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.
Shared Keyboard design improves phase I clinical trials by borrowing information across doses.
The spectacular expansion of the Internet has led to the development of a new research problem in the field of natural language processing: automatic toxic comment detection, since many countries prohibit hate speech in public media. There is no clear and formal definition of hate, offensive, toxic and abusive speeches…
Paper develops a fast Bayesian method to predict toxic trades in financial transactions.
Timely assessment of compound toxicity is one of the biggest challenges facing the pharmaceutical industry today. A significant proportion of compounds identified as potential leads are ultimately discarded due to the toxicity they induce. In this paper, we propose a novel machine learning approach for the prediction o…
Everyday we are exposed to various chemicals via food additives, cleaning and cosmetic products and medicines -- and some of them might be toxic. However testing the toxicity of all existing compounds by biological experiments is neither financially nor logistically feasible. Therefore the government agencies NIH, EPA …
We refine toxicity bounds for dynamic liquidation incentives in CP-AMM systems.
CondMTL improves toxicity detection by learning group-specific representations.
Prediction of toxicity levels of chemical compounds is an important issue in Quantitative Structure-Activity Relationship (QSAR) modeling. Although toxicity prediction has achieved significant progress in recent times through deep learning, prediction accuracy levels obtained by even very recent methods are not yet ver…
Model optimizes trading strategy with unobservable toxicity.
SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.
Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources. In this paper, we strongly argue for the models…
New method designs antimicrobial peptides with high potency and low toxicity.
To identify and classify toxic online commentary, the modern tools of data science transform raw text into key features from which either thresholding or learning algorithms can make predictions for monitoring offensive conversations. We systematically evaluate 62 classifiers representing 19 major algorithmic families …
Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but may suffer from difficulties when representing higher dimensional surfaces. Motivated by…
Toxicity analysis and prediction are of paramount importance to human health and environmental protection. Existing computational methods are built from a wide variety of descriptors and regressors, which makes their performance analysis difficult. For example, deep neural network (DNN), a successful approach in many o…
Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans. However, there has not b…
Study identifies two borrowing patterns in UK payday loan users.
Proposes dynamic borrowing method for historical data in clinical trials.
We propose the development of a prediction market for forecasting prices for "toxic assets" to be transferred from Irish banks to the National Asset Management Agency (NAMA). Such a market allows market participants to assume a stake in a security whose value is tied to a future event. We propose that securities are cr…
This paper improves bond market making by adjusting hit-ratios for client flow quality.
Bayesian framework improves minority class performance in class-imbalanced data.
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure. Among the univariate methods, differential expression analysis (DEA) is often applied for its simplicity and interpretability. A characteristic of methods for DEA is that they treat genes individu…
Financial institutions face new model risks with AI, requiring enhanced model risk management.
Machine Learning (ML) is increasingly applied in real-life scenarios, raising concerns about bias in automatic decision making. We focus on bias as a notion of opinion exclusion, that stems from the direct application of traditional ML pipelines to infer subjective properties. We argue that such ML systems should be ev…
In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different? Toxicity classifiers demonstrate a counterfactual fairness issue by predicting that "Some people are gay" is toxic while "…
This work examines the effects of allowing borrowing in betting-based hypothesis testing.
Study on optimal portfolio selection with varying borrowing and saving rates in continuous-time markets.
This paper studies the properties of the optimal portfolio-consumption strategies in a {finite horizon} robust utility maximization framework with different borrowing and lending rates. In particular, we allow for constraints on both investment and consumption strategies, and model uncertainty on both drift and volatil…
New framework controls statistical dispersion for high-stakes applications.
This paper provides a framework for modeling financial contagion in a network subject to fire sales and price impacts, but allowing for firms to borrow to cover their shortfall as well. We consider both uncollateralized and collateralized loans. The main results of this work are providing sufficient conditions for exis…
This research secures deployed sentiment analysis models by identifying and defending against attack vectors.
New algorithm borrows future randomness to stabilize model-free control.
Through a short sale, a person borrows a share of stock from a lender, sells the borrowed share to a third person at the current price, and purchases an identical share in the market at a future date and at a future price to replace the borrowed share of stock. This only makes sense if the short seller anticipates a do…
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…
We propose a simple model of inter-bank borrowing and lending where the evolution of the log-monetary reserves of banks is described by a system of diffusion processes coupled through their drifts in such a way that stability of the system depends on the rate of inter-bank borrowing and lending. Systemic risk is ch…
The paper examines smoothness of value function in consumption-investment models with borrowing constraints.
DeFi lending protocols faced challenges during Ethereum's merge, but avoided major liquidations.
Paper uses BERT to assess P2P borrowers' credit risk from loan descriptions.
Study optimal consumption and portfolio strategies with no-borrowing constraint in financial markets.
The study calculates securities lending haircuts and indemnification costs.
We propose an in-depth study of lending behaviors in Kiva using a mix of quantitative and large-scale data mining techniques. Kiva is a non-profit organization that offers an online platform to connect lenders with borrowers. Their site, kiva.org, allows citizens to microlend small amounts of money to entrepreneurs (bo…
This article aims to explore an empirical approach to analyze the macroeconomicsdeterminants of default of borrowers. For this purpose, we have measured the impact of the adverse economic conditions on the degradation of the credit portfolio quality.In our paper, we have shed more light on the question of the aggravati…
Study quantifies information borrowing in hierarchical Bayesian models.
We develop a deep learning model of multi-period mortgage risk and use it to analyze an unprecedented dataset of origination and monthly performance records for over 120 million mortgages originated across the US between 1995 and 2014. Our estimators of term structures of conditional probabilities of prepayment, forecl…
We determine the optimal investment strategy of an individual who targets a given rate of consumption and who seeks to minimize the probability of going bankrupt before she dies, also known as {\it lifetime ruin}. We impose two types of borrowing constraints: First, we do not allow the individual to borrow money to inv…
Researchers develop method to protect against 'weight poisoning' attacks on pre-trained models.
In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for image classification, we have developed "Chemception", a deep CNN for the predict…