382 citations · 1.2k across the 41 of their papers we have counts for
10 papers · 1 filter
Finding Counterfactually Optimal Action Sequences in Continuous State Spaces
Stratis Tsirtsis, Manuel Gomez-Rodriguez
Whenever a clinician reflects on the efficacy of a sequence of treatment decisions for a patient, they may try to identify critical time steps where, had they made different decisi…
Reinforcement Learning Under Algorithmic Triage
Eleni Straitouri, Adish Singla, Vahid Balazadeh Meresht +1
Methods to learn under algorithmic triage have predominantly focused on supervised learning settings where each decision, or prediction, is independent of each other. Under algorit…
Counterfactual Explanations in Sequential Decision Making Under Uncertainty
Stratis Tsirtsis, Abir De, Manuel Gomez-Rodriguez
Methods to find counterfactual explanations have predominantly focused on one step decision making processes. In this work, we initiate the development of methods to find counterfa…
Large-scale randomized experiment reveals machine learning helps people learn and remember more effectively
Utkarsh Upadhyay, Graham Lancashire, Christoph Moser +1
Machine learning has typically focused on developing models and algorithms that would ultimately replace humans at tasks where intelligence is required. In this work, rather than r…
Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
Lars Lorch, Heiner Kremer, William Trouleau +4
Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. Howeve…
Decisions, Counterfactual Explanations and Strategic Behavior
Stratis Tsirtsis, Manuel Gomez-Rodriguez
As data-driven predictive models are increasingly used to inform decisions, it has been argued that decision makers should provide explanations that help individuals understand wha…
Regression Under Human Assistance
Abir De, Nastaran Okati, Paramita Koley +2
Decisions are increasingly taken by both humans and machine learning models. However, machine learning models are currently trained for full automation -- they are not aware that s…
Optimal Decision Making Under Strategic Behavior
Stratis Tsirtsis, Behzad Tabibian, Moein Khajehnejad +3
We are witnessing an increasing use of data-driven predictive models to inform decisions. As decisions have implications for individuals and society, there is increasing pressure o…
Fair Decisions Despite Imperfect Predictions
Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf +2
Consequential decisions are increasingly informed by sophisticated data-driven predictive models. However, to consistently learn accurate predictive models, one needs access to gro…
Deep Reinforcement Learning of Marked Temporal Point Processes
Utkarsh Upadhyay, Abir De, Manuel Gomez-Rodriguez
In a wide variety of applications, humans interact with a complex environment by means of asynchronous stochastic discrete events in continuous time. Can we design online intervent…