Search Results for author: Jesse Davis

Found 36 papers, 12 papers with code

Mapping probability word problems to executable representations

no code implementations EMNLP 2021 Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc De Raedt, Walter Daelemans

While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically.

Contextualised Word Representations Math +2

Deep Neural Network Benchmarks for Selective Classification

1 code implementation23 Jan 2024 Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri

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.

Benchmarking Classification

Biases in Expected Goals Models Confound Finishing Ability

no code implementations18 Jan 2024 Jesse Davis, Pieter Robberechts

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

no code implementations2 Oct 2023 Wei Sun, Mingxiao Li, Damien Sileo, Jesse Davis, Marie-Francine Moens

Medical Question Answering~(medical QA) systems play an essential role in assisting healthcare workers in finding answers to their questions.

Explanation Generation Question Answering

How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection

1 code implementation7 Jan 2023 Lorenzo Perini, Daniele Giannuzzi, Jesse Davis

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.

Active Learning Anomaly Detection

Nowcasting Stock Implied Volatility with Twitter

no code implementations31 Dec 2022 Thomas Dierckx, Jesse Davis, Wim Schoutens

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

no code implementations27 Jun 2022 Laurens Devos, Wannes Meert, Jesse Davis

We take an alternative approach and attempt to detect adversarial examples in a post-deployment setting.

Elastic Product Quantization for Time Series

1 code implementation4 Jan 2022 Pieter Robberechts, Wannes Meert, Jesse Davis

Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements.

Quantization Time Series +1

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

no code implementations7 Apr 2021 Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, Jesse Davis

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.

counterfactual Decision Making

Versatile Verification of Tree Ensembles

1 code implementation26 Oct 2020 Laurens Devos, Wannes Meert, Jesse Davis

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

no code implementations16 Sep 2020 Thomas Dierckx, Jesse Davis, Wim Schoutens

Using machine learning and alternative data for the prediction of financial markets has been a popular topic in recent years.

Asset Management BIG-bench Machine Learning

Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder

1 code implementation12 Mar 2020 Natasa Sarafijanovic-Djukic, Jesse Davis

First, using normal examples, a convolutional autoencoder (CAE) is trained to extract a low-dimensional representation of the images.

Anomaly Detection Quantization

Verifying Tree Ensembles by Reasoning about Potential Instances

1 code implementation31 Jan 2020 Laurens Devos, Wannes Meert, Jesse Davis

Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?

Attribute Fairness

Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach

no code implementations29 Oct 2019 Pieter Robberechts, Rud Derie, Pieter Van den Berghe, Joeri Gerlo, Dirk De Clercq, Veerle Segers, Jesse Davis

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.

BIG-bench Machine Learning Event Detection +1

LazyBum: Decision tree learning using lazy propositionalization

no code implementations11 Sep 2019 Jonas Schouterden, Jesse Davis, Hendrik Blockeel

Propositionalization is the process of summarizing relational data into a tabular (attribute-value) format.


Actions Speak Louder than Goals:Valuing Player Actions in Soccer

no code implementations Published in KDD 2018 2019 Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis

Assessing the impact of the individual actions performed by soccerplayers during games is a crucial aspect of the player recruitmentprocess.

Football Action Valuation

A Bayesian Approach to In-Game Win Probability in Soccer

no code implementations12 Jun 2019 Pieter Robberechts, Jan Van Haaren, Jesse Davis

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.

Decision Making

Learning from positive and unlabeled data: a survey

1 code implementation12 Nov 2018 Jessa Bekker, Jesse Davis

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data.

BIG-bench Machine Learning Knowledge Base Completion +1

Learning from Positive and Unlabeled Data under the Selected At Random Assumption

no code implementations27 Aug 2018 Jessa Bekker, Jesse Davis

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.

General Classification Medical Diagnosis

PAC-Reasoning in Relational Domains

no code implementations15 Mar 2018 Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert

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

3 code implementations18 Feb 2018 Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis

Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process.

Football Action Valuation

Relational Marginal Problems: Theory and Estimation

no code implementations18 Sep 2017 Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert

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

no code implementations19 May 2017 Ondrej Kuzelka, Jesse Davis, Steven Schockaert

Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.

Relational Reasoning

Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks

no code implementations9 Dec 2016 Jessa Bekker, Arjen Hommersom, Martijn Lappenschaar, Jesse Davis

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)

no code implementations18 Nov 2016 Ondrej Kuzelka, Jesse Davis, Steven Schockaert

In this paper, we advocate the use of stratified logical theories for representing probabilistic models.

Learning Possibilistic Logic Theories from Default Rules

no code implementations18 Apr 2016 Ondrej Kuzelka, Jesse Davis, Steven Schockaert

We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta".

Learning Theory

Tractable Learning for Complex Probability Queries

no code implementations NeurIPS 2015 Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van Den Broeck

We propose a tractable learner that guarantees efficient inference for a broader class of queries.

Encoding Markov Logic Networks in Possibilistic Logic

1 code implementation3 Jun 2015 Ondrej Kuzelka, Jesse Davis, Steven Schockaert

Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds.

Assessing binary classifiers using only positive and unlabeled data

2 code implementations26 Apr 2015 Marc Claesen, Jesse Davis, Frank De Smet, Bart De Moor

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

no code implementations4 Feb 2014 Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel

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

no code implementations NeurIPS 2013 Nima Taghipour, Jesse Davis, Hendrik Blockeel

Lifting attempts to speed up probabilistic inference by exploiting symmetries in the model.

Cannot find the paper you are looking for? You can Submit a new open access paper.