Arena model predicts competition outcomes without rating many individuals.
problem Predicting competition outcomes without extensive individual ratings.
method Parametric arena model exploiting competition structure and uncertainty quantification.
result Invariant Bayes estimator and consistent uncertainty estimations.
Model predicts competition between similar products in sales.
problem Predicting cannibalization between similar products in sales.
method Developed a neural network model that computes a 'competitiveness' function based on product features.
result The model outperforms traditional methods in predicting market share.
Study shows practical optimization can outperform theoretical best models in competitive scenarios.
problem Does the best theoretical model always win in competitive machine learning?
method Game theoretic approach to analyze reward functions of models.
result In some cases, practical optimization can yield better rewards than theoretical best models.
Study predicts individual treatment effects in ride-sharing competitions.
problem Understanding how team competitions affect individual drivers' outcomes.
method Analyzed data from 500 competitions, built machine learning models.
result Reduced out-sample prediction error by over 24%.
Bayesian rating system for large competitions improves prediction and efficiency.
problem Rating systems for large, competitive events like online programming contests.
method Developed a Bayesian rating system for many participants, proving robustness and runtime.
result The system outperforms existing systems in accuracy and computation speed.
Machine learning competition predicts spacecraft collision risks.
problem Predicting future collision risks between orbiting satellites.
method Machine learning models trained on satellite collision data.
result Models accurately predicted collision risks with high precision.
Private cancer prediction model trained on federated genomic data.
problem Train a private cancer prediction model on federated genomic data.
method Differentially private federated learning (FL) for genomic cancer prediction.
result Ranked 3rd in a competition for private cancer prediction.
Study shows competition feedback can make ML predictors biased towards specific user groups.
problem How competition affects machine learning predictors and user prediction quality.
method Flexible model of competing ML predictors, empirical and mathematical analysis.
result Competition causes predictors to specialize for specific sub-populations at the cost of general performance.
Develops a learning model predictive controller for competitive racing.
problem Lack of exploration in state space and complexity in obstacle avoidance.
method Explores state space through multiple initializations and develops a new method for convex terminal set selection.
result Yields a richer terminal safe set and maintains convexity.
NeurIPS 2020 competition seeks to predict deep learning generalization.
problem Understanding and predicting generalization in deep learning models.
method Propose complexity measures to accurately predict generalization performance.
result A robust complexity measure could improve deep learning reliability.
New algorithm SFHC achieves near-optimal costs with predictions for non-convex optimization.
problem Online optimization with non-convex hitting costs and movement costs.
method Synchronized Fixed Horizon Control (SFHC) algorithm with conditions on hitting and movement costs.
result Synchronized Fixed Horizon Control (SFHC) achieves a 1+O(1/w) competitive ratio for near-optimal costs. Improved model accuracy can reduce overall user accuracy in competitive markets.
problem The impact of model competition on overall user accuracy.
method Defined a model of competition for classification tasks and used data representations to study the effect of scale.
result Improving data representation quality can decrease overall predictive accuracy across users (social welfare) in a competitive market.
Model predicts growth competition on curved surfaces.
problem Growth dynamics of two subsets on Riemannian manifolds.
method Modeling growth rates on spherically symmetric Riemannian manifolds.
result Conditions for bounded or unbounded growth on different manifolds.
EA-LSTM improves LSTM for time series prediction by evolving attention.
problem LSTMs struggle with assigning varying attention to sub-windows in time series data.
method Evolutionary attention-based LSTM with competitive random search.
result EA-LSTM achieves competitive performance in multivariate time series prediction.
User smeznar achieved 8th place in PGDL by predicting generalization of deep learning models.
problem Understanding and predicting generalization in deep learning models.
method Creating simple metrics and finding their best combination for automatic testing on a dataset.
result Combination of various properties of neural network architectures can be used for generalization prediction.
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
problem Predicting high-resolution peak demand from limited lower-resolution data.
method Combines generalized additive models (GAM) and deep neural networks (DNN) for half-hourly load forecasting.
result Proposed method reduces out-of-sample RMSE by 57.4% compared to benchmark.
Optimal ski rental strategies with machine learning predictions.
problem Minimizing ski rental costs with uncertain future days.
method Derive optimal randomized algorithms using machine learning predictions.
result Class of optimal algorithms with minimized competitive ratio.
Two approaches use TDA and graph theory for tennis match prediction.
problem Predicting tennis match outcomes using network features.
method Lower-star filtration on player competitive networks, Random Forest model, modified Katz similarity index.
result TDA features alone can achieve above-chance prediction in tennis match outcomes.
The M5 competition tackles overdispersed retail sales forecasting with GAMLSS.
problem Overdispersed and zero-inflated retail sales data.
method Distributional forecasting using GAMLSS framework.
result GAMLSS provides better probabilistic forecasting for count data.
GRUwE improves irregular time series prediction with simpler, efficient RNN-based approach.
problem Irregularly sampled multivariate time series prediction challenges.
method Gated Recurrent Unit with Exponential basis functions (GRUwE).
result GRUwE achieves competitive or superior performance compared to recent state-of-the-art methods.
MAT uses attention mechanism for molecule property prediction.
problem Designing a competitive neural network for molecule property prediction.
method Augmented attention mechanism using inter-atomic distances and molecular graph structure.
result MAT achieves state-of-the-art performance on diverse molecular prediction tasks.
Bayesian deep networks with local competition reduce model complexity without sacrificing accuracy.
problem Inference of deep networks with minimal model complexity.
method Revisits deep networks with local competition, using Bayesian nonparametrics for inference of connections and precision.
result Yields networks with less computational footprint and no accuracy loss.
New approach for algorithms that learn predictors to improve performance.
problem Improving algorithm performance by learning better predictions.
method Identify functional dependence and apply online learning techniques.
result Improved results in various online settings with simpler analysis.
Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal Élő update still remains, after more than 50 years, a valuable baseline which is difficult to…
missForestPredict fills missing data for prediction models quickly and accurately.
problem Missing data in input variables for prediction models.
method Iterative imputation using random forests until convergence.
result missForestPredict outperforms other imputation methods in prediction settings.
Random Forests provide interpretable prediction intervals with theoretical guarantees.
problem Lack of uncertainty estimates in machine learning point predictions.
method Out-of-Bag procedure for generating parametric and non-parametric prediction intervals.
result Proposed prediction intervals deliver correct coverage rates and narrow lengths.
Method explains machine learning diabetes risk predictions.
problem Lack of interpretability in machine learning models.
method Automatic explanation method for machine learning models.
result 87.4% of patients' predictions explained without accuracy loss.
Model shows disclosure reduces trading costs in oligopolistic markets.
problem Reducing trading costs in oligopolistic markets with imperfect competition.
method Developed a multi-period Kyle-type model with mandatory disclosure and imperfect competition, proving existence and uniqueness of a linear equilibrium.
result Disclosure lowers trading costs by reducing price impact, and its marginal benefit is larger when competition is weak.
New algorithms predict causal links better than traditional methods in time series data.
problem Learning causal structure from time series data with challenges in real-world Earth sciences.
method Combination of established ideas for linear methods to identify causal links in non-linear systems, with a focus on large regression coefficients.
result Large regression coefficients can predict causal links better than small p-values in practice.
The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data. If labels indices are considered as time indices, the problems can often be seen as equivalent. In this pape…
In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions. This framework has been learned by us, validated and improved over a period of more than two years by participating in online machine learning competitions. It aims at minimizing human interf…
We develop a mean-field theory for multi-component ICA in high dimensions.
problem Understanding multi-component ICA in high-dimensional settings.
method Asymptotically exact mean-field theory for multi-component online ICA.
result Explicit learnability boundaries and competition conditions linking step size, data moments, and initialization.
State-space models win a forecasting competition for unstable data.
problem Forecasting electricity demand during the post-covid period.
method Adapting state-space models to balance time-series adaptability and machine learning complexity.
result State-space models provide a better compromise between adaptability and accuracy for non-stationary data.
Winning solution for predicting player churn in a video game.
problem Predicting when players will stop playing a game.
method Long Short-Term Memory (LSTM) approach and conditional inference survival ensemble model.
result Models accurately predicted player churn and were robust to changing business models.
Model predicts next destination for users based on past trips and features.
problem Predicting the next destination in multi-destination trips.
method Used Cleora for city graph embedding and EMDE for prediction.
result Achieved 2nd place in Booking Data Challenge.
The study analyzes games and social hierarchies, incorporating luck and depth of competition.
problem Analyzing patterns of wins and losses in games and social hierarchies.
method Generalized probabilistic models incorporating luck and depth of competition.
result Social competition tends to be deeper with many distinct levels, but there is often a chance of upset victories.
EvoMSA unifies multilingual sentiment analysis systems.
problem Multilingual sentiment analysis in various languages.
method Genetic Programming-based classifier combining multiple text classifiers.
result EvoMSA performs competitively in multiple sentiment analysis competitions.
New algorithms learn model complexity and stochasticity robustly in online prediction.
problem Learning model complexity and stochasticity in online prediction.
method Probabilistic structural risk minimization integrated into adaptive algorithms.
result Competitive regret bounds for model and stochasticity adaptivity.
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for r…
Generalized algorithm for translation and scale-invariant prediction.
problem Sequential prediction with expert advice, focusing on translation and scale invariance.
method Designing a generalized online algorithm using the universal prediction perspective to compete against a generic class of expert selection strategies.
result No preliminary knowledge of loss sequences is required; performance bounds are stable under arbitrary scalings and translations.
Reanalysis of bioactivity prediction models suggests SVM performance is competitive with deep learning.
problem Benchmarking and validation of machine learning models in drug discovery.
method Reanalysis of a large-scale comparison of machine learning models for bioactivity prediction, using numerical experiments to question ROC curve relevance and suggest precision-recall curve.
result Support vector machines show competitive performance with deep learning methods in bioactivity prediction.
Advocacy learning uses competitive networks to improve classification accuracy.
problem Improving classification accuracy in attention-based classification problems.
method Two networks: Advocates and a Judge. Advocates produce class-conditional attention maps to convince the Judge of their class.
result Advocacy learning can lead to small improvements in classification accuracy over a baseline.
The paper proposes using experts' insights in machine learning tasks.
problem Lack of reliance on experts' opinions in machine learning tasks.
method A three-step framework for integrating experts' insights into machine learning models.
result Proposed models achieve more stable and lower log loss results compared to top solutions.
Gradient-free method improves predictive accuracy for probabilistic models.
problem Balancing computational efficiency and robust predictive performance in deep learning.
method CAVI-CMN, a gradient-free variational method for conditional mixture networks.
result CAVI-CMN achieves competitive and often superior predictive accuracy compared to MLE with backpropagation.
Proposes SQUAD for better predictive uncertainty in deep latent models.
problem Intractable inference in deep latent variable models lead to overconfident predictions.
method Introduces Stochastic Quantized Activation Distributions (SQUAD) for flexible yet tractable latent variable distributions.
result The model provides competitive quality predictive uncertainty and learns non-linearities.
Predictive topic models retain only relevant terms for better prediction and topic coherence.
problem Misspecification of topic models leads to poor prediction and topic coherence.
method Uses supervisory signal to select vocabulary terms improving prediction performance.
result Prediction-focused topic models learn more coherent topics while maintaining competitive predictions.
Model predicts US COVID-19 deaths with quantile estimates.
problem Predicting US COVID-19 deaths at county level.
method Hybrid machine learning and epidemiological approach, minimizing pinball loss.
result Quantile estimates accurately forecast deaths for different forecast periods.
Research predicts XRP price anomalies using graph topologies.
problem Forecasting extreme price movements in XRP cryptoasset.
method Analyzed topological features of XRP transaction graphs.
result Topological features indicate extreme price surges.