382 citations · 1.2k across the 41 of their papers we have counts for
42 papers
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…
Group Testing under Superspreading Dynamics
Stratis Tsirtsis, Abir De, Lars Lorch +1
Testing is recommended for all close contacts of confirmed COVID-19 patients. However, existing group testing methods are oblivious to the circumstances of contagion provided by co…
Differentiable Learning Under Triage
Nastaran Okati, Abir De, Manuel Gomez-Rodriguez
Multiple lines of evidence suggest that predictive models may benefit from algorithmic triage. Under algorithmic triage, a predictive model does not predict all instances but inste…
Reconciling Security and Utility in Next-Generation Epidemic Risk Mitigation Systems
Pierfrancesco Ingo, Nichole Boufford, Ming Cheng Jiang +8
Epidemics like the recent COVID-19 require proactive contact tracing and epidemiological analysis to predict and subsequently contain infection transmissions. The proactive measure…
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…
Classification Under Human Assistance
Abir De, Nastaran Okati, Ali Zarezade +1
Most supervised learning models are trained for full automation. However, their predictions are sometimes worse than those by human experts on some specific instances. Motivated by…
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…
Can A User Anticipate What Her Followers Want?
Abir De, Adish Singla, Utkarsh Upadhyay +1
Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, sh…
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…
Stochastic Optimal Control of Epidemic Processes in Networks
Lars Lorch, Abir De, Samir Bhatt +3
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes an…
Non-submodular Function Maximization subject to a Matroid Constraint, with Applications
Khashayar Gatmiry, Manuel Gomez-Rodriguez
The standard greedy algorithm has been recently shown to enjoy approximation guarantees for constrained non-submodular nondecreasing set function maximization. While these recent r…
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…
Steering Social Activity: A Stochastic Optimal Control Point Of View
Ali Zarezade, Abir De, Utkarsh Upadhyay +2
User engagement in online social networking depends critically on the level of social activity in the corresponding platform--the number of online actions, such as posts, shares or…
On the Complexity of Opinions and Online Discussions
Utkarsh Upadhyay, Abir De, Aasish Pappu +1
In an increasingly polarized world, demagogues who reduce complexity down to simple arguments based on emotion are gaining in popularity. Are opinions and online discussions fallin…
Optimizing Human Learning
Behzad Tabibian, Utkarsh Upadhyay, Abir De +3
Spaced repetition is a technique for efficient memorization which uses repeated, spaced review of content to improve long-term retention. Can we find the optimal reviewing schedule…
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Jooyeon Kim, Behzad Tabibian, Alice Oh +2
Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a st…
Harnessing Natural Experiments to Quantify the Causal Effect of Badges
Tomasz Kusmierczyk, Manuel Gomez-Rodriguez
A wide variety of online platforms use digital badges to encourage users to take certain types of desirable actions. However, despite their growing popularity, their causal effect…
From Parity to Preference-based Notions of Fairness in Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +2
The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups.…
Cheshire: An Online Algorithm for Activity Maximization in Social Networks
Ali Zarezade, Abir De, Hamid Rabiee +1
User engagement in social networks depends critically on the number of online actions their users take in the network. Can we design an algorithm that finds when to incentivize use…