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.
A new algorithm converts staged trees into Chain Event Graphs.
The paper introduces staged event trees for transparent treatment effect estimation.
R package stagedtrees learns staged tree structures from data.
New framework estimates staged tree models using hierarchical clustering on the probability simplex.
New classifiers account for context-specific independences.
New algorithm efficiently learns sparse staged trees.
New algorithms learn staged trees from incomplete data.
New algorithms learn simple staged trees from data, improving model fit.
New model explains volatility after extreme stock market events.
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hi…
Bayesian networks are simplified for categorical variables using staged trees and asymmetry-labeled DAGs.
Hippo optimizes deep learning hyper-parameters by reducing redundant trials.
Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations…
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hi…
Study infers tree topology from customer data using contrastive learning.
We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF). "Best-scored" means to select one regression tree with the best empirical performance out of a certain number of purely random regression tree candidates, and "two-stage" means to divide the or…
Boost-R uses gradient boosted trees for analyzing recurrence data.
Study a risk model with tree-structured Poisson-Markov random field for rainfall events.
Proposes a two-stage method for estimating heterogeneous treatment effects using gradient boosting trees.
Boosted tree method improves MTL in heterogeneous domains.
Machine learning tools are commonly used in modern high energy physics (HEP) experiments. Different models, such as boosted decision trees (BDT) and artificial neural networks (ANN), are widely used in analyses and even in the software triggers. In most cases, these are classification models used to select the "signal"…
Proposes a new tree-based algorithm for class-imbalanced data.
The problem of maximum-likelihood (ML) estimation of discrete tree-structured distributions is considered. Chow and Liu established that ML-estimation reduces to the construction of a maximum-weight spanning tree using the empirical mutual information quantities as the edge weights. Using the theory of large-deviations…
JSRT improves regression tree performance by incorporating global node information.
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on pred…
Develops a two-level monotonic multistage recommender system for better user-specific prediction.
NSOTree combines neural networks and trees for better survival analysis interpretability.
Develops a Bayesian method for causal inference with partly censored time-to-event data.
New tests for conditional copulas based on decision trees.
Using ensemble methods for regression has been a large success in obtaining high-accuracy prediction. Examples are Bagging, Random forest, Boosting, BART (Bayesian additive regression tree), and their variants. In this paper, we propose a new perspective named variable grouping to enhance the predictive performance. Th…
Sound event detection systems typically consist of two stages: extracting hand-crafted features from the raw audio waveform, and learning a mapping between these features and the target sound events using a classifier. Recently, the focus of sound event detection research has been mostly shifted to the latter stage usi…
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (> 10s) such as sleep stages, and micro-events (<2s) such as spindles and K-complexes. Annotations of such events require a trained sleep expert, a time co…
This research detects and identifies human-made objects in 3D point clouds using novel methods.
Finite-horizon lookahead policies are abundantly used in Reinforcement Learning and demonstrate impressive empirical success. Usually, the lookahead policies are implemented with specific planning methods such as Monte Carlo Tree Search (e.g. in AlphaZero). Referring to the planning problem as tree search, a reasonable…
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-…
It is known that describing or calculating the conditional probabilities of multiple events is exponentially expensive. In this work, Bayesian tensor network (BTN) is proposed to efficiently capture the conditional probabilities of multiple sets of events with polynomial complexity. BTN is a directed acyclic graphical …
The paper uses machine learning to find causal rules from business process logs.
Method uses trinomial trees to price nontraditional options.
This paper examines a novel gradient boosting framework for regression. We regularize gradient boosted trees by introducing subsampling and employ a modified shrinkage algorithm so that at every boosting stage the estimate is given by an average of trees. The resulting algorithm, titled Boulevard, is shown to converge …
The paper proposes a method to infer differentiation trees from RNA velocity data.
Various and ubiquitous information systems are being used in monitoring, exchanging, and collecting information. These systems are generating massive amount of event sequence logs that may help us understand underlying phenomenon. By analyzing these logs, we can learn process models that describe system procedures, pre…
The paper tackles robust classification trees for distribution shifts, improving accuracy in public health and social work.
DynForest predicts event probabilities from longitudinal data, handling endogenous predictors.
Proposes a meta-algorithm for classification with overlapping classes in high-energy physics.
In this paper, we consider multi-stage stochastic optimization problems with convex objectives and conic constraints at each stage. We present a new stochastic first-order method, namely the dynamic stochastic approximation (DSA) algorithm, for solving these types of stochastic optimization problems. We show that DSA c…
Fast nonparametric conditional independence testing via two-stage regression
A market-maker-based prediction market lets forecasters aggregate information by editing a consensus probability distribution either directly or by trading securities that pay off contingent on an event of interest. Combinatorial prediction markets allow trading on any event that can be specified as a combination of a …