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.
Background doesn't affect personality predictions in deep networks.
problem Understanding how background images influence personality attribution in deep learning models.
method Explicitly studied the effect of background images on personality prediction in deep residual networks, controlling for confounds.
result Adding background information to input decreases model performance for personality trait prediction.
Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators. However, the consensus might fail in settings, especially when a wide spectrum of annotators with different interests and comprehension about the attribute wor…
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applicatio…
Paper tackles variable-length, incomplete wearable sensor data to improve personalized insights.
problem Variable-length and incomplete time series data from wearable sensors.
method HeartSpace integrates a time series encoding module and pattern aggregation network, along with a Siamese-triplet network for representation learning.
result Empirical evaluation shows significant performance gains in personality prediction, demographics inference, and user identification.
Infer-AVAE infers missing user attributes from incomplete data using a novel adversarial approach.
problem Incomplete user attributes in social networks.
method Infer-AVAE combines MLP and GNNs with adversarial training to infer missing attributes.
result Infer-AVAE outperforms baselines by 7.0% in accuracy on real-world datasets.
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.
Framework learns dynamic graph attributes and links co-evolution.
problem Forecasting change of node attributes and link formation in dynamic graphs.
method CoEvoGNN framework with temporal self-attention and joint optimization.
result Framework outperforms baselines on predicting unseen graph snapshots.
Proposes TFCL to mitigate negative transfer in MTL by collaborating across features and tasks.
problem Negative transfer in Multi-Task Learning (MTL) due to dissimilar tasks.
method Task-Feature Collaborative Learning (TFCL) with heterogeneous block-diagonal structure regularizer and optimization method.
result Global convergence and block-diagonal structure recovery guarantees.
In this paper, we study the problem of transfer learning with the attribute data. In the transfer learning problem, we want to leverage the data of the auxiliary and the target domains to build an effective model for the classification problem in the target domain. Meanwhile, the attributes are naturally stable cross d…
Study predicts social relationships using triadic influence from social networks.
problem Difficulty in quantifying social relationships and their dynamics.
method Real social networks of 13 schools, neural networks, high-dimensional embedding.
result Triadic influence achieves highest accuracy in predicting student relationships.
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…
Paper proposes TPathMine model for more accurate user attribute prediction.
problem Predicting user attributes from click data in heterogeneous networks.
method HetPathMine model with meta-path weights optimized for user emotional preferences.
result TPathMine model achieves higher accuracy in user attribute prediction.
In this paper we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections betwee…
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.
Unified approach for conversational recommendation by integrating attributes and items.
problem Cold-start users' real-time personalization in online recommendation.
method Seamlessly unifies attributes and items in Thompson Sampling framework for interactive decision-making.
result Conversational Thompson Sampling (ConTS) outperforms existing methods in success rate and conversation turns.
Author2Vec generates user embeddings from social media data.
problem Generating useful user embeddings from noisy social media data.
method End-to-end neural network with BERT sentence representations and unsupervised pre-training.
result Author2Vec outperforms traditional methods in user classification tasks.
This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her…
RB-Modulation trains free diffusion models without external adapters.
problem Training-free personalization of diffusion models with style and content control.
method Stochastic optimal control with a style descriptor and cross-attention aggregation.
result Precise content and style extraction and control without external adapters.
Federated learning leaks participant dataset quality even with secure aggregation.
problem Leakage of participant dataset quality in federated learning with secure aggregation.
method Image recognition experiments to infer and attribute dataset quality.
result Relative quality ordering of participants can be inferred and used for various purposes.
Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications. Demographic data is an enabler of personalization, identity security, and other applications. Despite that, this data is sensitive and often hard to obtain. Previous work has shown that purchase …
Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial …
Personalized size and fit recommendations bear crucial significance for any fashion e-commerce platform. Predicting the correct fit drives customer satisfaction and benefits the business by reducing costs incurred due to size-related returns. Traditional collaborative filtering algorithms seek to model customer prefere…
New method mitigates bias without sensitive data using causal graph and variational autoencoder.
problem Lack of fairness strategies when sensitive attributes are not collected.
method SRCVAE framework based on causal graph for inferring a proxy sensitive attribute.
result Significant improvements in fairness metrics over existing methods.
Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as lin…
New algorithm clusters LLM inputs efficiently with quality control.
problem Scaling LLM inference cost and latency for millions of users.
method Two-stage algorithm using Mini-batch K-Means and greedy selection of representatives.
result Enforces similarity and attribute guardrails exactly, scales to tens of millions of samples.
It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before. We call the ability to create images of novel semantic concepts visually grounded imagination. In this paper, we show how we can modify variational auto-encoders to perform this task. Our method use…
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognom…
This paper improves fairness in recommendation systems by learning individual preferences across multiple dimensions.
problem Fairness in recommender systems, especially in areas with social impact.
method Opportunistic multi-aspect re-ranking approach that learns individual preferences and enhances provider fairness.
result Achieves a better trade-off between accuracy and fairness across multiple fairness dimensions.
Online shopping caters to the needs of millions of users daily. Search, recommendations, personalization have become essential building blocks for serving customer needs. Efficacy of such systems is dependent on a thorough understanding of products and their representation. Multiple information sources and data types p…
This paper debiases KG embeddings by filtering sensitive attributes.
problem Social and cultural biases in KG representations.
method Exploring and filtering popularity and gender biases in KGE.
result Sensitivity of KG embeddings to sensitive attributes like gender.
Federated learning algorithm reduces global model size by combining local and global representations.
problem Scalability issues in training large models on private data distributed over multiple devices.
method Proposes a federated learning algorithm that jointly learns compact local representations and a global model.
result The global model can be smaller since it only operates on local representations, reducing the number of communicated parameters.
With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where us…
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.
Tracking large numbers of densely-arranged, interacting objects is challenging due to occlusions and the resulting complexity of possible trajectory combinations, as well as the sparsity of relevant, labeled datasets. Here we describe a novel technique of collective tracking in the model environment of a 2D honeybee hi…
We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous approaches, our encoding feature vector is largely…
Work proposes CLAIRE to achieve counterfactual fairness from observational data without causal models.
problem Achieving counterfactual fairness from observational data without prior causal models.
method Proposes CLAIRE, a representation learning framework based on counterfactual data augmentation and an invariant penalty.
result CLAIRE effectively mitigates biases from the sensitive attribute and improves counterfactual fairness and prediction performance.
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.
Study compares classification techniques to predict customer churn in banking.
problem Predicting customer churn in banking industry.
method Comparison of six supervised classification techniques (ANN and random forest) on 10000 European bank customers.
result ANN structure with five nodes in a single hidden layer is the best performing classifier.
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.
Study models growth of unorganized retail in Indian pharma sector amid organized and e-retail competition.
problem Survival strategies of unorganized retailers challenged by organized and e-retailers.
method Field investigation, conjoint analysis, design of experiments, agent-based simulation.
result Unorganized retailers' performance influenced by their own attribute levels and price discounts.
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.
GBS uses machine learning to design products based on consumer preferences.
problem Designing products to meet consumer preferences.
method GBS is a discrete choice experiment that uses machine learning to adaptively construct paired comparison questions.
result GBS outperforms existing methods in accuracy and sample efficiency.
FMP improves personalized ASR models on private devices.
problem Limited accuracy and privacy in federated fine-tuning of ASR models.
method FMP estimates global and personalized marginal distributions and adjusts NNLM probabilities.
result FMP achieves modest WER reductions on ASR rescoring tasks.
Proposes a CNN-based method for better trajectory owner prediction.
problem Improves trajectory owner prediction for better personalized recommendations and urban planning.
method Connects POIs in a graph, encodes POIs into vectors, transforms trajectories into matrices, and uses a CNN to detect features and predict owners.
result Significantly outperforms existing methods in various metrics.
Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexi…
Face recognition models can be inferred from student models, posing privacy risks.
problem Privacy threats in transfer learning models for face recognition.
method Membership inference attacks and attribute inference from aggregate-level information.
result Sensitive attributes can be inferred from student models, even with limited auxiliary information.