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.
Bayesian method improves adaptive testing item selection, ensuring full item exposure.
Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including the ones that a user did not consume, are taken into consideration. But this assumption does not acc…
A new dataset tracks user interactions and click responses in online marketplaces.
Debias recommender systems by accounting for hidden confounders using network information.
Framework for fast CAT calibration and administration using AutoML and IRT.
The paper tackles biases in session-based recommender systems by modeling user interest as a stochastic process.
Algorithm ensures fair ranking by minority groups alongside majority groups.
A new learning-to-rank approach ensures fairness for item providers in dynamic ranking systems.
The paper develops a method to estimate consumer preferences from observed rankings.
Paper tackles exposure bias in recommender systems using contrastive learning.
While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons typically manifest themselves as biases in the training data, which then get reflected in the l…
Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g. online marketplaces, job plac…
New algorithm models satiation in recommender systems.
New model for personalized online advertising with multi-user interaction.
New ranking system balances fairness and user utility.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just …
Deep learning approximates Bermudan option exposures and future values.
Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.
Randomized neural networks improve exposure and CVA estimation for American options.
The paper addresses privacy in rank aggregation using randomized responses.
Valuation of Credit Valuation Adjustment (CVA) has become an important field as its calculation is required in Basel III, issued in 2010, in the wake of the credit crisis. Exposure, which is defined as the potential future loss of a default event without any recovery, is one of the key elementsfor pricing CVA. This pap…
Paper optimizes neural networks for Bermudan option pricing with faster convergence and risk management tools.
Exposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy. Although a lot of algorithms have been proposed to avoid teacher forcing and theref…
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To solve this issue, many modern Internet applications propose to predict a new item's interaction from the possessing contents. However, it is…
Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item an…
In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with…
In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situati…
We study the impact of central clearing of over-the-counter (OTC) transactions on counterparty exposures in a market with OTC transactions across several asset classes with heterogeneous characteristics. The impact of introducing a central counterparty (CCP) on expected interdealer exposure is determined by the tradeof…
Optimal recommendation system using user and item clustering.
The paper proposes using function approximations to reduce the computational burden in measuring counterparty credit exposure.
Study prenatal PM2.5 exposure and 4th grade reading scores, identifying critical windows of susceptibility.
Active learning improves ordering of items with contextual attributes.
We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?" Our framework learns distributed representations for items from available textual data, with the learned representations representing items in a latent space expressing f…
A personalized learning system needs a large pool of items for learners to solve. When working with a large pool of items, it is useful to measure the similarity of items. We outline a general approach to measuring the similarity of items and discuss specific measures for items used in introductory programming. Evaluat…
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
Mack's estimator improves chain ladder prediction for large exposure insurance models.
This paper optimizes the number of comparisons needed to find the best k items from pairwise comparisons.
Two methods improve 10-K item segmentation using large language models.
Study estimates personalized effects of maternal PM2.5 exposure on birth weight.
We introduce a new method to calculate the credit exposure of Bermudan, discretely monitored barrier and European options. Core of the approach is the application of the dynamic Chebyshev method of Glau et al. (2019). The dynamic Chebyshev method delivers a closed form approximation of the option prices along the paths…
A new algorithm estimates item parameters in item response theory models.
Much of the data being created on the web contains interactions between users and items. Stochastic blockmodels, and other methods for community detection and clustering of bipartite graphs, can infer latent user communities and latent item clusters from this interaction data. These methods, however, typically ignore t…
We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. Each user may be recommended a given item at most once. A latent variable model specifies the user preferences: both users and items are clustered into types. Al…
Next basket recommendation, which aims to predict the next a few items that a user most probably purchases given his historical transactions, plays a vital role in market basket analysis. From the viewpoint of item, an item could be purchased by different users together with different items, for different reasons. Ther…
Algorithm clusters items by sequentially selecting features, minimizing observations.
New method debiases selection bias in PU classification with exposure data.
Modeling incentives for content creators on algorithm-curated platforms.