333 citations · 596 across the 14 of their papers we have counts for
16 papers
Designing Decision Support Systems Using Counterfactual Prediction Sets
Eleni Straitouri, Manuel Gomez Rodriguez
Decision support systems for classification tasks are predominantly designed to predict the value of the ground truth labels. However, since their predictions are not perfect, thes…
Human-Aligned Calibration for AI-Assisted Decision Making
Nina L. Corvelo Benz, Manuel Gomez Rodriguez
Whenever a binary classifier is used to provide decision support, it typically provides both a label prediction and a confidence value. Then, the decision maker is supposed to use…
On the Within-Group Fairness of Screening Classifiers
Nastaran Okati, Stratis Tsirtsis, Manuel Gomez Rodriguez
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that, if a classifier…
Counterfactual Inference of Second Opinions
Nina L. Corvelo Benz, Manuel Gomez Rodriguez
Automated decision support systems that are able to infer second opinions from experts can potentially facilitate a more efficient allocation of resources; they can help decide whe…
Improving Screening Processes via Calibrated Subset Selection
Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
Many selection processes such as finding patients qualifying for a medical trial or retrieval pipelines in search engines consist of multiple stages, where an initial screening sta…
Improving Expert Predictions with Conformal Prediction
Eleni Straitouri, Lequn Wang, Nastaran Okati +1
Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately. However, existing systems typically require…
Counterfactual Temporal Point Processes
Kimia Noorbakhsh, Manuel Gomez Rodriguez
Machine learning models based on temporal point processes are the state of the art in a wide variety of applications involving discrete events in continuous time. However, these mo…
Consequential Ranking Algorithms and Long-term Welfare
Behzad Tabibian, Vicenç Gómez, Abir De +2
Ranking models are typically designed to provide rankings that optimize some measure of immediate utility to the users. As a result, they have been unable to anticipate an increasi…
Enhancing the Accuracy and Fairness of Human Decision Making
Isabel Valera, Adish Singla, Manuel Gomez Rodriguez
Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken…
Teaching Multiple Concepts to a Forgetful Learner
Anette Hunziker, Yuxin Chen, Oisin Mac Aodha +5
How can we help a forgetful learner learn multiple concepts within a limited time frame? While there have been extensive studies in designing optimal schedules for teaching a singl…
NeVAE: A Deep Generative Model for Molecular Graphs
Bidisha Samanta, Abir De, Gourhari Jana +3
Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data.…
Fake News Detection in Social Networks via Crowd Signals
Sebastian Tschiatschek, Adish Singla, Manuel Gomez Rodriguez +2
Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating us…
Fairness Constraints: Mechanisms for Fair Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +1
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of d…
Shaping Social Activity by Incentivizing Users
Mehrdad Farajtabar, Nan Du, Manuel Gomez Rodriguez +3
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous ev…
Influence Maximization in Continuous Time Diffusion Networks
Manuel Gomez Rodriguez, Bernhard Schölkopf
The problem of finding the optimal set of source nodes in a diffusion network that maximizes the spread of information, influence, and diseases in a limited amount of time depends…
Submodular Inference of Diffusion Networks from Multiple Trees
Manuel Gomez Rodriguez, Bernhard Schölkopf
Diffusion and propagation of information, influence and diseases take place over increasingly larger networks. We observe when a node copies information, makes a decision or become…