CoNDA improves domain classification for IPDAs by incorporating new and personalized domains.
problem Continuous learning for new and personalized domains in IPDAs.
method Neural network based approach for incremental learning.
result CoNDA achieves high accuracy and outperforms baselines.
Guarantees for third-person imitation learning from offline data.
problem Improving generalizability in imitation learning.
method Problem-dependent statistical learning guarantees for third-person imitation from offline observation.
result Strong performance guarantees for transferred policies in the offline setting.
Algorithm learns from human demonstrations to schedule tasks efficiently.
problem Efficient resource scheduling in dynamic environments.
method Personalized apprenticeship learning framework infers decision-making criteria from heterogeneous human demonstrations.
result Achieves high accuracy in synthetic and real-world domains, outperforming baselines.
Study shows LLM-advisors match human performance in eliciting preferences but struggle with conflicting needs and trust.
problem How do LLM-advisors perform in complex financial domains where domain expertise is crucial?
method Lab-based user study with 64 participants, focusing on three challenges: preference elicitation, personalized guidance, and relationship building.
result LLM-advisors can match human performance in preference elicitation but struggle with conflicting needs and trust issues.
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.
Improved accuracy in a commercial assistant by smartly selecting new training data.
problem Expensive and time-consuming to annotate new data for ML systems.
method Automatically identifies new helpful examples suitable for human annotation.
result The proposed method leads to higher accuracy improvements with a fixed annotation budget.
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…
Domain adaptation in person re-identification (re-ID) has always been a challenging task. In this work, we explore how to harness the natural similar characteristics existing in the samples from the target domain for learning to conduct person re-ID in an unsupervised manner. Concretely, we propose a Self-similarity Gr…
Real-time personalization for HAR models learns from new users without prior data.
problem Poor performance of HAR models on new users without labeled data.
method Incremental online domain adaptation using batch normalization.
result Personalized HAR models adapt to new users in real-time.
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.
We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions. Building on an existing architecture for extractive explanations via hard attention, we add an adversarial layer which scans the residual of the attention for remaining predictive signal. Motivated by the impo…
FSPO optimizes synthetic preferences for LLM personalization.
problem Personalizing large language models for diverse users.
method FSPO reframes reward modeling as a meta-learning problem, using few labeled preferences and synthetic data.
result FSPO achieves high winrates in personalized responses, both synthetic and real.
A novel method to prevent bias in authentication models.
problem Bias in data-driven authentication models trained in one domain but required to apply in others.
method Two-stage method involving one-versus-rest disentangle learning and additive adversarial learning.
result Demonstrated effectiveness and superiority of the proposed method through comprehensive evaluation.
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.
We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12, 18, and 24 months. We start by training a population-level model using multi-modal data …
Reinforcement Learning improves personalized dialogue systems.
problem Personalized dialogue systems that generalize across users.
method Two RL-based approaches: single learner with user context features, and learner per context segmentation.
result RL methods outperform handcrafted systems in financial product recommendation.
Personalized stress model using transfer learning from 20 participants.
problem Limited generalizability of machine learning models due to individual physiological differences.
method Transfer learning from a base model trained on 20 participants' physiological data collected in real-time.
result Improved model personalization and cross-domain performance.
When training a deep neural network for image classification, one can broadly distinguish between two types of latent features of images that will drive the classification. We can divide latent features into (i) "core" or "conditionally invariant" features Xcore whose distribution Xcore∣Y, cond…
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…
Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (…
Algorithm generates private continuous-time data for sensitive domains.
problem Private generation of continuous-time data for sensitive domains.
method Mean-field Langevin dynamics and noisy particle gradient descent.
result Strong privacy guarantees for one-time data contributions.
Personalizes pre-trained models for nonparametric regression with limited data.
problem Improving data efficiency in nonparametric regression with few samples.
method Develops a theoretical framework and algorithms for few-shot personalization of black-box models.
result Achieves minimax optimal rate for personalization in nonparametric regression.
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.
Private RL algorithm with privacy guarantees for personalized medicine decisions.
problem Privacy-preserving reinforcement learning for personalized medicine decisions.
method Developed a private optimism-based RL algorithm using joint differential privacy (JDP).
result Achieved strong PAC and regret bounds with a privacy guarantee.
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.
PSI-KT improves KT accuracy and interpretability in learning materials.
problem Optimizing learning materials selection and timing for understanding and retention.
method Hierarchical generative approach using Bayesian inference.
result Superior multi-step predictive accuracy and scalable inference.
Proposes HBayes for hierarchical Bayesian recommendation learning.
problem Hierarchical structures in recommender systems.
method Hierarchical Bayesian learning framework with variational inference.
result Outperforms state-of-the-art models in precision, recall, and NDCG.
The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning …
Method transfers knowledge between partially labeled domains to classify all samples.
problem Weakly supervised open-set domain adaptation between partially labeled domains.
method Collaborative Distribution Alignment (CDA) method for bilaterally knowledge transfer and outlier identification.
result Achieves state-of-the-art performance on Office benchmark and person reidentification.
Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes …
The paper aims to define a benchmark for deep learning recommendation models.
problem Insufficient benchmarking for deep learning recommendation models.
method Synthesizes modeling strategies, defines desirable characteristics, and summarizes advice from the MLPerf Recommendation Advisory Board.
result Defines an industry-relevant benchmark for deep learning recommendation models.
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by learning a population-level model using multi-modal data from previously seen patients us…
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.
Tailoring the presentation of information to the needs of individual students leads to massive gains in student outcomes~\cite{bloom19842}. This finding is likely due to the fact that different students learn differently, perhaps as a result of variation in ability, interest or other factors~\cite{schiefele1992interest…
Paper tackles unobserved confounding in human-AI collaborations.
problem Unobserved confounding undermines human-AI collaboration effectiveness.
method Combines sensitivity analysis from causal inference with AI-driven statistical modeling.
result Enhances robustness and reliability of collaborative outcomes.
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.
Proposes ADC for cross-domain recommendation balancing user preferences.
problem Users' preferences change across different domains (e.g., social media, e-commerce).
method Designs a neural architecture and cross-domain loss function to adaptively balance user preferences.
result ADC model effectively balances the impact of domains with different complexities.
We propose a flexible method for estimating value functions in reinforcement learning without parametric assumptions.
problem Lack of interpretability in reinforcement learning models, especially in healthcare applications.
method Nonparametric additive model using local kernel regression and basis expansion.
result Personalized, adaptive recommendations for postoperative recovery.
Aims to eliminate domain bias in authentication without domain labels.
problem Authentication models are biased due to domain differences.
method Discover latent domains and eliminate domain difference alternately, using a meta-learning framework.
result Eliminates domain difference in authentication without domain labels.
Scalable model for slate recommendation learns reward probabilities.
problem Scalable personalized slate recommendation in large action spaces.
method Probabilistic Rank and Reward (PRR) model combining reward, interaction, and rank.
result PRR outperforms existing methods and is scalable to large action spaces.
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…
CUQ-GNN adapts uncertainty quantification for graph data, improving on GPN.
problem Defining meaningful uncertainty on graph data with domain-specific characteristics.
method Combines Graph Neural Networks with Posterior Networks using Normalizing Flows.
result CUQ-GNN produces more flexible and effective uncertainty estimates.
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.
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.
Multi-label network classification is a well-known task that is being used in a wide variety of web-based and non-web-based domains. It can be formalized as a multi-relational learning task for predicting nodes labels based on their relations within the network. In sparse networks, this prediction task can be very chal…
While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult. Users spend significant amounts of time in identifying the parametric form of the random va…
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.