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.
Custom loss functions improve accuracy of wildfire rate of spread forecasts.
The paper develops loss functions for pricing models using observational data.
Improved stock price prediction using LSTM with customized loss function and analyst calls.
Improved machine learning model performance through data augmentation, custom loss functions, and transfer learning.
New loss functions optimize pricing policies using transaction data, ensuring expected revenue guarantees.
We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification cost…
Ebay uses forecasting and simulation to decide when to disable a vendor.
In the UK betting market, bookmakers often offer a free coupon to new customers. These free coupons allow the customer to place extra bets, at lower risk, in combination with the usual betting odds. We are interested in whether a customer can exploit these free coupons in order to make a sure gain, and if so, how the c…
Method identifies potential customers from limited data.
Paper benchmarks and customizes energy forecasting methods.
We study a general problem of allocating limited resources to heterogeneous customers over time under model uncertainty. Each type of customer can be serviced using different actions, each of which stochastically consumes some combination of resources, and returns different rewards for the resources consumed. We consid…
New method predicts customer churn using mixed-penalty logistic regression.
In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---probabilistic graphical models and large margin methods---have their own distinct strengths but also possess significant drawbacks. Conditio…
Paper develops a framework to optimize neural networks using weighted metrics.
Evolutionary methods improve neural network loss functions, reducing overfitting.
We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers an…
Unsupervised space partitioning improves ANNS performance without pre-processing.
Proposes a new network for accurate predictions and uncertainty estimation.
This paper addresses the problem of time series forecasting for non-stationary signals and multiple future steps prediction. To handle this challenging task, we introduce DILATE (DIstortion Loss including shApe and TimE), a new objective function for training deep neural networks. DILATE aims at accurately predicting s…
Dynamic assortment problem on two-sided platform with unknown parameters
Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed. Motivated by known connections between complex test metrics and cost-weighted learning, we propose addressing these issues by using a weighted loss function with a standard…
Whether stochastic or parametric, the Pareto/NBD model can only be utilized for an in-sample prediction rather than an out-of-sample prediction. This research thus provides a neural network based extension of the Pareto/NBD model to estimate the out-of-sample parameters, which overrides the estimation burden and the ap…
TorchGAN is a PyTorch based framework for writing succinct and comprehensible code for training and evaluation of Generative Adversarial Networks. The framework's modular design allows effortless customization of the model architecture, loss functions, training paradigms, and evaluation metrics. The key features of Tor…
A scalable system detects price anomalies in online marketplaces to improve customer experience.
This work addresses encoding biases in neural networks by tailoring models with unsupervised losses.
TPG-DNN predicts user intent using GRU loss and multi-task learning.
This paper generalizes BO uncertainty measures using decision-theoretic entropies.
EXoN creates an explainable latent space for semi-supervised learning.
A new pricing strategy learns customer valuations without noise distribution knowledge.
Graph neural networks improve volatility forecasting by capturing spillover effects.
Improved resource allocation method reduces procurement costs.
In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs). In particular, we demonstrate our method in controlling color, texture, and shape of a generated garment image for computer-aided fashion design. To disentangle the effect of input at…
Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measuremen…
Regulatory requirements dictate that financial institutions must calculate risk capital (funds that must be retained to cover future losses) at least annually. Procedures for doing this have been well-established for many years, but recent developments in the treatment of conduct risk (the risk of loss due to the relat…
Fashion preference is a fuzzy concept that depends on customer taste, prevailing norms in fashion product/style, henceforth used interchangeably, and a customer's perception of utility or fashionability, yet fashion e-retail relies on algorithmically generated search and recommendation systems that process structured d…
Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making. A key challenge for direct integration of more meaningful domain and task-based evaluation criteria into an end-to-end gradient…
HistNetQ improves quantification tasks by optimizing loss functions and eliminating label requirements.
The purpose of this study was to build a customer selection model based on 20 dimensions, including customer codes, total contribution, assets, deposit, profit, profit rate, trading volume, trading amount, turnover rate, order amount, withdraw amount, withdraw rate, process fee, process fee submitted, process fee retai…
NNEinFact fits any nonnegative tensor factorization quickly and accurately.
DCE learns customer embeddings from digital activity and financial context.
Enhances deep learning robustness to noise without sacrificing clean data accuracy.
Financial institutions use LSTM models to predict customer goals.
Train a lightweight carry-on model on existing LLMs for faster customization.
Customer momentum is a positive relationship between a firm's returns and past returns of its customers.
Data-aware activation function customization reduces neural network error.
Learning using privileged information (LUPI) is a powerful heterogenous feature space machine learning framework that allows a machine learning model to learn from highly informative or privileged features which are available during training only to generate test predictions using input space features which are availab…
Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize…
Image segmentation is widely used in a variety of computer vision tasks, such as object localization and recognition, boundary detection, and medical imaging. This thesis proposes deep learning architectures to improve automatic object localization and boundary delineation for salient object segmentation in natural ima…