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.
Machine learning models can lead to group representation disparity, affecting long-term retention.
MACI improves LLM factuality inference with higher retention and lower time cost.
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.
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.
Endogenous reinsurance pricing in large insurance markets
New insights into continual learning with task similarity.
New approach to continual learning prioritizes adaptation over retention.
The paper analyzes dynamics of momentum in high dimensions with sparse updates.
A new AMM design reduces impermanent loss and retains more liquidity.
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…
A new uplift modeling approach uses binary treatment indicators more efficiently.
The paper uses RFM and clustering to segment bank customers.
Machine learning models (e.g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e.g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss. Worse, as model accuracy affects user retention, a minor…
A new framework ensures model safety by retaining old model capabilities while improving new tasks.
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.
Quantum systems with scrambling improve temporal information processing, but scaling requires exponential overhead.
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…
Paper classifies brain signals using eigenvalues for 2D and 3D educational content questions.
Proposes a greedy algorithm for telecom offers to retain subscribers.
Paper presents AETN for efficient user modeling from mobile app usage.
Graph-based approach predicts stock trends using dynamic multi-relational graphs.
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…
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…
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…
Selective reinitialization improves adaptability of neural bandits in dynamic environments.
Study compares classification techniques to predict customer churn in banking.
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 …
Paper optimizes recommendation systems for long-term business metrics.
We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent network…
Muon optimizes training efficiency by improving data retention at large batch sizes.
Deep neural networks are known to suffer the catastrophic forgetting problem, where they tend to forget the knowledge from the previous tasks when sequentially learning new tasks. Such failure hinders the application of deep learning based vision system in continual learning settings. In this work, we present a simple …