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Found 35 papers, 11 papers with code
Mapping probability word problems to executable representations
While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically.
Faster Repeated Evasion Attacks in Tree Ensembles
Tree ensembles are one of the most widely used model classes.
Deep Neural Network Benchmarks for Selective Classification
The selective classification framework aims to design a mechanism that balances the fraction of rejected predictions (i. e., the proportion of examples for which the model does not make a prediction) versus the improvement in predictive performance on the selected predictions.
Biases in Expected Goals Models Confound Finishing Ability
We found that sustained overperformance of cumulative xG requires both high shot volumes and exceptional finishing, including all shot types can obscure the finishing ability of proficient strikers, and that there is a persistent bias that makes the actual and expected goals closer for excellent finishers than it really is.
Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence
Medical Question Answering~(medical QA) systems play an essential role in assisting healthcare workers in finding answers to their questions.
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection
In this paper, we propose a mixed strategy that, given a budget of labels, decides in multiple rounds whether to use the budget to collect AL labels or LR labels.
Nowcasting Stock Implied Volatility with Twitter
In this study, we predict next-day movements of stock end-of-day implied volatility using random forests.
Adversarial Example Detection in Deployed Tree Ensembles
We take an alternative approach and attempt to detect adversarial examples in a post-deployment setting.
Elastic Product Quantization for Time Series
Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements.
Machine Learning with a Reject Option: A survey
Machine learning models always make a prediction, even when it is likely to be inaccurate.
Leaving Goals on the Pitch: Evaluating Decision Making in Soccer
Our key conclusion is that teams would score more goals if they shot more often from outside the penalty box in a small number of team-specific locations.
Versatile Verification of Tree Ensembles
This paper introduces a generic algorithm called Veritas that enables tackling multiple different verification tasks for tree ensemble models like random forests (RFs) and gradient boosting decision trees (GBDTs).
Using Machine Learning and Alternative Data to Predict Movements in Market Risk
Using machine learning and alternative data for the prediction of financial markets has been a popular topic in recent years.
Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder
First, using normal examples, a convolutional autoencoder (CAE) is trained to extract a low-dimensional representation of the images.
Verifying Tree Ensembles by Reasoning about Potential Instances
Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?
A general anomaly detection framework for fleet-based condition monitoring of machines
In most of these applications, it is safe to assume healthy conditions for the majority of machines.
Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach
Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running.
LazyBum: Decision tree learning using lazy propositionalization
Propositionalization is the process of summarizing relational data into a tabular (attribute-value) format.
Actions Speak Louder than Goals:Valuing Player Actions in Soccer
Assessing the impact of the individual actions performed by soccerplayers during games is a crucial aspect of the player recruitmentprocess.
A Bayesian Approach to In-Game Win Probability in Soccer
First, we demonstrate that in-game win probability models for other sports struggle to provide accurate estimates for soccer, especially towards the end of a game.
Learning from positive and unlabeled data: a survey
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data.
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
Most positive and unlabeled data is subject to selection biases.
Learning from Positive and Unlabeled Data under the Selected At Random Assumption
Experiments show that our method is not only very capable of learning under this assumption, but it also outperforms the state of the art for learning under the selected completely at random assumption.
PAC-Reasoning in Relational Domains
We consider the problem of predicting plausible missing facts in relational data, given a set of imperfect logical rules.
Actions Speak Louder Than Goals: Valuing Player Actions in Soccer
Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process.
Relational Marginal Problems: Theory and Estimation
In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals.
Induction of Interpretable Possibilistic Logic Theories from Relational Data
Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.
Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks
Our results confirm that prescriptions may lead to unintended negative consequences in further development of multimorbidity in cardiovascular diseases.
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)
In this paper, we advocate the use of stratified logical theories for representing probabilistic models.
Learning Possibilistic Logic Theories from Default Rules
We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta".
Tractable Learning for Complex Probability Queries
We propose a tractable learner that guarantees efficient inference for a broader class of queries.
Encoding Markov Logic Networks in Possibilistic Logic
Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds.
Assessing binary classifiers using only positive and unlabeled data
We provide theoretical bounds on the quality of our estimates, illustrate the importance of estimating the fraction of positives in the unlabeled set and demonstrate empirically that we are able to reliably estimate ROC and PR curves on real data.
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language
The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language.
First-Order Decomposition Trees
Lifting attempts to speed up probabilistic inference by exploiting symmetries in the model.
