characteristics and time-aware distribution in EMR data.
result Improves healthcare prediction accuracy through personalized health context.
The paper explores how machine learning personalization aligns with humanistic views of the person.
problem Aligning machine learning personalization with humanistic views of the person.
method Explicating the concept of personalization and contrasting it with humanistic views of the person.
result Proposes dimensions for evaluating the degree of personalization of ML personalized scores.
Private mean estimation with multiple samples requires a certain number of people to maintain privacy.
problem Private mean estimation with person-level differential privacy for multiple samples.
method The approach involves estimating the mean up to a distance α in ℓ_2-norm under ε-differential privacy, using algorithms based on the clip-and-noise framework and new analyses.
result The necessary and sufficient number of people to estimate the mean up to distance α in ℓ_2-norm is given by a specific formula.
Proposes a deep spectral Q-learning for mobile health data.
problem Personalized treatment assignment for patients with time-varying covariates.
method Integrates PCA with deep Q-learning for mixed frequency data.
result Mean return converges to optimal under estimated optimal policy.
SPIRE enables efficient federated learning for diffusion models by separating client-specific embeddings from a shared backbone.
problem Large diffusion models are impractical for federated learning due to their size.
method SPIRE separates the network into a global backbone and client-specific embeddings, enabling efficient finetuning.
result SPIRE achieves parameter-efficient finetuning, updating only a small fraction of weights.
Study personalizes user experience to maximize rewards with patience budget.
problem Maximizing rewards for a platform while respecting user patience.
method Proposes bandit algorithms for sequential choice with feedback models.
result Upper and lower bounds on regret of order O(N2/3) and Ω(N2/3). Optimizes recommendation models using skew normal distribution.
problem Improving personalized recommendation systems.
method Develops a new optimization criterion based on skew normal distribution.
result Significantly outperforms state-of-the-art models.
We investigate the shape of the Italian personal income distribution using microdata from the Survey on Household Income and Wealth, made publicly available by the Bank of Italy for the years 1977--2002. We find that the upper tail of the distribution is consistent with a Pareto-power law type distribution, while the r…
Federated learning framework extended for personalizing global models.
problem Evaluate personalization strategies for global models on-device.
method Extend federated learning framework, develop tools for analysis and evaluation.
result Personalization yields significant benefits for a large user population.
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.
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
Paper uses language models to predict MBTI personality types with high accuracy.
problem Predicting Myers-Briggs personality types from text.
method Fine-tuned BERT model for predicting MBTI types and generating personality-specific language.
result BERT model achieves high accuracy in predicting MBTI types and personality-specific language generation.
Study uses deep learning to quickly estimate tissue properties for personalized radio-frequency dosimetry.
problem Time-consuming tissue segmentation limits personalized radio-frequency dosimetry.
method Developed a learning-based approach using Convolutional Neural Networks (CNN) for magnetic resonance images.
result Smooth distribution of dielectric properties improves SAR distribution consistency.
Personalized explanations improve understanding of machine learning models.
problem Improving human understanding of machine learning models and decisions.
method Deriving a conceptualization of personalized explanation, categorizing explainee data, identifying key properties, and introducing new measures.
result Identification of three key properties amendable to personalization: complexity, decision information, and presentation.
The paper tackles personalized policy learning from diverse data sources in a federated setting.
problem Learning personalized decision policies from observational bandit feedback across multiple heterogeneous data sources.
method Introduces a novel regret analysis for distinguishing global and local regret, and presents a federated policy learning algorithm using local policies trained with doubly robust offline policy evaluation strategies.
result Establishes finite-sample upper bounds on global and local regret, characterizing them by source heterogeneity and distribution shift.
Deep learning improves trip prediction accuracy in transportation planning.
problem Traditional models fail to accurately predict person and vehicle trips due to complexity and dynamics.
method Developed and trained a deep learning model using NHTS data.
result Deep learning model achieved 98% accuracy for person trip prediction and 96% for vehicle trip estimation.
New algorithms improve efficiency in learning from personalized rewards.
problem Learning from personalized rewards in recommendation systems.
method Developed provably efficient algorithms with sublinear regret for context-dependent feedback.
result Introduced a Lipschitz reward estimator that improves generalization performance.