On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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48 results for “Recent Satisfaction”

Advertising and Brand Attitudes: Evidence from 575 Brands over Five Yearsq-fin.GN

Study examines how different types of advertising impact brand attitudes.

problem Understanding how advertising types affect brand perceptions.

method Analysis of 575 brands over five years, using national, local, and digital ads, with brand/quarter and industry/week fixed effects.

result Different types of advertising have varying impacts on brand perceptions.

Advertising Brand Attitudes Perceived Quality Perceived Value Recent Satisfaction

2018-09-04abs ↗pdf ↗

Empirical Analysis of Service Quality, Reliability and End-User Satisfaction on Electronic Banking in Nigeriaq-fin.GN

Study on electronic banking satisfaction in Nigeria.

problem Limited research on factors enhancing end users' satisfaction in electronic banking.

method Empirical analysis of factors influencing electronic banking user satisfaction.

result Factors influencing electronic banking user satisfaction and their relationship with satisfaction.

Electronic Banking Nigeria Customer Satisfaction Reliability Service Quality

2021-05-24abs ↗pdf ↗

Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learningcs.CL

The paper improves dialogue quality estimation using a novel user satisfaction model.

problem Improving dialogue quality estimation in spoken dialogue systems.

method Proposes a novel user satisfaction estimator based on BiLSTMs and reinforcement learning.

result The novel user satisfaction estimator outperforms previous models in terms of user satisfaction and task success.

dialogue systems reinforcement learning user satisfaction BiLSTM spoken dialogue

2020-01-21abs ↗pdf ↗

A Deep Prediction Network for Understanding Advertiser Intent and Satisfactioncs.SI

DSPN predicts advertiser satisfaction and intent for e-commerce platforms.

problem Understanding advertiser intent and satisfaction for e-commerce platforms.

method Two-stage Deep Satisfaction Prediction Network (DSPN) that models intent and satisfaction.

result DSPN outperforms state-of-the-art baselines and predicts advertiser satisfaction accurately.

advertising e-commerce DSPN advertiser intent

2020-08-20abs ↗pdf ↗

Multi-domain Conversation Quality Evaluation via User Satisfaction Estimationcs.LG

Automated dialogue quality evaluation using user satisfaction estimates across multiple domains.

problem Lack of automated and domain-independent dialogue quality evaluation metrics.

method Created a new Response Quality annotation scheme, introduced five domain-independent feature sets, and experimented with six machine learning models.

result Gradient Boosting Regression model achieved best prediction performance, with a 16% relative improvement in binary satisfaction class prediction accuracy.

dialogue quality user satisfaction machine learning multi-domain response quality

2019-11-18abs ↗pdf ↗

Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agentscs.HC

Hybrid approach combines user feedback and machine learning for predicting user satisfaction.

problem Measuring user satisfaction in large-scale conversational agent systems.

method Fusion of explicit user feedback and predictions from two machine-learned models trained on different data types.

result Hybrid approach significantly improves user satisfaction predictions.

user satisfaction conversational agents machine learning user feedback waterfall policy

2020-05-29abs ↗pdf ↗

Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimationcs.LG

New metric estimates user satisfaction for dialogue quality evaluation.

problem Sparse and intrusive explicit user feedback for dialogue quality evaluation.

method Created a new Response Quality annotation scheme and developed a turn-level User Satisfaction metric using five domain-independent feature sets and six machine learning models.

result Gradient boosting regression achieved best correlation of ~0.79 between predicted and annotated user satisfaction labels.

dialogue quality user satisfaction machine learning annotation scheme multi-domain

2019-08-19abs ↗pdf ↗

PDP: A General Neural Framework for Learning Constraint Satisfaction Solverscs.LG

PDP framework learns CSP solvers without explicit search strategy.

problem Learning effective search strategies for CSP solvers.

method Proposes a generic neural framework based on propagation, decimation, and prediction.

result Demonstrates effectiveness in SAT solving compared to neural and state-of-the-art baselines.

constraint satisfaction neural networks search strategies unsupervised learning graphical models

2019-03-05abs ↗pdf ↗

VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editingcs.LG

Improves generative models by optimizing rewards and sample editing.

problem Efficiently generating high-reward samples with structural constraints.

method Introduces MDM-VGB, a discrete diffusion sampler that augments unmasking generation with reward-guided remasking.

result MDM-VGB achieves quadratic complexity and robustness to noise, outperforming heuristics like best-of-

N

Masked Diffusion Model Reward-guided Sampling Generative Models Efficient Inference Constraint Satisfaction

2026-06-26abs ↗pdf ↗

Optimization Method for Interval Portfolio Selection Based on Satisfaction Index of Interval inequality Relationmath.OC

In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …

2012-07-09abs ↗pdf ↗

LIMEADE: From AI Explanations to Advice Takingcs.IR

LIMEADE improves AI advice for opaque models, enhancing accuracy and user satisfaction.

problem Lack of advice methods for opaque AI models.

method Develops a general framework to translate advice into model updates.

result Improves accuracy and user satisfaction compared to baselines.

AI advice opaque models user satisfaction image classification text recommendation

2020-03-09abs ↗pdf ↗

Streamlining Variational Inference for Constraint Satisfaction Problemscs.AI

New algorithm improves solving constraint satisfaction problems by avoiding contradictory estimates.

problem Improving solving efficiency for constraint satisfaction problems with approximate marginals.

method Introducing a streamlined branching strategy based on streamlining constraints.

result Streamlined solvers outperform decimation-based solvers on random k-SAT instances, reducing the gap in performance by 16.3% on average.

constraint satisfaction variational inference streamlining branching k-SAT

2018-11-24abs ↗pdf ↗

What can be learned from satisfaction assessments?econ.EM

Study shows non-systematic bias in customer satisfaction surveys limits data value.

problem Non-systematic bias in customer satisfaction surveys limits data value.

method Used real customer satisfaction survey data of a large retail bank to show the irreducible error and suggest thoughtful survey design methods.

result A thoughtful survey design can reduce non-systematic error in customer satisfaction surveys.

customer satisfaction survey bias ordinal data non-systematic error calibration method

2020-10-26abs ↗pdf ↗

Ontology Neural Networks for Topologically Conditioned Constraint Satisfactioncs.LG

Enhanced neural network framework improves constraint satisfaction with topological conditioning.

problem Maintaining semantic coherence while satisfying physical and logical constraints in neuro-symbolic reasoning.

method Integrates topological conditioning with gradient stabilization mechanisms using Forman-Ricci curvature, Deep Delta Learning, and Covariance Matrix Adaptation Evolution Strategy.

result Achieves mean energy reduction to 1.15 compared to baseline values of 11.68, with 95 percent success rate.

Neural Networks Constraint Satisfaction Topology Gradient Stabilization Forman-Ricci Curvature

2026-01-08abs ↗pdf ↗

Feature Selection for Gender Classification in TUIK Life Satisfaction Surveycs.LG

Study reduces dataset size by 3 attributes to 73% classification accuracy.

problem Reducing dataset size for gender classification in TSI Life Satisfaction Survey.

method Attribute selection using Chi2 algorithm, followed by classification tests with Naive Bayes and Random Forest.

result Best classification rate achieved with 3 attributes selected by Chi2 algorithm.

gender classification attribute selection feature reduction life satisfaction machine learning

2018-07-12abs ↗pdf ↗

Combinatorial Allocation Bandits with Nonlinear Arm Utilitycs.LG

A new online learning problem, CAB, tackles matching platforms to maximize user satisfaction.

problem Maximizing matches in a matching platform can lead to dissatisfaction and churn.

method Developed CAB, an online learning problem that maximizes arm satisfaction, and analyzed algorithms like UCB and Thompson sampling.

result CAB-UCB achieves higher cumulative satisfaction than baselines in experiments.

matching platform user satisfaction combinatorial allocation bandits upper confidence bound Thompson sampling

2026-03-07abs ↗pdf ↗

Retail Market Analysisq-fin.GN

Analyzes retail trends from sales, search, and reviews.

problem Optimizing inventory and marketing for better customer satisfaction.

method Historical sales data, search trends, and customer reviews.

result Identifies patterns and trending products for retailers.

retail market analysis inventory management marketing strategies customer satisfaction

2025-01-20abs ↗pdf ↗

The co-varying ties between networks and item responses via latent variablesstat.ME

Researchers study how teachers' advising relationships influence their perceptions of satisfaction and students, not policy influence.

problem Understanding the relationship between teachers' advising relationships and their perceptions of satisfaction and students.

method Proposed a novel joint model of network and item responses (JNIRM) with correlated latent variables.

result Teachers' advising relationships contribute more to satisfaction and students than to influence over educational policies.

teachers advising relationships perceptions satisfaction students

2024-09-28abs ↗pdf ↗

Hierarchical Transformers for Long Document Classificationcs.CL

Transformers handle long documents better with hierarchical segmentation and recurrent layers.

problem Applying BERT to long documents like call transcripts.

method Hierarchical segmentation, recurrent layers, and softmax activation.

result Significant improvement in customer call satisfaction prediction and topic classification tasks.

BERT transformers long document classification recurrent layers softmax

2019-10-23abs ↗pdf ↗

A Goal Programming Model with Satisfaction Function for Risk Management and Optimal Portfolio Diversificationq-fin.RM

We extend the classical risk minimization model with scalar risk measures to the general case of set-valued risk measures. The problem we obtain is a set-valued optimization model and we propose a goal programming-based approach with satisfaction function to obtain a solution which represents the best compromise betwee…

2012-01-09abs ↗pdf ↗

Automatic Music Playlist Generation via Simulation-based Reinforcement Learningstat.ML

Paper presents a reinforcement learning framework for personalized music playlist generation.

problem Misalignment between offline model objectives and online user satisfaction metrics in conventional playlist recommendation methods.

method Simulation-based reinforcement learning approach using a Deep Q-Network (DQN) modified to address large state and action spaces.

result The modified DQN (AH-DQN) policy leads to better user-satisfaction metrics compared to baseline methods during online A/B tests.

reinforcement learning playlist generation user satisfaction deep q-network reinforcement learning framework

2023-10-13abs ↗pdf ↗

Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checkingcs.LG

Scalable model checking for stochastic systems using Gaussian Processes and Bayesian Neural Networks.

problem Efficiently verifying properties of stochastic systems with high-dimensional parameter spaces.

method Stochastic Variational Smoothed Model Checking (SV-smMC) using Gaussian Processes and Bayesian Neural Networks.

result SV-smMC scales to larger datasets and enables application to high-dimensional parameter spaces.

stochastic verification Bayesian inference Gaussian Processes Bayesian Neural Networks scalability

2022-05-11abs ↗pdf ↗

User Model-Based Intent-Aware Metrics for Multilingual Search Evaluationcs.IR

Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This approach translates the multilingual search problem into a particular task of searc…

2016-12-13abs ↗pdf ↗

Local entropy as a measure for sampling solutions in Constraint Satisfaction Problemscond-mat.dis-nn

We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to efficiently sample solutions of random Constraint Satisfaction Problems (CSPs). First, we extend a recent result that, using a large-deviation analysis, shows that the geometry of the space of solutions of the Binary Perceptron Learning Problem (a proto…

2015-11-18abs ↗pdf ↗

GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfactioncs.LG

GACEM optimizes complex multi-modal problems using neural networks.

problem Black-box optimization and constraint satisfaction in multi-modal environments.

method Modified Cross-Entropy Method with masked auto-regressive neural network.

result GACEM outperforms traditional CEM in diverse solutions, mode discovery, and sample efficiency.

black-box optimization constraint satisfaction multi-modal problems neural networks Reinforcement Learning

2020-02-17abs ↗pdf ↗

Constraint-Based Visual Generationcs.LG

Combines deep learning and logic constraints for visual generation.

problem Generating images with specific properties using deep learning and logic constraints.

method Formulates visual generation as a constrained satisfaction problem, using deep architectures to model variables and t-norm theory for logic variables.

result Promising results in generating handwritten characters and face transformations.

Visual Generation Deep Learning Logic Constraints Constrained Satisfaction t-norm Theory

2018-07-16abs ↗pdf ↗

Risk-Aware Continuous Control with Neural Contextual Banditscs.LG

Paper proposes a risk-aware decision-making framework for real-world sequential decisions.

problem Real-world sequential decision-making problems often have critical constraints that learning solutions often neglect.

method Actor multi-critic architecture with risk characterization.

result Our approach consistently satisfies system constraints with minimal performance toll.

risk-aware continuous control neural contextual bandits constraint satisfaction 5G mobile network

2023-12-15abs ↗pdf ↗

Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networkscs.LG

Generative adversarial networks improve radiation therapy planning.

problem Creating optimal radiation therapy plans.

method Using a GAN to predict 3D dose distributions directly.

result Significantly outperforms previous methods in clinical satisfaction and metrics.

radiation therapy GAN 3D dose distributions oropharyngeal cancer clinical satisfaction

2018-07-17abs ↗pdf ↗

Teaching the Old Dog New Tricks: Supervised Learning with Constraintscs.LG

A new ML method teaches constraints directly to models.

problem Addressing safety and fairness in AI systems.

method Directly teaching constraint satisfaction to ML models using a constraint solver.

result Empirically, our approach performs well on fairness and synthetic constraints.

Machine Learning Constraint Solving Supervised Learning Fairness Safety

2020-02-25abs ↗pdf ↗

Safe Exploration Method for Reinforcement Learning under Existence of Disturbancecs.LG

Safe exploration method for RL under disturbance ensures safety with probabilistic guarantees.

problem Safe reinforcement learning in real environments with disturbance.

method Uses partial prior knowledge and conservative inputs to ensure state constraint satisfaction.

result Guaranteed safety with pre-specified probability in the presence of stochastic disturbance.

reinforcement learning safe exploration stochastic disturbance state constraints conservative inputs

2022-09-30abs ↗pdf ↗

Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streamingstat.ML

An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video strea…

2018-03-04abs ↗pdf ↗

Automatic Exploration Process Adjustment for Safe Reinforcement Learning with Joint Chance Constraint Satisfactioncs.LG

This paper proposes a method to safely adjust exploration in RL to satisfy constraints.

problem Unsafe exploration in reinforcement learning violates constraints on controlled object states.

method Automatic adjustment of exploration inputs and variance-covariance matrix for safety.

result The method guarantees satisfaction of joint chance constraints with specified probability.

reinforcement learning exploration safe control joint chance constraint continuous state

2021-03-05abs ↗pdf ↗

Safely Learning to Control the Constrained Linear Quadratic Regulatormath.OC

Safe control of systems with unknown dynamics using persistent excitation.

problem Tension between safety and exploration in data-driven control.

method System identification through persistent excitation, robust constraint satisfaction, and synthesis of feedback controllers.

result Non-asymptotic guarantees on estimation and controller performance.

control theory linear quadratic regulator system identification constraint satisfaction persistent excitation

2018-09-26abs ↗pdf ↗

Experimental Mathematics Approach to Gauss Diagrams Realizabilitymath.GT

New experiments show Gauss diagrams not all as simple as previously thought.

problem Determining which Gauss diagrams correspond to knots.

method Experimental mathematics using Prolog and constraint satisfaction.

result Found counterexamples showing existing criteria are incomplete.

Gauss diagrams realizability experimental mathematics constraint satisfaction Prolog

2021-03-03abs ↗pdf ↗

Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guaranteescs.LO

Paper uses RL to synthesize control policies for LTL objectives in uncertain environments.

problem Control policies for uncertain, probabilistic environments with temporal logic specifications.

method Model-free RL algorithm, LTL to LDBA translation, synchronous reward function, asymptotic satisfaction probability.

result RL algorithm maximizes satisfaction probability of LTL objectives in PL-MDPs.

Reinforcement Learning Temporal Logic Control Policies Probabilistic Satisfaction Markov Decision Processes

2019-09-11abs ↗pdf ↗

An Analysis of Regularized Approaches for Constrained Machine Learningcs.LG

The paper shows regularization can't always find all optimal solutions in constrained ML.

problem Finding the right balance between model accuracy and constraint satisfaction.

method Formal demonstration of the limitations of regularization-based approaches in non-convex cases.

result There are optimal solutions for constrained problems that do not correspond to any multiplier value.

Machine Learning Regularization Constraint Satisfaction Optimization Non-convex Problems

2020-05-20abs ↗pdf ↗

TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Loadcs.AI

TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.

problem Real-time market prediction requires accurate predictions before a deadline.

method Filters feasible models, dispatches workers, trades accuracy for deadline risk.

result Optimized pool achieves 0.991 timely accuracy and 0.994 raw accuracy.

market prediction inference scheduling time-predictable uncertain load timeliness

2025-05-30abs ↗pdf ↗

Learning Interpretable Models in the Property Specification Languagecs.LG

Develops a learning algorithm for PSL formulas from examples.

problem Learning human-interpretable descriptions of complex systems from examples.

method Reduces learning to propositional logic constraint satisfaction and uses SAT solver.

result Proposed method provides succinct human-interpretable descriptions from examples.

Temporal Logic Property Specification Language Interpretable Models Constraint Satisfaction SAT Solver

2020-02-10abs ↗pdf ↗

A Deep Learning System for Predicting Size and Fit in Fashion E-Commercecs.LG

Deep learning predicts fit for fashion e-commerce.

problem Predicting correct fit for customer satisfaction and cost reduction.

method Deep learning content-collaborative approach using customer and article embeddings.

result Significant improvement over state-of-the-art methods.

deep learning fashion e-commerce size prediction fit recommendation collaborative filtering

2019-07-23abs ↗pdf ↗

Training Recurrent Neural Networks as a Constraint Satisfaction Problemcs.LG

This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP). The quotient gradient system (QGS) is a trajectory-based method for solving the CSP. This study converts the training set of a neural network into a CSP and uses the QGS to …

2018-03-20abs ↗pdf ↗

Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Taskscs.LG

SAVED safely learns robot tasks with sparse rewards.

problem Challenges in reinforcement learning for robotics, especially sparse rewards and complex constraints.

method SAVED uses supervision to constrain exploration and learn efficiently, handling complex constraints.

result SAVED outperforms state-of-the-art methods in success rate, constraint satisfaction, and sample efficiency.

reinforcement learning robotics model-based sparse rewards constraint satisfaction

2019-05-31abs ↗pdf ↗

The effective noise of Stochastic Gradient Descentcond-mat.dis-nn

Stochastic Gradient Descent introduces noise in training, affecting model decision boundaries.

problem Understanding the impact of noise in SGD on model decision boundaries.

method Characterized SGD and persistent SGD dynamics in a neural network model, measuring noise magnitude in both under- and over-parametrized regimes.

result Noisier algorithms lead to wider decision boundaries in constraint satisfaction problems.

Stochastic Gradient Descent SGD noise Decision boundaries Neural networks Mean-field theory

2021-12-20abs ↗pdf ↗

Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networksmath.OC

This work uses a SI-DNN to predict AC-OPF solutions efficiently.

problem Efficiently predicting AC-OPF solutions in real-time power systems.

method Sensitivity-Informed Deep Neural Network (SI-DNN) for AC-OPF.

result SI-DNN can predict AC-OPF solutions with better generalization and constraint satisfaction.

AC-OPF Deep Neural Networks Optimal Power Flow Sensitivity Constraint Satisfaction

2021-03-27abs ↗pdf ↗

CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guaranteecs.LG

CRPO solves challenging SRL problems with convergence guarantee.

problem Safe reinforcement learning with nonconvex constraints.

method Primal approach using alternating policy updates and NPG.

result Achieves

\mathcal{O}(1/\sqrt{T})

convergence rate and error bound.

safe reinforcement learning constraint satisfaction primal approach policy optimization convergence guarantee

2020-11-11abs ↗pdf ↗

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delaycs.LG

New algorithm optimizes long-term user satisfaction in recommendation systems.

problem Optimizing long-term user satisfaction in recommendation systems with delayed rewards.

method Developed a predictive model of delayed rewards and a bandit algorithm that balances exploration and exploitation.

result Our approach results in substantially better performance compared to short-term or delayed optimization.

recommender systems long-term satisfaction multi-armed bandits delayed rewards podcast recommendations

2023-07-19abs ↗pdf ↗

Multi-Manifold Learning for Large-scale Targeted Advertising Systemcs.LG

A new method for precise user targeting in advertising using hyperbolic manifold learning.

problem Improving user satisfaction in targeted advertising by overcoming skepticism and spam perception.

method Proposes a Multi-Manifold Learning framework to learn hierarchical user and ad representations in the hyperbolic space.

result Demonstrates improved performance in user targeting and prediction accuracy on both public datasets and a large-scale commercial system.

targeted advertising hyperbolic manifold learning user representation ad representation user satisfaction

2020-07-05abs ↗pdf ↗

Lyapunov-based Safe Policy Optimization for Continuous Controlcs.LG

Safe reinforcement learning for continuous control using Lyapunov methods.

problem Ensuring safe interactions in continuous action reinforcement learning.

method Formulates CMDPs, uses Lyapunov-based projection for policy updates, integrates with PG methods.

result Demonstrates effectiveness in balancing performance and constraint satisfaction.

reinforcement learning continuous control safe policy Lyapunov policy gradient

2019-01-28abs ↗pdf ↗

Impatient Bandits: Optimizing for the Long-Term Without Delaycs.LG

New algorithm optimizes for long-term user satisfaction in delayed reward settings.

problem Optimizing for long-term user satisfaction in delayed reward settings.

method Developed a predictive model of delayed rewards and a bandit algorithm that combines rewards and surrogate outcomes.

result Our algorithm significantly outperforms methods that optimize for short-term proxies or rely solely on delayed rewards.

recommender systems bandit problems delayed rewards long-term satisfaction podcast recommendation

2025-01-14abs ↗pdf ↗