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.
We describe and extract time-ordered multibody interactions from complex systems.
Agent-based model simulates market dynamics with real-time order matching.
New method resolves time order in genetic mutation models.
This paper investigates the relationship between algebraic quantum field theories and factorization algebras on globally hyperbolic Lorentzian manifolds. Functorial constructions that map between these two types of theories in both directions are developed under certain natural hypotheses, including suitable variants o…
We develop a new method to price SOFR futures contracts considering convexity, skew, and smile.
We argue that an important contributing factor into market inefficiency is the lack of a robust mechanism for the stock price to rise if a company has good earnings, e.g., via buybacks/dividends. Instead, the stock price is prone to volatility due to rather random perception/interpretation of earnings announcements (am…
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statisti…
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000). Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, …
We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variat…
New AI technique explains neural net decisions over time.
Paper uses bipartite graph to forecast cross-market returns, revealing asymmetry.
fastml guards against data leakage in automated machine learning.
Continuous-time Kyle model shows privacy subsidy from noise-perturbed order flow.
Order-flow entropy predicts price magnitude without directionality.
Unfair stock trading strategies have been shown to be one of the most negative perceptions that customers can have concerning trading and may result in long-term losses for a company. Investment banks usually place trading orders for multiple clients with the same target assets but different order sizes and diverse req…
New method uses models from regularity structures as features in machine learning.
Combines Bézier curves with Gaussian processes for better sequential data modeling.
Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the …
The classification of time series data is a challenge common to all data-driven fields. However, there is no agreement about which are the most efficient techniques to group unlabeled time-ordered data. This is because a successful classification of time series patterns depends on the goal and the domain of interest, i…
Granger causality has been used for the investigation of the inter-dependence structure of the underlying systems of multi-variate time series. In particular, the direct causal effects are commonly estimated by the conditional Granger causality index (CGCI). In the presence of many observed variables and relatively sho…
Paper proposes BOCPD for real-time order flow and market impact prediction.
Surrogate-based analysis of interactions via local effect smooths
Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless featur…
A new method detects interactions in neural networks using topological analysis.
iKF method uncovers complex variable interactions for scientific discovery.
A graph neural network detects beneficial feature interactions for recommender systems.
Dropout regularizes against high-order interactions by canceling interaction rates.
Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …
Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent latent functions, all current MTGPs including the salient linear model of coregionalization (LMC) and convolution frameworks cannot effectively…
A semigroup of annuli integrates a central extension of vector fields on S^1.
SocialInteractionGAN generates realistic human interactions from low-dimensional data.
A new method detects interactions in machine learning models.
xDeepInt learns both vector-wise and bit-wise feature interactions.
In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions. Specifically, we propose a graph neural network that jointly predicts the discrete interaction modes and 5-second future trajectories for all agents in the scene. Our model infers an inte…
Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generate low-dimensional embeddings of entities and relations, and use these to make inferences. ConvE, a recently proposed approach, applies convo…
This study explores how feature graphs enhance GNNs' performance in modeling interactions.
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can a…
We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structu…
Paper introduces IC-index to evaluate interaction prediction methods.
This paper uses Gaussian mixtures to mimic interactions in large language models.
Study reveals self-attention's role in learning and generalizing interactions.
Archipelago provides interpretable explanations of feature interactions in machine learning models.
Automated tests detect interactions in unstructured data.
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of exist…
Recommender systems, medical diagnosis, network security, etc., require on-going learning and decision-making in real time. These -- and many others -- represent perfect examples of the opportunities and difficulties presented by Big Data: the available information often arrives from a variety of sources and has divers…
PIN models feature interactions using a neural network that mimics decision trees.
The paper explains DNNs by quantifying interactions among input variables.
Modeling continuous movement of entities in latent space for interaction timing.