The study clusters neighborhoods based on childhood vulnerability data and program retention rates.
problem Improving childhood outcomes and decision-making in early childhood development.
method Data-driven approaches combining public and private sources to visualize children's experiences and analyze program retention rates.
result Neighborhoods can be clustered based on childhood vulnerability, and certain programs have higher retention rates.
Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classifi…
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention dis…
In the hypothesis of rare loss events, the general expression of the policy value has been determined as a functional of the "expected frequency / loss severity" function and of the retention function. Exponential disutility has been chosen after mathematical characterization of some of its economical aspects, where fu…
A Qini-based uplift model improves retention marketing campaign performance.
problem Isolating the marketing effect of a campaign and identifying responsive customers.
method Qini-based uplift regression model using logistic regression.
result Qini-optimized uplift models improve performance and provide interpretable models.
Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suite…
This study integrates cost-sensitive and causal classification methods.
problem Improving classification model performance in business decision-making.
method A unifying evaluation framework for cost-sensitive and causal classification.
result Conventional classification is a specific case of causal classification.
Endogenous reinsurance pricing in large insurance markets
problem Endogenous reinsurance pricing in large insurance markets
method Stackelberg leader and insurer equilibrium analysis
result Characterization of insurers' equilibrium retention and Stackelberg equilibria
Machine learning models can lead to group representation disparity, affecting long-term retention.
problem Representation disparity in ML models can lead to group retention issues over time.
method Analyzed user dynamics and fairness criteria in a sequential decision-making framework.
result Representation disparity can worsen over time without proper fairness criteria.
New insights into continual learning with task similarity.
problem Challenges in learning similar tasks without interference.
method Linear teacher-student model with latent structure.
result High input feature similarity with low readout similarity is catastrophic.
MACI improves LLM factuality inference with higher retention and lower time cost.
problem Ensuring factuality in LLM responses for high-stakes domains.
method Reformulated conformal inference in a multiplicative filtering setting, leveraging ensembles for more accurate factuality scores and group-conditional calibration.
result MACI achieves higher retention and lower time cost compared to baselines, preserving validity through group-conditional calibration.
New approach to continual learning prioritizes adaptation over retention.
problem Catastrophic forgetting in lifelong learning models.
method Formalized CL as an online optimization problem, introduced Transfer Efficiency, and derived a Critical Task Duration.
result Retention can hinder real-time adaptation in non-stationary environments.
The paper analyzes dynamics of momentum in high dimensions with sparse updates.
problem Theoretical analysis of momentum dynamics in high-dimensional sparse settings.
method Theoretical analysis of two models: least squares with sparse inputs and logistic regression with a rare class.
result Characterization of high-dimensional limits of momentum dynamics and phase structure.
A new AMM design reduces impermanent loss and retains more liquidity.
problem Inefficiencies in conventional AMM designs lead to liquidity loss and user engagement issues in DEXs.
method Proposes a dual-mechanism framework: a power-law invariant BMM and dynamic rebate system.
result Reduces impermanent loss by 36% and retains 3.98x more liquidity during price volatility.
Current generation of memory-augmented neural networks has limited scalability as they cannot efficiently process data that are too large to fit in the external memory storage. One example of this is lifelong learning scenario where the model receives unlimited length of data stream as an input which contains vast majo…
The paper uses RFM and clustering to segment bank customers.
problem Challenges in customer retention and profitable segmentation in banking.
method RFM technique and clustering algorithms applied to real data.
result Successful customer segmentation improves conversion rates.
A new framework ensures model safety by retaining old model capabilities while improving new tasks.
problem Catastrophic forgetting in continual learning systems.
method Retention-centric framework with data-dependent constraints and an efficient constrained optimization algorithm.
result The method ensures that new models retain important capabilities of old models while improving new tasks.
Trimming helps in conformal prediction when it separates anomaly scores.
problem Effectiveness of trimming in conformal prediction under contamination.
method Analyse fixed-threshold trimming as a replacement of the contaminated calibration law with a retained law.
result Trimming helps when it separates anomaly scores, reducing clean-target coverage to a one-dimensional score-CDF transfer problem.
Based on a point of view that solvency and security are first, this paper considers regular-singular stochastic optimal control problem of a large insurance company facing positive transaction cost asked by reinsurer under solvency constraint. The company controls proportional reinsurance and dividend pay-out policy to…
The brain optimizes memory by forgetting what's predictable, improving generalization.
problem Memory consolidation struggles with representational drift, semanticisation, and offline replay.
method Proposes predictive forgetting as a mechanism to optimize generalization by reducing complexity.
result Predictive forgetting improves information-theoretic generalization bounds on stored representations.
New theory explains forgetting in learning algorithms.
problem Tendency of learning algorithms to forget past knowledge.
method Proposes a self-consistency theory of forgetting as a loss of predictive information.
result Exact Bayesian inference allows for adaptation without forgetting.
New model analyzes customer churn with tensor completion and binary data.
problem Analyzing the impact of interventions on customer churn.
method Tensorized latent factor block hazard model with 1-bit tensor completion.
result Effective categorization of interventions by similar impacts.
Paper tackles treatment leakage in text-based causal inference, proposing methods to mitigate bias.
problem Treatment leakage in text-as-confounder applications introduces bias in causal estimates.
method Formal definitions, four text distillation methods (passage removal, classification, salient feature removal, nullspace projection).
result Moderate distillation optimally balances bias reduction against confounder retention.
Unified framework for unlearning in diffusion models using KL divergence and likelihood constraints.
problem Removing undesirable data or concepts while preserving utility of pretrained models.
method Constrained optimization framework based on reverse and forward KL divergences, and likelihood constraints.
result Our KL-constrained approach achieves superior retention-unlearning tradeoffs compared to weight-based baselines.
Quantum systems with scrambling improve temporal information processing, but scaling requires exponential overhead.
problem Scalability and memory retention of quantum reservoirs in temporal information processing.
method Examined a quantum reservoir processing framework with scrambling reservoirs modeled by high-order unitary designs, analyzed in noiseless and noisy settings.
result Memory retention improves exponentially with reservoir size but worsens with reservoir iterations, requiring exponential shot overhead for scaling.
This paper considers nonlinear regular-singular stochastic optimal control of large insurance company. The company controls the reinsurance rate and dividend payout process to maximize the expected present value of the dividend pay-outs until the time of bankruptcy. However, if the optimal dividend barrier is too low t…
Deep learning aligns GC-MS peaks for biomarker discovery.
problem Aligning retention times of GC-MS peaks across different samples.
method ChromAlignNet, a deep learning model for peak alignment.
result ChromAlignNet outperforms existing methods on complex data sets.
Paper classifies brain signals using eigenvalues for 2D and 3D educational content questions.
problem Classifying brain signals for 2D and 3D educational content questions.
method Eigenvalues of covariance matrix used as features; KNN and SVM classifiers applied.
result No significant difference in learning, memory retention, and recall between 2D and 3D educational content.
Proposes a greedy algorithm for telecom offers to retain subscribers.
problem Maximizing revenue while preventing churn in telecom subscribers.
method Combinatorial algorithm for offer optimization under heterogeneous incentives.
result Efficient and accurate solution for large subscriber bases.
A new uplift modeling approach uses binary treatment indicators more efficiently.
problem Lack of full value utilization in binary uplift modeling.
method Design a novel transformed outcome for binary target variables.
result Our new approach outperforms traditional methods in synthetic and real-world datasets.
Paper presents AETN for efficient user modeling from mobile app usage.
problem Efficient user modeling from mobile app usage with reduced manual effort.
method AutoEncoder-coupled Transformer Network (AETN).
result AETN achieves effective user embeddings with reduced manual effort.
Graph-based approach predicts stock trends using dynamic multi-relational graphs.
problem Predicting future stock movements in complex, time-evolving stock relationships.
method Dynamic multi-relational stock graphs, stochastic diffusion process, parallel retention.
result Outperforms state-of-the-art baselines in stock trend forecasting.
A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.
problem Understanding individual metro usage dynamics over multi-year horizons.
method A state-based lifecycle modeling framework integrating HSMM and discrete-time survival analysis.
result Identification of interpretable mobility states, transition dynamics, and state-dependent exit and re-entry processes.
Firm growth process in the developing economies is known to produce divergence in their growth path giving rise to bimodality in the size distribution. Similar bimodality has been observed in wealth distribution as well. Here, we introduce a modified kinetic exchange model which can reproduce such features. In particul…
Optimal insurance minimizes ruin probability with non-decreasing functions.
problem Minimizing ruin probability with insurance premiums and non-decreasing functions.
method Reformulated problem with inverse survival function as control variable.
result Deductible insurance with maximum limit is optimal.
The aim of this paper is to introduce an insurance model allowing reinsurance and dividend payment. Our model deals with several homogeneous contracts and takes into account the legislation regarding the provisions to be justified by the insurance companies. This translates into some restriction on the (maximal) number…
SED integrates synthesis, execution, and debugging for neural program synthesis.
problem Challenges in synthesizing complex programs that match specifications.
method SED combines synthesis, execution, and debugging to improve neural program generation.
result SED reduces error rates and outperforms standard decoding methods.
Stochastic programs simplify complex models with noise and nondeterminism.
problem Handling models with nuisance parameters, noise, and nondeterminism.
method Developed a reference implementation for stochastic probabilistic programs and inference.
result Efficient inference in models with noise and nondeterminism is possible.
Deployable probabilistic programming for Go and other languages.
problem Adding probabilistic programming to mainstream languages.
method Design guidelines and Infergo implementation for Go.
result Infergo demonstrates performance and applicability in various use cases.
Improves probabilistic programming by analyzing program structure.
problem Inefficiency and limitations of single inference algorithms in probabilistic programming.
method Three novel techniques: static and dynamic analyses to adapt programs for more efficient inference.
result Improves probabilistic programming by making inference more efficient.
We introduce dropout compaction, a novel method for training feed-forward neural networks which realizes the performance gains of training a large model with dropout regularization, yet extracts a compact neural network for run-time efficiency. In the proposed method, we introduce a sparsity-inducing prior on the per u…
A neural program synthesis method with iterative fix operations.
problem Creating correct programs from input-output examples.
method Combines encoder-decoder synthesis with a differentiable fixer.
result Improves synthesis accuracy by reducing discrepancies between outputs and desired outputs.
In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual l…
Paper introduces techniques to learn higher-order programs, improving predictive accuracy and reducing learning times.
problem Expressing and learning complex programs in ILP.
method Extending meta-interpretive learning to support higher-order definitions as background knowledge.
result Learning higher-order programs reduces hypothesis space and sample complexity, improving predictive accuracy and reducing learning times.
Graph-based approach repairs programs from diagnostic feedback.
problem Learning to repair programs from limited labeled data and compiler error messages.
method Introduces program-feedback graph and graph neural network for reasoning, and self-supervised learning with unlabeled programs.
result DrRepair significantly outperforms prior work, achieving high repair rates.
COSET benchmarks neural program embeddings using diverse source-code datasets.
problem Evaluating neural program embeddings is challenging due to lack of straightforward metrics.
method COSET framework with labeled programs, transformations, and a pilot study.
result COSET identifies strengths and weaknesses of neural models and program characteristics.
We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. We are able to demonstrate ho…
SPoC uses search to translate pseudocode into correct programs with error localization.
problem Mapping pseudocode to functionally correct long programs.
method Search-based approach guided by compilation errors for credit assignment.
result Search improves synthesis success rate from 25.6% to 44.7%.