Study examines how event rate affects bankruptcy prediction model performance.
problem Effect of event rate on bankruptcy prediction model performance.
method Oversampled event rates from 0.12% to 50%, developed and evaluated 7 models.
result Bayesian Network is least sensitive to event rate, SVM most sensitive.
The paper identifies key macroeconomic events affecting exchange rate volatility.
problem Understanding which macroeconomic events impact exchange rate volatility.
method Data-driven approach to select relevant macroeconomic events using sparsity-based methods.
result The identified macroeconomic events significantly impact exchange rate volatility.
New methods for inferring, predicting, and estimating continuous-time, discrete-event processes.
problem Inferring, predicting, and estimating entropy rate of continuous-time, discrete-event processes.
method Bayesian structural inference extended with neural networks.
result Methods are competitive for prediction and entropy-rate estimation with state-of-the-art.
AUC is unreliable in rare event settings but stable with moderate numbers of events.
problem Misleading performance metrics in rare event settings.
method Simulation study varying dataset sizes and event rates.
result AUC is unreliable in rare event settings but stable with moderate numbers of events.
Bayesian model improves categorization of explosions from sparse data.
problem Challenges in categorizing explosions from limited data.
method Bayesian update to Event Categorization Matrix model with Bayesian Decision Theory.
result Consistent gains in overall accuracy and lower false negative rates.
Event-based control improves neural network training speed and accuracy.
problem Optimizing learning rate and gradient tuning for CNN convergence.
method Two Event-Based control loops for adjusting learning rate in E/PD algorithm.
result Event-Based E/PD control leads to higher final accuracy and lower final loss.
Enhanced ad event prediction boosts performance.
problem Improving ad performance metrics like CTR and CVR.
method New feature engineering approach for ad event prediction.
result Significantly outperforms alternative prediction algorithms.
MOB-dS uses permutation to correct for dependency in discrete survival data.
problem Identifying subgroups in discrete event time data with potential spurious results.
method Model-based recursive partitioning (MOB) with modified data matrix and permutation test.
result MOB-dS controls type I error rate better than standard MOB for discrete survival data.
Bayesian method corrects timing misalignment in recurrent event studies.
problem Estimating differences in event rates under two treatments with timing misalignment.
method g-computation procedure with joint semiparametric Bayesian model.
result Correctly estimates average causal effects under right-censoring.
Calibrates Hawkes models for market events, revealing power-law feedback kernels.
problem Estimating the influence of past events and price changes on future market events.
method Proposes a calibration procedure for Quadratic Hawkes models, analyzing the kernel components.
result Empirically calibrated kernel components reveal power-law behavior, suggesting system near critical point.
Study improves crash rate forecasting in Washington, D.C. using stochastic volatility model.
problem Forecasting crash rates in areas with irregular traffic patterns and exogenous events.
method Adopted a stochastic volatility model to capture heterogeneity and temporal instability.
result The stochastic volatility model outperforms conventional models in forecasting crash rates in Washington, D.C.
We study large deviations and rare default clustering events in a dynamic large heterogeneous portfolio of interconnected components. Defaults come as Poisson events and the default intensities of the different components in the system interact through the empirical default rate and via systematic effects that are comm…
Research examines impact of Brexit on GBP/EUR exchange rate.
problem Impact of Brexit on GBP/EUR exchange rate.
method Investigates financial market reactions to Brexit news.
result Magnitude and direction of impact on GBP/EUR exchange rate.
Proposes a new framework to disentangle event influences in MTPP.
problem Underexplored how individual events influence overall dynamics over time.
method Decoupled MTPP framework using Neural Ordinary Differential Equations (Neural ODEs).
result Significantly improves performance on real-life datasets compared to state-of-the-art methods.
Simple tabular event prediction model outperforms existing methods.
problem Predicting events from tabular data with historic events.
method Standard autoregressive LLM-style transformers with elementary positional embeddings and causal language modeling.
result Simple model outperforms existing approaches across various datasets and use-cases.
A new method prices time-to-event cash flows using survival analysis.
problem Pricing insurance investment portfolios with time-to-event cash flows.
method Discrete-time survival analysis framework, hazard rate estimators, asymptotic multivariate normality.
result Pricing model yields estimates closer to actual cash flows than non-random models.
Event-triggered learning reduces communication in networked control systems.
problem Reduction of communication in networked control systems.
method Triggered learning experiments when communication performance is poor, using statistical properties of inter-communication times.
result Event-triggered learning improves robustness and communication efficiency.
Neural surrogate predicts SPN rates from token trajectories.
problem Challenging parameter estimation in SPNs with covariates.
method 1D Convolutional Residual Network trained on Gillespie-simulated SPN realizations.
result Surrogate predicts rate-function coefficients with RMSE = 0.043.
Paper improves Native ads CTR prediction using event embeddings and recurrent networks.
problem Hard CTR prediction for Native ads due to lack of direct query intent.
method Proposes a large-scale event embedding scheme and a recurrent neural network model.
result Significantly outperforms baseline and variants in CTR prediction.
The paper explores how control variates can reduce variance in Monte Carlo simulations, especially for Sobolev functions.
problem Efficiency of control variates in reducing variance for Monte Carlo simulations.
method Study of a specific quadrature rule using nonparametric regression-adjusted control variates.
result A specific quadrature rule can improve the Monte Carlo rate and achieve the minimax optimal rate under sufficient smoothness assumptions.
ET-GP-UCB optimizes time-varying functions without knowing change rates.
problem Sequentially optimizing a time-varying objective function with unknown change rates.
method Event-triggered Bayesian optimization with adaptive resets based on probabilistic uniform error bounds.
result ET-GP-UCB outperforms other GP-UCB algorithms in synthetic and real-world data.
UNHaP removes noise from physiological events using Hawkes processes.
problem Challenges in identifying true events from spurious ones in physiological signal analysis.
method UNHaP uses marked Hawkes processes to distinguish and unmix true events from noise.
result UNHaP significantly reduces false detection rates and enhances event understanding.
Study on massless Vlasov equation on Reissner-Nordström spacetimes, showing decay rates and non-decay phenomena.
problem Analyzing decay and non-decay rates of solutions to the massless Vlasov equation on Reissner-Nordström spacetimes.
method Quantitative analysis of geodesic flow and comparison to wave equation instability results.
result Exponential decay rates in subextremal cases and polynomial rates in extremal cases, with non-decay of transversal derivatives in extremal cases.
Study finds no significant impact of US sovereign credit rating downgrade on equity market.
problem Impact of US sovereign credit rating downgrade on US equity market.
method Event study methodology using three companies and S&P500 index.
result No significant effects of US sovereign credit rating downgrade on US equity market.
Paper proposes a new trading strategy using corporate event detection from news articles.
problem Predicting stock movements based on corporate events from news articles.
method Bi-level event detection model: low-level for token-level event identification, high-level for article-level event identification.
result The proposed strategy outperforms existing models in stock prediction metrics.
Adaptive prediction timing improves healthcare outcomes by predicting patient events at the right frequency.
problem Inconsistent prediction granularity in healthcare models.
method Introduces a novel approach using Bayesian recurrent models and a new aggregation method to adapt prediction frequency based on uncertainty.
result Adaptive prediction timing leads to improved predictive performance, especially in the critical first 12 hours of patient stay.
Deep Recurrent Survival Analysis models for better event prediction and survival rate estimation.
problem Survival analysis challenges in handling data censorship and sequential patterns.
method Combines deep learning for conditional probability prediction and survival analysis for censorship handling.
result Significantly outperforms state-of-the-art solutions in various metrics on real-world tasks.
Data of the form of event times arise in various applications. A simple model for such data is a non-homogeneous Poisson process (NHPP) which is specified by a rate function that depends on time. We consider the problem of having access to multiple independent observations of event time data, observed on a common inter…
Study compares resampling methods for rare event prediction in longitudinal studies.
problem Predicting rare events in longitudinal follow-up studies.
method Comparison of resampling methods to improve standard regression models.
result Effect of sampling rate on model predictive performance.
A new compressed sensing system speeds up PSTE detection.
problem Accurately detecting fast, small optical events (PSTEs) is challenging.
method Developed a novel compressed sensing algorithm for rolling shutter systems.
result Accurately recovered PSTEs with spatial undersampling, showing speed and quality advantages.
Study compares ZBDT model to BDT for financial derivatives valuation.
problem Valuation of financial derivatives under catastrophic events.
method Introduced Zero Black-Derman-Toy (ZBDT) model with jumps to zero interest rate.
result ZBDT model better matches financial slowdown risk.
New approach detects and ranks novel and developing cyber threats in Twitter.
problem Detecting and ranking novel and developing cyber threats in Twitter streams.
method Unsupervised machine learning approach focusing on novelty and trendiness.
result Ranking of cyber threat events based on importance score using extracted terms.
Event-based learning reduces communication in distributed networks.
problem Distributed learning with diverse data distributions and communication inefficiencies.
method A distributed learning algorithm using ADMM with event-triggered communication.
result The algorithm converges even with distinct local data distributions and achieves accelerated convergence in convex settings.
The dynamical behavior of the currency exchange rate after its large-scale catastrophe is discussed through a case study of the rate of Russian rubles to US dollars after its crash in 2014. It is shown that, similarly to the case of the stock market crash, the relaxation is characterized by a power law, which is in ana…
Nonparametric neural-network estimation of current-status data
problem Estimation of conditional cumulative distribution function with current-status data
method Neural-network sieve maximum likelihood estimator
result Explicit convergence rate for Hölder smoothness
Develops a method to simulate rare dangerous events in autonomous systems.
problem Rare dangerous events in safety-critical systems are hard to test in real-world settings.
method Combines exploration, exploitation, and optimization techniques for rare-event simulation.
result Provides rigorous guarantees for the performance of the method.
The paper analyzes logistic regression for rare events data, deriving new insights on estimator efficiency and sampling strategies.
problem Binary logistic regression for rare events data with significantly fewer events than controls.
method Derives asymptotic distribution of MLE, proves under-sampling advantage, and compares over-sampling efficiency.
result Under-sampling a small proportion of nonevents can improve efficiency in rare events data analysis.
We present conditions under which positive alpha exists in the realm of active portfolio management- in contrast to the controversial result in Jarrow (2010, pg. 20) which implicates delegated portfolio management by surmising that positive alphas are illusionary. Specifically, we show that the critical assumption used…
Study examines USD exchange rate dynamics using Kramers-Moyal expansion.
problem Understanding and predicting exchange rate instability.
method Kramers-Moyal expansion and Fokker-Planck formalism applied to log-return data.
result Identifies a stabilizing linear drift and nonlinear diffusion term in exchange rate fluctuations.
Study examines cyber losses across sectors, finds high severity and frequency.
problem Understanding the nature of cyber losses and their variability across sectors.
method Analysis of a leading industry dataset of cyber events, focusing on frequency and severity.
result Cyber risks are heavy-tailed, with high probability of extreme losses.
ELUQuant quantifies uncertainties in DIS events using BNNs and MNFs.
problem Uncertainty quantification in Deep Inelastic Scattering (DIS) events.
method Physics-informed Bayesian Neural Network with flow approximated posteriors.
result Effective extraction of kinematic variables x, Q2, and y with detailed event-level uncertainty. Empirical study on UEEs reveals liquidity's role and universal recovery patterns.
problem Understanding and stabilizing financial markets affected by UEEs.
method Comparative analysis of UEEs over different years in US stock market.
result Liquidity is dominant in UEEs emergence and recovery patterns are universal.
Study adapts liquidity model to equity auctions, revealing accelerated event rates and reduced price impact.
problem Understanding and predicting price dynamics in equity auctions.
method Adapted latent/revealed order book framework to equity auctions, measuring order submissions, cancellations, and diffusion rates.
result Equity auctions exhibit accelerated event rates leading to reduced price impact and decreased volatility.
New algorithm identifies best arm in rare event scenarios.
problem Identifying the best arm with tiny reward probability.
method Approximated Compound Poisson process for faster algorithms.
result Improved computational efficiency with minor sample complexity increase.
Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics. Streaming event data are discrete observation occurred in continuous time, and the precise time interval between tw…
Paper develops deep learning for predicting clinical endpoints from diverse medical records.
problem Predicting clinical endpoints from heterogeneous, irregularly visiting medical records.
method Proposes a novel model with a new gate to control visiting rates of different events.
result Model effectively predicts death and abnormal lab tests with real-world clinical data.
New algorithm speeds up MCMC for complex distributions.
problem Efficient sampling from complex, high-dimensional distributions.
method Numerical Generalized Randomized Hamiltonian Monte Carlo with state-dependent event rates.
result Approximates Hamiltonian trajectories for robust sampling.
The study examines Hawkes processes and their long-term behavior.
problem Understanding the long-term behavior of Hawkes processes.
method Proving functional limit theorems under various conditions on the dispersion of child events.
result Functional limit theorems hold for Hawkes processes with different levels of child event dispersion.