This paper uses MIL and MHCNN-RNN to predict precursors to aviation safety events.
problem Identifying events that precede aviation safety incidents.
method Multiple-instance learning (MIL) framework combined with a Multi-Head Convolutional Neural Network-Recurrent Neural Network (MHCNN-RNN) architecture.
result Multiple binary classifiers outperform in predicting high speed and high path angle events during the approach phase.
New deep learning method handles rare and imbalanced events in time series.
problem Challenges in event detection in time series data, especially rare and imbalanced events.
method Supervised regression-based deep learning approach that handles various types of events.
result Superior performance across diverse domains, particularly for rare events and imbalanced datasets.
Study infers tree topology from customer data using contrastive learning.
problem Inferring local network topology from customer data.
method Contrastive learning approach for binary event encoding from continuous time series.
result Preliminary results show potential for valuable encoder learning.
Adaptive Multilevel Splitting improves rare event pricing for financial derivatives.
problem Efficient pricing of binary options in rare event regimes with discontinuous payoffs.
method Adaptive Multilevel Splitting (AMS) reformulates rare-event problem as conditional events.
result AMS achieves up to 200-fold improvements over standard Monte Carlo, preserving unbiasedness.
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
problem Predicting CLABSI risk in hospital admissions using EHR data with competing risks.
method Comparison of static and dynamic random forest models for binary, multinomial, survival, and competing risks outcomes.
result Static and dynamic random forest models perform similarly in predicting CLABSI risk, with multinomial models having the lowest computation times.
We present the Infinite Latent Events Model, a nonparametric hierarchical Bayesian distribution over infinite dimensional Dynamic Bayesian Networks with binary state representations and noisy-OR-like transitions. The distribution can be used to learn structure in discrete timeseries data by simultaneously inferring a s…
The paper categorizes and analyzes various event-linked perpetual futures contracts.
problem Developing a risk-design framework for complex event-linked perpetual futures.
method Formal taxonomy of seven pure-form canonical variants, organized along four design axes.
result Detailed analysis of microstructure properties and limitations of various variants.
What do binary (or probabilistic) forecasting abilities have to do with overall performance? We map the difference between (univariate) binary predictions, bets and "beliefs" (expressed as a specific "event" will happen/will not happen) and real-world continuous payoffs (numerical benefits or harm from an event) and sh…
New method detects TC imagery patterns for rapid intensity change.
problem Detecting upcoming rapid intensity changes in TC satellite imagery.
method Nonparametric test of association between images and event labels using neural networks and bootstrap.
result Identifies archetypes of infrared imagery associated with elevated rapid intensification risk.
Develops a neural model to predict event occurrence and timing.
problem Standard event time models ignore the distinction between event occurrence probability and predicted time.
method Introduces a conditional event time model using a neural network with a binary stochastic layer.
result Shows superior event occurrence and timing predictions on various datasets.
Develops forecast hedging for improved calibration of forecasts.
problem Improving the accuracy of forecasted frequencies.
method Combines deterministic and stochastic approaches to forecast hedging.
result Ensures expected track record can only improve.
Parity calibration aims to predict increase-decrease events, not values.
problem Forecasting future increase-decrease events rather than exact values.
method Online binary calibration method to achieve parity calibration.
result Online binary calibration achieves parity calibration effectively.
gOMP algorithm selects features for various types of data.
problem Feature selection for scalable molecular data.
method Generalized Orthogonal Matching Pursuit algorithm for multiple types of data.
result gOMP performs similarly or better than LASSO on various datasets.
VOWEL trains WTA-SNNs for multi-valued events, overcoming resource limitations.
problem Training WTA-SNNs for multi-valued events is challenging due to non-differentiability and recurrent behavior.
method Develops a variational online local training rule (VOWEL) for WTA-SNNs using local pre- and post-synaptic information and a common reward signal.
result VOWEL outperforms conventional binary SNNs in real-world neuromorphic datasets with multi-valued events.
DisCoveR efficiently discovers declarative process models from event logs.
problem Mining declarative process models from event logs efficiently and accurately.
method DisCoveR precisely formalizes an algorithm, uses a bit vector implementation, and rigorously evaluates performance.
result DisCoveR outperforms other declarative miners in accuracy and runtime.
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.
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 Neural Networks detect gravitational wave events with high accuracy and real-time potential.
problem Detecting and identifying the full duration of compact binary coalescence events in gravitational wave data.
method Integrating Bayesian approach into a CLDNN classifier that combines CNN and LSTM for event detection and uncertainty estimation.
result Successfully detected all seven BBH events in LIGO Livingston O2 data with high accuracy.
We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual d…
New algorithm detects unique events in time series data.
problem Detecting anomalous events in unknown properties.
method Model-free, unsupervised detection using Temporal Outlier Factor (TOF).
result TOF outperforms traditional outlier detection methods.
Here we present a novel approach to statistical analysis of financial time series. The approach is based on n-grams frequency dictionaries derived from the quantized market data. Such dictionaries are studied by evaluating their information capacity using relative entropy. A specific quantization of (originally conti…
In this article, we study the problem of pricing defaultable bond with discrete default intensity and barrier under constant risk free short rate using higher order binary options and their integrals. In our credit risk model, the risk free short rate is a constant and the default event occurs in an expected manner whe…
Study compares two methods for predicting extreme atmospheric events.
problem Forecasting threshold exceedances of atmospheric variables like temperature and wind speed.
method Direct vs. full distribution probabilistic methods for rare events.
result Full distribution approach outperforms direct method for extreme events.
In this article, we consider a 2 factors-model for pricing defaultable bond with discrete default intensity and barrier where the 2 factors are stochastic risk free short rate process and firm value process. We assume that the default event occurs in an expected manner when the firm value reaches a given default barrie…
ENTED efficiently decomposes binary and count tensors using nonparametric Gaussian processes.
problem Handling high-dimensional and sparse binary and count data with traditional tensor decompositions.
method ENTED uses nonparametric Gaussian processes and sparse orthogonal variational inference to handle binary and count tensors.
result ENTED outperforms traditional methods in binary and count tensor completion tasks.
New split rules improve subpopulation targeting in policy-making.
problem Improving binary classification for subpopulation targeting in policy-making.
method MDFS, PFS, wEFS for maximizing distance and penalizing final splits.
result Proposed methods target more vulnerable subpopulations than classic CART/KD-CART.
Binary and multiclass epilepsy detection methods using EEG features.
problem Epilepsy diagnosis from EEG data.
method Feature extraction from power spectrum, spectrogram, and bispectrogram; eight machine learning algorithms used.
result Random forest and backpropagation algorithms achieved highest accuracy for binary and multiclass classification.
In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sens…
The paper explores anticipative binary information in financial markets using Brownian motion and Poisson processes.
problem Capturing anticipative information in financial markets with Brownian motion and Poisson processes.
method Using Malliavin calculus and filtration enlargement techniques, the paper computes the semimartingale decomposition of the processes.
result The paper provides the exact value of anticipative information in the pure jump case.
Develops a new framework for perpetual futures on binary prediction markets.
problem Lack of effective risk management in perpetual futures on binary prediction markets.
method PIRAP framework with six components: index estimator, margin sizing, leverage, funding rule, halt protocol, and eligibility framework.
result Mixed results from empirical evaluation, with some pre-registered floors passing and others failing.
Paper explores learning patterns in binary sequences, finding no method consistently outperforms others.
problem Learning patterns in infinite binary sequences.
method Various learning methods are compared, finding no method consistently outperforms others.
result No learning method consistently outperforms others in predicting binary sequences.
Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom eva…
This study evaluates subgroup analysis methods for time-to-event outcomes in randomized controlled trials.
problem Identifying subgroups of good responders in non-significant randomized controlled trials.
method Evaluation of several subgroup analysis algorithms for time-to-event outcomes using synthetic and semi-synthetic data.
result Provides a new synthetic and semi-synthetic data generation process and an open-source Python package for benchmarking.
The paper estimates personalized treatment effects in medical settings with competing risks.
problem Estimating treatment effectiveness for specific events in the presence of alternative event types.
method Meta-learners combining Cox regression or random survival forests for risk modeling and elastic net regression or random forests for direct CATE modeling.
result Compared meta-learners in multiple simulation settings, providing practical guidance for model selection.
SurvITE learns treatment effects from time-to-event data, addressing unique challenges.
problem Inferring heterogeneous treatment effects from time-to-event data.
method Proposes a novel deep learning method for treatment-specific hazard estimation.
result Method outperforms baselines by addressing covariate shifts from various sources.
There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discret…
New test identifies risk spillovers in financial markets using extreme events.
problem Identifying risk spillovers in financial markets for systemic risk assessment.
method Novel Granger causality test in tail events using likelihood ratio statistic.
result Good size and power, especially for large sample size, inferring correct time scale.
ForesightFlow detects informed trading on prediction markets using an information leakage score.
problem Detecting informed trading on decentralized prediction markets.
method Developed an Information Leakage Score (ILS) framework to quantify the fraction of terminal information move priced in before public news events.
result The score connects label generation to proper-scoring-rule literature and reveals systematic biases in insider trading documentation.
Enhances patient failure prediction using dynamic survival models.
problem Lack of precise individual level prediction in conventional models.
method Developed counterfactual dynamic survival model (CDSM).
result Inflection point of estimated survival curves predicts patient failure time.
Recent events such as the global financial crisis have renewed the interest in the topic of economic networks. One of the main channels of shock propagation among countries is the International Trade Network (ITN). Two important models for the ITN structure, the classical gravity model of trade (more popular among econ…
A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve…
New method predicts wave height exceedance probabilities.
problem Forecasting significant wave height to prevent coastal disasters.
method Point forecasting approach using cumulative distribution function.
result Proposed method outperforms existing approaches.
PredictionMarketBench benchmarks trading agents on prediction markets.
problem Evaluating trading agents on prediction markets with realistic conditions.
method Deterministic replay of historical data, execution-realistic simulator, agent interface.
result Fee-aware algorithmic strategies outperform naive agents in volatile episodes.
A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time. Well calibrated predictions are particularly important when machine learning models are used in decision analysis. This paper presents two new non-parametric …
Develops a new volatility model for prediction markets.
problem Volatility forecasting in prediction markets differs from standard asset markets.
method Combines Wright-Fisher and Glosten-Milgrom mechanisms to model binary prediction markets.
result Structural model outperforms standard ARCH/GARCH models in volatility forecasting.
Develops a new volatility model for prediction markets.
problem Volatility forecasting in prediction markets differs from standard asset markets.
method Combines Wright-Fisher and Glosten-Milgrom mechanisms to model binary prediction markets.
result Structural model outperforms standard ARCH/GARCH models in volatility forecasting.
No-regret learning with strategic experts, incentivized.
problem Online learning with strategic experts who misreport beliefs.
method Building on wagering mechanisms, we provide algorithms for no-regret and incentive compatibility in both full and partial information settings.
result Our algorithms achieve no regret and incentive compatibility for myopic experts, with comparable regret to classic no-regret algorithms and diminishing regret for forward-looking agents.
Background: Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison. But some aspects have received little attention. First, most comparison studies treat prediction performance and variable…