arXiv research

Predicts clinical events using a landmark approach with machine learning for large biomarker histories.

Neural network model predicts alternating event-free periods.

The study examines Cox models for lifetime loan default risk, addressing biased estimates by incorporating recurrent events.

Thanks to the rise of wearable and connected devices, sensor-generated time series comprise a large and growing fraction of the world's data. Unfortunately, extracting value from this data can be challenging, since sensors report low-level signals (e.g., acceleration), not the high-level events that are typically of in…

Paper introduces a novel point process model for graph data using GNNs.

Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles…

DynForest predicts event probabilities from longitudinal data, handling endogenous predictors.

Develops FSC for maxima nominated samples, improving classification in rare-event data.

Unified framework detects changes in complex system models.

FSNet improves online time series forecasting by balancing fast adaptation and old knowledge.

Study finds non-adherence to schizophrenia meds leads to earlier adverse events.

Study identifies two borrowing patterns in UK payday loan users.

Technology offers new ways to measure the locations of the players and of the ball in sports. This translates to the trajectories the ball takes on the field as a result of the tactics the team applies. The challenge professionals in soccer are facing is to take the reverse path: given the trajectories of the ball is i…

The P300 event-related potential (ERP), evoked in scalp-recorded electroencephalography (EEG) by external stimuli, has proven to be a reliable response for controlling a BCI. The P300 component of an event related potential is thus widely used in brain-computer interfaces to translate the subjects' intent by mere thoug…

Tensor networks improve b-jet classification in high-energy physics.

We consider the forecast aggregation problem in repeated settings, where the forecasts are done on a binary event. At each period multiple experts provide forecasts about an event. The goal of the aggregator is to aggregate those forecasts into a subjective accurate forecast. We assume that experts are Bayesian; namely…

Firms miscount their customers who stop buying without saying goodbye.

Random shuffle method boosts HF dataset size 10-21 times.

Algorithm learns to bid in auctions with shilling, masking real bids.

Optimal strategies are found for a repeated betting game using diffusion approximation.

Convolutional networks struggle with repeating patterns in ECGs.

We study two systems of tangle equations that arise when modeling the action of the Integrase family of proteins on DNA. These two systems--direct and inverted repeats--correspond to two different possibilities for the initial DNA sequence. We present one new class of solutions to the tangle equations. In the case of i…

Study explores algorithmic collusion in repeated games using various learning dynamics.

Scorio.jl ranks systems from repeated tasks using various methods.

We investigate the question of when distinct branched surfaces in the complement of a 2-bridge knot support essential surfaces with identical boundary slopes. We determine all instances in which this occurs and identify an infinite family of knots for which no boundary slopes are repeated.

LAFF algorithm balances adaptability and non-exploitability in repeated games.

Link concordance and Whitney towers linked to Milnor invariants.

Neural Diffusion Intensity Models simplify Cox processes inference.

Risk is part of the fabric of every business; surprisingly, there is little work on establishing best practices for systematic, repeatable risk identification, arguably the first step of any risk management process. In this paper, we present a proposal that constitutes a more holistic risk management approach, a method…

It has long been known that a Milnor invariant with no repeated index is an invariant of link homotopy. We show that Milnor's invariants with repeated indices are invariants not only of isotopy, but also of self C_k-moves. A self C_k-move is a natural generalization of link homotopy based on certain degree k clasper su…

We investigate some geometric properties of the real algebraic variety $Δ$ of symmetric matrices with repeated eigenvalues. We explicitly compute the volume of its intersection with the sphere and prove a Eckart-Young-Mirsky-type theorem for the distance function from a generic matrix to points in $Δ$. We exhibit conne…

This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows si…

Machine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network graph based on the data topology. When applying this to two case studies, accuracy exceeds alternative models wit…

Study optimizes product assortment for retailers with repeated exposures and patience costs.

Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph. However, because common neighbors are shared between different nodes, this leads to repeated and inefficient computations. We propose Hierarchically Aggregated computation Graphs (HAGs), a new GNN graph re…

We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. Th…

Study analyzes broker's gain from trade in repeated context-based trading.

A new method combines machine learning with mixed-effects models for better repeated measurement analysis.

Repeated self-distillation improves model performance significantly.

Develops a neural model to predict event occurrence and timing.

New method interprets machine learning forecasts as historical analogies.

Whether stochastic or parametric, the Pareto/NBD model can only be utilized for an in-sample prediction rather than an out-of-sample prediction. This research thus provides a neural network based extension of the Pareto/NBD model to estimate the out-of-sample parameters, which overrides the estimation burden and the ap…

Paper proposes a new trading strategy using corporate event detection from news articles.

XSPNs combine SPNs and MEVMs for efficient inference in data with repeated parts.

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem…

AUC is unreliable in rare event settings but stable with moderate numbers of events.

Non-spanning identification of scheduled event risk in option pricing.

New approach predicts event probabilities for better event detection.