Predicts student success and major using first courses.
problem Predicting student success and major based on early course data.
method Used random forests to build classifiers for predicting degree completion and major.
result Accurate classifiers for predicting degree completion and major.
Categorical Co-Frequency Analysis clusters diagnoses to predict hospital readmissions.
problem Predicting patients' risk of 30-day hospital readmission.
method Categorical Co-Frequency Analysis (CoFA) measures diagnosis similarity using random forests.
result Identified three groups of diagnoses with varying readmission risk.
With pressure to increase graduation rates and reduce time to degree in higher education, it is important to identify at-risk students early. Automated early warning systems are therefore highly desirable. In this paper, we use unsupervised clustering techniques to predict the graduation status of declared majors in fi…
Novel analysis improves weighted majority vote in multiclass classification.
problem Improving the performance of weighted majority vote in multiclass classification.
method Analyzes expected risk of weighted majority vote, considering prediction correlations and provides a bound for efficient minimization.
result Minimization of the new bound typically does not degrade the test error of the ensemble.
New margin bound improves generalization for voting classifiers.
problem Improving generalization bounds for voting classifiers.
method Established a new margin-based generalization bound.
result Derives an optimal weak-to-strong learner with matching theoretical lower bound.
With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem i…
Geometric framework determines optimal number of ensemble classifiers.
problem Determining the optimal number of component classifiers for ensemble prediction accuracy.
method Geometric framework for a priori determining ensemble size, applicable to batch and online environments.
result The framework proves the existence of an ideal number of components for WMV, equal to the number of class labels.
New method identifies predictive biomarkers for subgroup analysis.
problem Identifying predictive biomarkers from large covariates.
method Generalized penalized regression with overlapped group penalties.
result Asymptotically consistent method for sparse, interpretable models.
Machine learning ensemble improves accuracy by considering minority answers as more likely true.
problem Ensemble methods often rely on majority voting, which can fail when the majority is wrong.
method Proposes Bayesian Truth Serum for classification problems, detecting surprising majority answers.
result Better classification performance achieved by considering minority answers as more likely true.
Paper analyzes how weather data improves solar power prediction.
problem Predicting the unpredictability of solar power generation.
method Examined the impact of weather data on photovoltaic power prediction.
result Weather data significantly improves photovoltaic power prediction accuracy.
Backpropagation rules emerge from weighted automata combinations.
problem Understanding the effectiveness of deep learning.
method Combining finite automata using a weighted majority algorithm and simplifying the result to a neural network.
result Backpropagation is equivalent to a neural network's learning process.
We document a mechanism operating in complex adaptive systems leading to dynamical pockets of predictability (``prediction days''), in which agents collectively take predetermined courses of action, transiently decoupled from past history. We demonstrate and test it out-of-sample on synthetic minority and majority game…
This paper predicts significant stock price changes using neural networks.
problem Predicting significant stock price changes.
method Three neural network models (MLP, CNN, LSTM) and two benchmark models (Random Forest, Relative Strength Index) were tested on 10-year daily stock price data of four major US companies.
result Neural network models significantly outperform traditional methods in predicting significant stock price changes.
A framework predicts employment status for students considering unconscious biases.
problem Unconscious biases hinder college students' job hunting.
method Developed a framework MAYA using GAN, LSTM, and bias regularization.
result Framework effectively predicts employment status with bias consideration.
In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization…
Deep learning predicts baseball home runs with better accuracy.
problem Predicting player performance in baseball.
method Long Short-Term Memory (LSTM) neural network.
result LSTM outperformed other models in predicting home runs.
We present a systematic algorithm testing for the existence of collective self-organization in the behavior of agents in social systems, with a concrete empirical implementation on the Dow Jones Industrial Average index (DJIA) over the 20th century and on Hong Kong Hang Seng composite index (HSI) since 1969. The algori…
A novel algorithm CULP uses link prediction for graph classification.
problem Graph classification using unlabeled data.
method CULP uses a Label Embedded Graph (LEG) and a link predictor to classify unlabeled nodes.
result CULP and CULM show high accuracy and competitiveness with existing graph classifiers.
Framework learns to transform majority to minority samples for balanced classification.
problem Imbalanced classification leading to biased predictions.
method Minimizes MMD and uses triplet loss for global alignment and boundary awareness.
result Consistent improvements over classical and generative baselines in AUROC, G-mean, F1-score, and MCC.
Cryptocurrencies show stable prices as a medium of exchange.
problem Price stability of cryptocurrencies as a medium of exchange.
method Filtered daily returns of major cryptocurrencies compared to major financial assets using Pearson correlations, dynamic time-warping method, and Black-Scholes model.
result Cryptocurrencies exhibit stable daily returns relative to major financial assets over the years 2016-2020.
Genomic models learn DNA sequences to predict functions.
problem Understanding complex genetic interactions.
method Training LLMs on DNA sequences to predict functions.
result gLMs can predict functions of DNA elements.
Community moderation drifts towards majority, study finds.
problem How to ensure crowd-sourced moderation systems trust and reward accurate evaluations.
method Consensus-based auditing with a two-stage algorithm that weights contributors by the stability of their past residuals.
result Minority contributors' evaluations drift towards the majority, and their participation share falls on controversial topics.
POSL predicts dynamic convection volumes in hemodiafiltration patients.
problem Continuous, personalised predictions in personalised medicine.
method Adapted POSL to dynamically predict convection volumes using combinations of parametric regressions and machine learning.
result POSL outperformed candidate learners in predicting convection volumes with lower errors and better calibration.
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural netwo…
Web browser autofill predicts form field labels for convenience.
problem Predicting form field labels for autofill in web browsers.
method Machine learning solution implemented as a web service using Azure ML Studio.
result Improved form field label prediction for autofill.
Paper predicts house prices in major Chinese cities using LSTM networks.
problem Predicting house prices in major Chinese cities.
method Used LSTM networks compared to ARIMA for prediction accuracy.
result LSTM networks showed superior accuracy in predicting house prices.
A new framework for predictive clustering and optimization.
problem Finding clusters of data that yield low error on a supervised target.
method Generalized optimization framework using MILP and MM for scalability.
result Models can uncover different interpretable discrete cluster structures.
Predicts stock market crashes using rational bubble model.
problem Financial market crashes prediction.
method White box model based on rational bubble theory.
result Successfully predicts major crashes in Dow Jones and Bitcoin markets.
We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression. This convex relaxation of reduced-rank multinomial regression has the advantage of leveraging underlying structure among the response categories to make better predictions. We apply our method, nuclear pen…
Graph conformal prediction predicts future power outages with high confidence.
problem Accurately predicting future power outages to enable rapid recovery.
method Developed a graph conformal prediction method for quarter-hourly outage data.
result Graph conformal prediction method delivers accurate prediction regions for future outage numbers.
TLRS improves predictive power of mined formulaic alpha factors.
problem Sparse rewards in RL for mining formulaic alpha factors.
method Trajectory-level Reward Shaping (TLRS) with reward centering.
result TLRS boosts predictive power by 9.29% over existing methods.
Study uses machine learning to predict rain in Australia.
problem Challenging task of predicting rainfall with uncertain outcomes.
method Machine learning techniques, including modeling inputs, methods, and pre-processing.
result Comparison of various machine learning techniques' reliability in predicting rainfall.
Graph Attention Networks predict power outage durations from natural disasters.
problem Accurately predicting power outage durations from geospatial and weather data.
method Graph Attention Networks (GAT) for semi-supervised learning.
result GAT model outperforms existing methods by 2% - 15% in accuracy.
DeepBalance uses DBN ensembles to improve minority class prediction in imbalanced datasets.
problem Class imbalance in financial fraud detection and network intrusion analysis.
method Random DBN ensembles trained with balanced bootstraps and random feature selection.
result DeepBalance outperforms baseline resampling methods in AUC and sensitivity metrics.
Ensemble CNNs improve mode classification in smartphone travel surveys.
problem Classifying transportation modes from smartphone travel survey data.
method Developed an ensemble of CNN models with different architectures and hyper-parameters, combined using average voting, majority voting, optimal weights, and a Random Forest meta-learner.
result The ensemble method with Random Forest as meta-learner achieved 91.8% accuracy, surpassing other methods.
Survey examines machine learning for credit rating predictions.
problem Manual loan approvals are slow and error-prone.
method Examines sentiment analysis techniques in credit rating.
result Machine learning improves credit rating predictions.
The occurrence of aftershocks following a major financial crash manifests the critical dynamical response of financial markets. Aftershocks put additional stress on markets, with conceivable dramatic consequences. Such a phenomenon has been shown to be common to most financial assets, both at high and low frequency. It…
AMP0 predicts antimicrobial peptides targeting specific microbes.
problem Low-throughput screening of antimicrobial peptides.
method Zero-shot and few-shot machine learning.
result AMP0 can predict antimicrobial activity against specific microbes.
The paper develops AI methods to predict weather-sensitive product sales.
problem Lack of accurate demand prediction for weather-sensitive products.
method Artificial Neural Networks applied to historical Walmart sales data.
result Improved demand prediction for weather-sensitive products.
The paper tackles partial inference in structured prediction using a convex optimization approach.
problem Maximizing a score function with unary and pairwise potentials in graph label spaces.
method Generative model approach with two-stage convex optimization for label recovery.
result Conditions for recovering a majority of labels with provable guarantees.
The paper assesses fairness in risk score models, focusing on epistemic value.
problem Fairness of risk score models in communicating uncertainty.
method Identified key fairness desiderata, developed metrics for quantitative assessment, and applied methodology in two case studies.
result Introduced a novel calibration error metric for meaningful comparisons between groups of different sizes.
This work introduces COLA, a strategy to aggregate conformal prediction sets efficiently.
problem Efficiently combining multiple conformity scores to reduce prediction set size.
method Introduces COnfidence-Level Allocation (COLA) to optimally allocate confidence levels across sets.
result COLA achieves smaller prediction sets than state-of-the-art methods while maintaining valid coverage.
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
problem Challenges in predicting new bike stations due to lack of historical data.
method AtCoR algorithm that predicts both existing and new bike stations using station-centered heatmaps and historical correlations.
result AtCoR outperforms existing models in predicting bike station usage.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.
New algorithms bound graph structure sampling and learning high-dimensional graphical models.
problem Learning high-dimensional graphical models and efficient graph structure sampling.
method Online learning framework with exponentially weighted average (EWA) or randomized weighted majority (RWM) forecasters using log loss function.
result New sample complexity bounds and efficient algorithms for learning Bayes nets, including trees and chordal skeletons.
BusTr predicts bus travel times from real-time traffic forecasts.
problem Improving accuracy of bus travel time predictions.
method Neural sequence model trained on real-time traffic forecasts.
result BusTr outperforms DeepTTE by 30% in Mean Absolute Percentage Error (MAPE).
Paper proposes a novel method for unsupervised ensemble learning using Ising model.
problem Challenges in unsupervised ensemble learning, especially in crowdsourcing applications.
method unElisa method combining pruning and predicting steps using Ising model.
result Consistent estimate of Bayes classifier achieved through pruning and majority voting.
The study predicts surgical complications in Crohn's disease patients using machine learning.
problem Predicting surgical complications in Crohn's disease patients.
method Developed a novel algorithm using ensemble machine learning on 29 baseline covariates.
result Proposed pseudo-observation based estimators for evaluating predictive performance.