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.
A Qini-based uplift model improves retention marketing campaign performance.
Endogenous reinsurance pricing in large insurance markets
A new AMM design reduces impermanent loss and retains more liquidity.
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…
A new uplift modeling approach uses binary treatment indicators more efficiently.
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…
Graph-based approach predicts stock trends using dynamic multi-relational graphs.
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.
New insights into continual learning with task similarity.
MACI improves LLM factuality inference with higher retention and lower time cost.
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…
Study uses Open Banking data to estimate customer value, showing potential 21% increase.
New approach to continual learning prioritizes adaptation over retention.
The paper analyzes dynamics of momentum in high dimensions with sparse updates.
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.
This paper optimizes portfolios using TDA and financial news sentiment.
Study compares classification techniques to predict customer churn in banking.
This paper studies how AMMs can minimize losses from arbitrage while retaining uninformed trading activity.
A new framework ensures model safety by retaining old model capabilities while improving new tasks.
A new method for uplift modeling using learning-to-rank techniques.
Trimming helps in conformal prediction when it separates anomaly scores.
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.
New theory explains forgetting in learning algorithms.
New model analyzes customer churn with tensor completion and binary data.
Unified framework for unlearning in diffusion models using KL divergence and likelihood constraints.
In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect a…
Quantum systems with scrambling improve temporal information processing, but scaling requires exponential overhead.
Machine Learning (ML) models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the model may be less favorable to groups contributing less to the training process; this in turn can degrade population retention in these groups over …
The paper values reinsurance contracts for dynamic catastrophe claims without arbitrage.
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…
CPP improves predictive model outputs for better intervention decisions.
Proposes a greedy algorithm for telecom offers to retain subscribers.
Paper presents AETN for efficient user modeling from mobile app usage.
A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.
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.
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…
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…
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…
New method quantifies uncertainty in fine-tuned LLMs using LoRA ensembles.
Bernard et al. (2015) study an optimal insurance design problem where an individual's preference is of the rank-dependent utility (RDU) type, and show that in general an optimal contract covers both large and small losses. However, their contracts suffer from a problem of moral hazard for paying more compensation for a…
Study finds flipped classrooms improve student self-concept, enjoyment, but not exam scores.
Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model stud…
Spaced repetition is a technique for efficient memorization which uses repeated, spaced review of content to improve long-term retention. Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition? In this paper, we introduce a novel, flexible representation of spaced repetition using the …