Memorizing rare examples helps neural networks generalize better.
problem Improving generalization in deep learning models.
method Theoretical analysis and experiments on neural networks with composition capability.
result Memorizing rare examples can help neural networks make correct predictions on rare test examples.
Generative model improves scene graph prediction for rare compositions.
problem Challenges in inferring unseen or rare scene graph compositions.
method Synthesizing rare scene graphs by perturbing real ones, using conditional GANs.
result Marginal but consistent improvements in zero- and few-shot metrics.
A new method estimates rare events using tensor trains.
problem Estimating rare event probabilities in high-dimensional problems.
method Approximating optimal importance distribution via tensor-train decompositions and compositions.
result Better variance reduction and efficient computation of rare event probabilities.
This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.
problem Statistical issues with standard financial ratios at industry level.
method Compositional data analysis techniques for financial ratios.
result Improved analysis of financial ratios using compositional data methods.
Develops FSC for maxima nominated samples, improving classification in rare-event data.
problem Combining labeled and unlabeled data in rare-event scenarios.
method Introduces a latent representation to account for maxima nomination sampling.
result Improves classification performance in rare-event contamination mixtures.
Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the ex…
The paper introduces diagnostic transport maps to improve the reliability of rare event predictions.
problem Improper calibration of predictive distributions, especially for rare events.
method Diagnostic transport maps to adjust base model's probabilities for better calibration.
result Diagnostic transport maps improve predictive performance for rare events, including 24-hour rapid intensity change.
Unified framework for growth models with environmental risk and pollution-dependent disasters.
problem Analyzing how rare but catastrophic shocks interact with capital accumulation and pollution in stochastic growth models.
method General Poisson point process formulation leading to non-local HJB equations with closed-form solutions.
result Unified framework captures how environmental degradation amplifies macroeconomic vulnerability and strengthens incentives for abatement.
Paper addresses uncertainty in model generalization under regime shifts.
problem Uncertainty in model generalization under regime changes.
method Proposes a framework to quantify and separate regime mismatch and sensitivity.
result Obtains exact decomposition and minimax lower bound for regime-aware models.
Study analyzes Airbnb lead-time distributions for Nights Booked and Gross Booking Value, finding divergent shapes and tail behavior.
problem Analyzing lead-time distributions for Airbnb demand metrics.
method Compositional analysis of daily lead-time vectors, fitting Gamma, Weibull, and Lognormal distributions, using generalized Pareto for tail inference.
result Lead-time distributions for Nights Booked and Gross Booking Value diverge, with GBV concentrating more in mid-range horizons.
We develop importance sampling based efficient simulation techniques for three commonly encountered rare event probabilities associated with random walks having i.i.d. regularly varying increments; namely, 1) the large deviation probabilities, 2) the level crossing probabilities, and 3) the level crossing probabilities…
New method improves MMD estimation without convexity assumptions.
problem Lack of theoretical guarantees for MMD estimation algorithms.
method Preconditioned gradient descent (PGD) scheme for MMD optimization.
result PGD scheme converges globally under specific conditions.
It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from natural …
Generative model simulates rare events for better decision making.
problem Rare events impact decision making but are hard to sample.
method Normalizing Flow coupled with Importance Sampling.
result Accurate estimation of rare events improves decision outcomes.
AI boosts study of rare weather extremes with lower costs.
problem Difficulty in studying rare weather events due to limited data and models.
method Coupling AI forecasts with physics models using rare-event algorithms.
result Efficiently characterizes very rare events like once-per-millennium heatwaves.
Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare disease detection is an important task for identifying patients with rare conditions based on longitudinal medical claims. In this paper, we present a deep learning m…
Develops a method to estimate rare-event probabilities under distributional uncertainty.
problem Distributional uncertainty limits the effectiveness of rare-event simulation techniques.
method Wasserstein distributionally robust rare-event simulation (DRIS) framework.
result DRIS achieves vanishing relative error in estimating rare-event probabilities.
Reduces false positives in classifying rare online platforms.
problem Challenges in accurately identifying rare online platforms with ML.
method Calibrated probabilities and ensembles to reduce bias.
result Significantly reduces false positives in rare event detection.
This paper evaluates data enrichment techniques for rare event detection in manufacturing.
problem Rare events in manufacturing lead to unplanned downtime and high energy consumption.
method Time series data augmentation, sampling, and imputation techniques combined with supervised machine learning.
result Data enrichment enhances rare failure event detection and prediction by up to 48%.
Enhanced diffusion sampling improves rare event sampling in biomolecular simulations.
problem Efficiently sampling rare transition events in biomolecular systems.
method Quantitative steering protocols to generate biased ensembles and exact reweighting.
result Fast, accurate, and scalable estimation of equilibrium properties.
Enhanced diffusion sampling tackles rare event sampling in biomolecular simulations.
problem Efficiently sampling rare transition events in biomolecular simulations.
method Quantitative steering protocols to generate biased ensembles, followed by exact reweighting.
result Fast, accurate, and scalable estimation of equilibrium properties for folding free energies.
Proposes a method to ensure accurate estimation of rare events in AI systems.
problem Lack of efficiency guarantees in black-box systems for rare-event simulation.
method Integrates deep learning with importance sampling to create a statistically guaranteed estimator.
result Demonstrates effective estimation of rare-event probabilities in AI systems.
In many situations, classes of data points of primary interest also happen to be those that are least numerous. A well-known example is detection of fraudulent transactions among the collection of all financial transactions, the vast majority of which are legitimate. These types of problems fall under the label of `rar…
WEST uses EHRs and expert cases to improve rare disease phenotyping.
problem Limited labeled data for rare diseases.
method Weakly supervised transformer model trained on probabilistic silver-standard labels.
result WEST outperforms existing methods in phenotype classification and subphenotyping.
Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures. Developing an efficient method for rare disease detection is a crucial first step towards subsequent clinical research. In this paper, we present a semi-supervised learning framework for rare disease …
Gemini uses inexpensive measurements to correct biases in expensive property evaluations.
problem Accurate estimation of materials properties using expensive measurements is hindered in scientific discovery campaigns.
method Gemini is a data-driven model that corrects systematic biases between property evaluation methods using inexpensive measurements.
result Gemini reduces the number of expensive evaluations needed for Bayesian optimization in materials discovery.
Given a labeled dataset that contains a rare (or minority) class of of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? We introduce RaRecognize, which (i) estimates a general decision boundary betwee…
This research examines rare spurious correlations in neural networks and their impact on accuracy and privacy.
problem Rare spurious correlations in neural networks and their privacy risks.
method Introducing spurious patterns correlated with a fixed class to a few training examples, analyzing ℓ2 regularization and Gaussian noise. result Rare spurious correlations can significantly impact neural network accuracy and privacy, and specific mitigation methods can be effective.
In rare disease physician targeting, a major challenge is how to identify physicians who are treating diagnosed or underdiagnosed rare diseases patients. Rare diseases have extremely low incidence rate. For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involve…
Simulates patient pathways to detect delayed rare disease diagnoses.
problem Delayed rare disease diagnoses in France, causing health system and patient harm.
method Probabilistic modelling of patient pathways to create an alert system.
result Alert system detects and refers wandering patients to CRMRs.
Markov chain Monte Carlo (MCMC) algorithms for hidden Markov models often rely on the forward-backward sampler. This makes them computationally slow as the length of the time series increases, motivating the development of sub-sampling-based approaches. These approximate the full posterior by using small random subsequ…
Model predicts travel time under rare conditions using a vector-space model.
problem Predicting travel time under rare temporal conditions (e.g., holidays, school vacations) is challenging due to limited historical data and other temporal changes.
method Presented a vector-space model for encoding rare temporal conditions, allowing coherent representation learning across different conditions.
result Increased performance for travel time prediction over different baselines when using the vector-space encoding for representing the temporal setting.
Deep CNNs struggle with rare taxa; one-class classifiers help identify them.
problem Efficiently identifying rare benthic macroinvertebrates in insect monitoring.
method Combining deep CNNs with one-class classifiers to identify rare taxa.
result The proposed approach can improve rare species identification, supporting automation.
Most previous studies on multi-agent reinforcement learning focus on deriving decentralized and cooperative policies to maximize a common reward and rarely consider the transferability of trained policies to new tasks. This prevents such policies from being applied to more complex multi-agent tasks. To resolve these li…
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.
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.
New test uncovers causal links in rare event dynamics.
problem Causal discovery for rare event phenomena in dynamic systems.
method Nonparametric conditional independence test on time-invariant data.
result Validated across simulated and real-world datasets.
Proposes Ada-Sit method for mortality prediction of rare diseases.
problem Data insufficiency and clinical diversity of rare diseases make mortality prediction hard.
method Initialization-sharing multi-task learning method (Ada-Sit) for fast adaptation to similar tasks.
result Experimental results show the proposed model is effective for mortality prediction of diverse rare diseases.
PRESTO improves rare event prediction by shrinking towards proportional odds model.
problem Difficult to predict rare events due to class imbalance.
method PRESTO relaxes proportional odds model by estimating separate weights for transitions between categories, imposing L1 penalty to shrink towards proportional odds.
result PRESTO consistently estimates decision boundary weights under sparsity assumption, improving rare probability estimation.
This work is an analytical and numerical study of the composition of several fractals into one and of the relation between the composite dimension and the dimensions of the component fractals. In the case of composition of standard IFS with segments of equal size, the composite dimension can be expressed as a function …
Deep-PrAE improves rare-event simulation for black-box systems.
problem Evaluating rare safety-critical events in learning-based systems.
method Combines deep neural networks with IS to create statistically guaranteed estimations.
result Deep-PrAE provides accurate bounds on safety-critical event probabilities.
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.
NOFIS uses normalizing flows to estimate rare event probabilities more efficiently.
problem Accurate estimation of rare event probabilities using conventional methods is inefficient and resource-intensive.
method NOFIS learns a sequence of proposal distributions by minimizing KL divergence losses and estimates rare event probability using importance sampling.
result NOFIS outperforms baseline approaches in estimating rare event probabilities across 10 distinct test cases.
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.
New geometric approach for analyzing compositional data like gut microbiomes.
problem Analyzing non-negative compositional data with relative values only.
method Reinterpret compositional data as quotient topology of a sphere, using spherical harmonics and reflection group actions.
result Construction of Reproducing Kernel Hilbert Space (RKHS) for compositional data.
PHINN: A generative model for rare-event time series using persistent homology
problem Generating rare events in time series
method Flow-matching framework with dynamic Betti curves and persistence landscape loss
result Outperforms statistical and diffusion baselines in topological fidelity and tail coverage
New method uses active importance sampling for rare event optimization in high-dimensional problems.
problem Optimizing complex, high-dimensional functions with rare events.
method Combines rare events sampling with neural network optimization.
result Importance sampling reduces asymptotic variance, improving generalization.
New method learns diffusion bridges for rare events.
problem Simulating rare events in diffusion processes.
method Iterative online learning based on self-consistency.
result Strong performance in various empirical settings.