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.
There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One approach to resolving this is a shared response factor model. This assumes a shared and time synchronized stimulus across subjects. Such a model…
Unified framework for binary responses using AUC loss and low-rank constraint.
A new ICA model identifies shared brain activity patterns across subjects.
Estimates individualized treatment effects using shared RBF-net neurons.
New method identifies shared topics in LLM inputs and outputs for better detection of hallucinations.
The large majority of risk-sharing transactions involve few agents, each of whom can heavily influence the structure and the prices of securities. This paper proposes a game where agents' strategic sets consist of all possible sharing securities and pricing kernels that are consistent with Arrow-Debreu sharing rules. F…
Scorio.jl ranks systems from repeated tasks using various methods.
Study compares market microstructure between two South African exchanges.
Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work o…
Motivation: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions, and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms…
IPGP framework improves psychological assessment by integrating shared and unique traits.
We propose an adversarial learning approach for generating multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs). The GAN's generator is a modified hierarchical recurrent encoder-decoder network (HRED) and the discriminator is a word-level bidirec…
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights. Is there some correspondence between these neural network solutions? For linear networks, it has been shown that different instances of the same network architecture encode the same representatio…
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain…
Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our propos…
Proposes a new method for rank-consistent ordinal regression without weight-sharing constraints.
Paper reinterprets marginal productivity theory using vectorial products, challenging traditional ethical interpretations.
The paper examines stability of shares in Proof of Stake protocol, identifying different investor behaviors and phase transitions.
New metric compares noisy neural trajectories using optimal transport.
New model extracts shared brain activity patterns from fMRI data.
When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty into the system. The quantification of the impact of input uncertainty on output response has been extensively studied. Most…
The vast majority of current machine learning algorithms are designed to predict single responses or a vector of responses, yet many types of response are more naturally organized as matrices or higher-order tensor objects where characteristics are shared across modes. We present a new machine learning algorithm BaTFLE…
The standard linear and logistic regression models assume that the response variables are independent, but share the same linear relationship to their corresponding vectors of covariates. The assumption that the response variables are independent is, however, too strong. In many applications, these responses are collec…
Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug thera-pies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corr…
Research shows eco-innovation boosts earnings management, especially in constrained firms.
PIMA autoencoders discover shared features in multimodal scientific data.
Paper identifies a shared toolkit of strategies for risk management across fields.
Unified framework for response-adaptive targeting in multi-treatment experiments
New method estimates effects of multiple nutrients on blood glucose.
Higher environmental performance linked to more tax avoidance, especially for financially constrained firms.
This paper compares two loss functions for learning from aggregated responses and introduces an interpolating estimator.
Unified multitask learning framework for mixed-type outcomes.
FedIRT enables privacy-preserving psychometric estimation without centralizing data.
Supply Chain Management often requires independent organizations to work together to achieve shared objectives. This collaboration is necessary when coordinated actions benefit the group more than the uncoordinated efforts of individual firms. Despite the commonly reported benefits that can be gained in close relations…
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to bea…
We address the question of how stock prices respond to changes in demand. We quantify the relations between price change over a time interval and two different measures of demand fluctuations: (a) , defined as the difference between the number of buyer-initiated and seller-initiated trades, and (b) , def…
Response time improves alignment with diverse human preferences.
Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. We propose Bayesian Tensor Filtering (BTF), a hierarchical Bayesian model …
We study the following basic machine learning task: Given a fixed set of -dimensional input points for a linear regression problem, we wish to predict a hidden response value for each of the points. We can only afford to attain the responses for a small subset of the points that are then used to construct linear pre…
SHHK Stock Connect increases A-H share price premium, more for less efficient markets.
Paper introduces SDM for detecting LLM hallucinations, improving on entropy tests.
In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics. The problem of ranking response surfaces is motivated by estimating optimal feedback policy maps in stochastic control problems, aiming to efficiently find the index…
Proposes SROF for row-wise fusion in federated learning for multivariate responses.
In both mobile and web applications, speeding up user interface response times can often lead to significant improvements in user engagement. A common technique to improve responsiveness is to precompute data ahead of time for specific activities. However, simply precomputing data for all user and activity combinations…
Hierarchical AI multi-agent framework optimizes equity portfolios in China's A-share market.
Corrected moment-based methods improve inference in topic model regression.
The creation of social ties is largely determined by the entangled effects of people's similarities in terms of individual characters and friends. However, feature and structural characters of people usually appear to be correlated, making it difficult to determine which has greater responsibility in the formation of t…
Current multi-view factorization methods make assumptions that are not acceptable for many kinds of data, and in particular, for graphical data with hierarchical structure. At the same time, current hierarchical methods work only in the single-view setting. We generalize the Treelet Transform to the Multi-View Treelet …