527 citations · 967 across the 30 of their papers we have counts for
33 papers
A CaLLM Judge is a Better Judge: Mitigating Bias in LLM-as-a-judge
Cesare Barbera, Luca Stradiotti, Lorenzo Cascioli +6
Large language models (LLMs) are increasingly used as judges to predict which of two candidate answers a human evaluator would prefer, providing a scalable alternative when human e…
Multicalibration for Unbiased Model-Based Prevalence Estimation
Fridolin Linder, Thomas Leeper, Daniel Haimovich +3
Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is fundamental to science, pub…
Agentic Business Process Management: A Research Manifesto
Diego Calvanese, Angelo Casciani, Giuseppe De Giacomo +15
This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for gove…
On the Convergence of Multicalibration Gradient Boosting
Daniel Haimovich, Fridolin Linder, Lorenzo Perini +2
Multicalibration gradient boosting has recently emerged as a scalable method that empirically produces approximately multicalibrated predictors and has been deployed at web scale.…
Multicalibration Yields Better Matchings
Riccardo Colini Baldeschi, Simone Di Gregorio, Simone Fioravanti +9
Consider the problem of finding the best matching in a weighted graph where we only have access to predictions of the actual stochastic weights, based on an underlying context. If…
MCGrad: Multicalibration at Web Scale
Niek Tax, Lorenzo Perini, Fridolin Linder +5
We propose MCGrad, a novel and scalable multicalibration algorithm. Multicalibration - calibration in subgroups of the data - is an important property for the performance of machin…
Measuring multi-calibration
Ido Guy, Daniel Haimovich, Fridolin Linder +4
A suitable scalar metric can help measure multi-calibration, defined as follows. When the expected values of observed responses are equal to corresponding predicted probabilities,…
Online Learning with Sublinear Best-Action Queries
Matteo Russo, Andrea Celli, Riccardo Colini Baldeschi +5
In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred. Following the recent line of research on alg…
On the Convergence of Loss and Uncertainty-based Active Learning Algorithms
Daniel Haimovich, Dima Karamshuk, Fridolin Linder +2
We investigate the convergence rates and data sample sizes required for training a machine learning model using a stochastic gradient descent (SGD) algorithm, where data points are…
Active learning with biased non-response to label requests
Thomas Robinson, Niek Tax, Richard Mudd +1
Active learning can improve the efficiency of training prediction models by identifying the most informative new labels to acquire. However, non-response to label requests can impa…
TCE: A Test-Based Approach to Measuring Calibration Error
Takuo Matsubara, Niek Tax, Richard Mudd +1
This paper proposes a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE). TCE incorporates a novel loss func…
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
David S. Watson, Joshua O'Hara, Niek Tax +2
Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast,…
Machine Learning for Fraud Detection in E-Commerce: A Research Agenda
Niek Tax, Kees Jan de Vries, Mathijs de Jong +5
Fraud detection and prevention play an important part in ensuring the sustained operation of any e-commerce business. Machine learning (ML) often plays an important role in these a…
Mining Insights from Weakly-Structured Event Data
Niek Tax
This thesis focuses on process mining on event data where such a normative specification is absent and, as a result, the event data is less structured. The thesis puts special emph…
Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)
Anja F. Syring, Niek Tax, Wil M. P. van der Aalst
Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformanc…
Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring
Stephan A. Fahrenkrog-Petersen, Niek Tax, Irene Teinemaa +4
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outc…
An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction
Niek Tax, Irene Teinemaa, Sebastiaan J. van Zelst
Data of sequential nature arise in many application domains in forms of, e.g. textual data, DNA sequences, and software execution traces. Different research disciplines have develo…
Automatic Playlist Continuation through a Composition of Collaborative Filters
Irene Teinemaa, Niek Tax, Carlos Bentes
The RecSys Challenge 2018 focused on automatic playlist continuation, i.e., the task was to recommend additional music tracks for playlists based on the playlist's title and/or a s…
Alarm-Based Prescriptive Process Monitoring
Irene Teinemaa, Niek Tax, Massimiliano de Leoni +2
Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Exi…
Mining Non-Redundant Local Process Models From Sequence Databases
Niek Tax, Marlon Dumas
Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database. A practical limitation of these techniques is that they overl…
Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities
Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst
Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs…
Mining Process Model Descriptions of Daily Life through Event Abstraction
Niek Tax, Natalia Sidorova, Reinder Haakma +1
Process mining techniques focus on extracting insight in processes from event logs. Process mining has the potential to provide valuable insights in (un)healthy habits and to contr…
The Imprecisions of Precision Measures in Process Mining
Niek Tax, Xixi Lu, Natalia Sidorova +2
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed…
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
Felix Mannhardt, Niek Tax
Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When…