GRAM addresses healthcare data insufficiency and interpretation challenges using graph-based attention.
problem Data insufficiency and lack of interpretability in healthcare predictive modeling.
method GRAM integrates EHR with medical ontologies, using attention mechanisms to represent medical concepts.
result GRAM outperforms RNN in accuracy and interpretability, using less data.
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.
Paper tackles label insufficiency and inaccuracy in semi-supervised learning.
problem Label insufficiency and inaccuracy in semi-supervised learning.
method Graph-based propagation for label insufficiency and label filtering for inaccuracy.
result SIIS improves performance in the presence of label noise and scarcity.
Deep transfer learning uses limited data to improve performance.
problem Insufficient training data in domains like bioinformatics and robotics.
method Uses transfer learning to leverage limited data with deep neural networks.
result Demonstrates the effectiveness of deep transfer learning in various domains.
Selective joint fine-tuning improves deep learning with limited data.
problem Insufficient labeled training data for deep learning tasks.
method Joint fine-tuning of shared convolutional layers between source and target tasks using selected training images.
result Improves classification accuracy by 2% - 10% on multiple visual classification tasks.
Develops a new criterion for subgroup fairness in algorithmic decision support.
problem Identifying fair recommendations in algorithms despite group-level differences.
method IJDI criterion and IJDI-Scan approach to detect and mitigate disparities.
result Identifies significant disparities in recommendations across subpopulations.
Paper proposes neural approach for Chinese named entity recognition.
problem Challenges in Chinese named entity recognition due to context-dependency and lack of word delimiters.
method Introduces a CNN-LSTM-CRF neural architecture and a unified framework for joint training with word segmentation.
result Improves Chinese named entity recognition performance, especially with limited training data.
Model predicts respiratory insufficiency in ALS patients with high accuracy.
problem Lack of insight into risk of error and optimal time for non-invasive ventilation.
method Combines Conformal Prediction and mixture experts to predict respiratory insufficiency and optimal time.
result Near 80% of predictions correctly identified, with confidence measures.
We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications typically there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression ove…
A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant chall…
New method identifies common cause in causal insufficiency, revealing complex phase transitions.
problem Identifying common cause in causal insufficiency with observed joint probability.
method Generalized maximum likelihood method, closely related to maximum entropy principle.
result Identifies consistent common cause that aligns with the common cause principle.
CCHM algorithm learns BN structure with latent variables, improving causal effect measurement.
problem Latent variables cause spurious relationships in BN structure learning.
method Hybrid approach combining constraint-based and score-based learning, incorporating do-calculus.
result CCHM outperforms state-of-the-art in reconstructing true BN structure.
New causal versions of MaxEnt and PIR avoid paradoxical probability updates.
problem Paradoxical probability updates in causal MaxEnt and PIR.
method Separate constraints into cause-specific and mechanism-specific restrictions.
result Causal MaxEnt avoids paradoxical updates and aligns with Information Geometric Causal Inference.
CFM fee income is insufficient to hedge market risk, study finds.
problem Inefficiency of CFM fee income in hedging market risk.
method Analysis through continuous-time financial mathematics and multi-agent simulations.
result Fee income is insufficient to compensate for market risk.
New findings suggest minimax optimality doesn't guarantee distribution learning for GANs.
problem Understanding when GANs can truly learn the underlying distribution.
method Using cryptographic assumptions and ReLU network generators, the paper shows that achieving minimax optimality is insufficient for distribution learning.
result Achieving minimax optimality is insufficient for distribution learning in the usual statistical sense.
New findings show the large margins theory is insufficient for explaining ensemble methods.
problem Explaining the performance of ensemble methods, especially boosting.
method Illustrated by counterexamples that show how to improve margin distribution without improving test set performance.
result The large margins theory is not sufficient to explain the performance of ensemble methods.
Enhances ordinal embedding with less data by focusing on margin distribution.
problem Insufficient labeled data for ordinal embedding.
method Proposes Distributional Margin based Ordinal Embedding (DMOE) to improve generalization with less data.
result Demonstrates improved generalization performance with less labeled data.
New fairness metrics improve collaborative filtering fairness.
problem Collaborative filtering's bias in historical data leads to unfair predictions for minority groups.
method Identified and proposed four new fairness metrics to address different forms of unfairness.
result Our new metrics better measure fairness than baseline metrics and effectively reduce unfairness.
Transfer learning is a recent field of machine learning research that aims to resolve the challenge of dealing with insufficient training data in the domain of interest. This is a particular issue with traditional deep neural networks where a large amount of training data is needed. Recently, StochasticNets was propose…
RMFGP combines multi-fidelity models for efficient uncertainty quantification.
problem Efficiently infer quantities of interest with limited high-fidelity data.
method Rotated multi-fidelity Gaussian process with dimension reduction and Bayesian active learning.
result RMFGP model improves accuracy and efficiency in high-dimensional problems.
A new decomposition explains over-parameterized models' counterintuitive behaviors.
problem Understanding predictive error in over-parameterized models.
method Introducing the Generalized Aliasing Decomposition (GAD) to explain predictive performance.
result The GAD decomposes predictive error into three parts: model insufficiency, data insufficiency, and generalized aliasing.
Paper analyzes convergence of SGD with shuffling in distributed learning.
problem Analyzing convergence of distributed SGD with shuffling.
method Formalizes data partition with global/local shuffling, proves convergence for convex and non-convex cases, and considers insufficient shuffling.
result SGD with global shuffling converges in both convex and non-convex cases, and local shuffling is slower.
Detects out-of-domain cases with limited training data.
problem Detecting out-of-domain cases with insufficient in-domain training data.
method Proposes an OOD-resistant Prototypical Network.
result Outperforms state-of-the-art methods in zero-shot OOD detection.
A stochastic model helps maintain insufficiently funded pension funds.
problem Maintaining pension funds that are underfunded and require external financing.
method A time-homogeneous diffusion process with a barrier is used to model the unrestricted reserves value, and a renewal-reward process models the financing effort.
result Expected values and cost evaluations of maintenance are derived, and the approach is applied to a generalized Brownian motion process.
WD-DTL uses Wasserstein distance to transfer deep features for fault diagnosis.
problem Transfer learning difficulty in diverse working conditions with insufficient labelled data.
method Adversarial training with Wasserstein distance to align feature distributions.
result WD-DTL improves fault diagnosis accuracy in diverse conditions.
Paper develops a method to detect abnormal road user trajectories using deep autoencoder.
problem Insufficient abnormal data for training machine learning models.
method Used a deep autoencoder network trained on augmented normal data.
result Performs better than classical outlier detection methods on various scenes.
Box Thirding identifies the best arm efficiently under limited samples.
problem Efficiently identifying the best arm with limited sampling.
method Iterative ternary comparison of arms, discarding the weakest and exploring the best.
result Achieves comparable performance to Successive Halving with less predefined parameters.
DIG visualizes complex time series data.
problem Insufficient visualization of high-dimensional dynamical processes.
method DIG (Dynamical Information Geometry) using diffusion framework.
result Reveals structure in multivariate time series data.
Paper improves Chinese word segmentation using dictionary knowledge.
problem Chinese word segmentation with limited labeled data.
method Two methods: pseudo labeled data generation and multi-task learning.
result Improves Chinese word segmentation performance, especially with scarce training data.
New framework studies policy learning problems under data scarcity.
problem Learning improving policies when data is insufficient.
method Developed a mathematical framework for policy learning problems.
result Reduced policy learning problems to simpler ones in sample complexity.
Paper shows comparing single performance scores is insufficient for non-deterministic systems, proposing to compare score distributions.
problem Insufficient comparison of non-deterministic sequence tagging systems.
method Compare score distributions based on multiple executions of LSTM-networks.
result LSTM-networks produce superior and more stable performance when compared using score distributions.
New approach generates better synthetic data for neural program synthesis.
problem Current approaches to neural program synthesis generalize poorly to real data.
method Adversarial approach to control synthetic data distributions.
result Proposed method outperforms current approaches.
New approach combines neural networks for creative problem-solving.
problem Neural networks struggle with new, unseen data.
method Conceptual expansion: reusing existing models without backpropagation.
result Outperforms standard transfer learning on few-shot tasks.
The study examines how to assess skill when outcomes are noisy and insufficient.
problem Determining skill when outcomes are unreliable and insufficiently numerous.
method Characterizes decision domains with noise and effective sample size, using population-level validation methods.
result Domains with noisy outcomes are unreliable for individual skill assessment.
Graphical lasso may fail to fit models when data points are insufficient.
problem When does graphical lasso fail to select and fit a graphical model?
method Computational experiments with graphical lasso.
result Graphical lasso may fail when the number of data points is less than the maximum likelihood threshold.
New approach uses nonlinear expectations to estimate extreme risks.
problem Estimating tail quantities in heavy-tailed data.
method Data-robust expectation and regularization for Pareto distributions.
result Qualitative requirement for reliable estimation of extreme risks.
The Morse-Bott inequalities relate the topology of a closed manifold to the topology of the critical point set of a Morse-Bott function defined on it. The Morse-Bott inequalities are sometimes stated under incorrect orientation assumptions. We show that these assumptions are insufficient with an explicit counterexample…
Neural eliminators reduce unreliable classification by eliminating improbable classes.
problem Unreliable classification due to noise, insufficient data, overlapping distributions, and unclear class definitions.
method Construct eliminators using classifiers with modified error functions, assigning cases to multiple classes instead of one.
result Elimination of improbable classes improves classification accuracy in real-life medical applications.
The Epps effect, the decrease of correlations between stock returns for short time windows, was traced back to the trading asynchronicity and to the occasional lead-lag relation between the prices. We study pairs of stocks where the latter is negligible and confirm the importance of asynchronicity but point out that al…
The paper critiques existing uncertainty concepts and proposes a new decision-theoretic approach.
problem Incoherence in existing discussions of aleatoric and epistemic uncertainty.
method Decision-theoretic perspective that relates uncertainty, predictive performance, and statistical dispersion.
result Popular information-theoretic quantities can be poor estimators but still useful for guiding data acquisition.
FRI identifies relevant features in high-dimensional data for biomedical experiments.
problem Spurious feature selection in high-dimensional data.
method Feature relevance method for identifying all-relevant variables in linear classification and regression.
result FRI can identify causal features in biomedical experiments.
Identifying causal direction in location-scale noise models with hidden variables
problem Causal discovery in location-scale noise models with hidden variables
method ADMGs satisfying a bow-free condition
result First identifiability result for causally insufficient models beyond noise additivity
The paper assesses fairness in AI for financial services, using statistical methods.
problem Unintentional bias and insufficient model validation in AI applications.
method Statistical methods for imbalanced data treatment and bias mitigation.
result Fairness evaluation metrics applied to a credit card default payment example.
We consider the Einstein-Maxwell-fluid constraint equations, and make use of the conformal method to construct and parametrize constant-mean-curvature hyperboloidal initial data sets that satisfy the shear-free condition. This condition is known to be necessary in order that a spacetime development admit a regular conf…
ADGAN improves risk tolerance prediction by aligning cross-domain data.
problem Lack of professional knowledge and domain-specific models in risk tolerance studies.
method Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality.
result ADGAN better handles class imbalance and unqualified data than state-of-the-art methods.
GIT uses gradient estimators to target interventions for causal discovery.
problem Challenges in inferring causal structure from observational data.
method GIT uses gradient estimators to target interventions for causal discovery.
result GIT performs on par with competitive baselines, surpassing them in low-data regimes.
The paper proposes a variational autoencoder for discrete data analysis.
problem Sparse, high-dimensional, and overdispersed discrete data analysis.
method Variational autoencoder based on negative-binomial distribution.
result The proposed models achieve significantly better performance on text analysis, collaborative filtering, and multi-label learning compared to state-of-the-art baselines.
Big data applications, such as medical imaging and genetics, typically generate datasets that consist of few observations n on many more variables p, a scenario that we denote as p>>n. Traditional data processing methods are often insufficient for extracting information out of big data. This calls for the development o…