1 code implementation • 25 Sep 2024 • Ivi Chatzi, Nina Corvelo Benz, Eleni Straitouri, Stratis Tsirtsis, Manuel Gomez-Rodriguez
Our model allows any large language model to perform counterfactual token generation at almost no cost in comparison with vanilla token generation, it is embarrassingly simple to implement, and it does not require any fine-tuning nor prompt engineering.
1 code implementation • 27 May 2024 • Giovanni De Toni, Nastaran Okati, Suhas Thejaswi, Eleni Straitouri, Manuel Gomez-Rodriguez
Then, we show that the problem of finding the optimal prediction sets under which the human experts achieve the highest average accuracy is NP-hard.
no code implementations • 24 May 2024 • Seungeon Lee, Nina Corvelo Benz, Suhas Thejaswi, Manuel Gomez-Rodriguez
Then, we develop a post-processing algorithm that, given placement decisions made by a default policy on a pool of refugees and their employment outcomes, solves an inverse~matching problem to minimally modify the predictions made by a given classifier.
1 code implementation • NeurIPS 2023 • 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 decisions, the patient's health would have improved.
no code implementations • 23 Sep 2021 • Eleni Straitouri, Adish Singla, Vahid Balazadeh Meresht, Manuel Gomez-Rodriguez
Methods to learn under algorithmic triage have predominantly focused on supervised learning settings where each decision, or prediction, is independent of each other.
1 code implementation • NeurIPS 2021 • Stratis Tsirtsis, Abir De, Manuel Gomez-Rodriguez
In this work, we initiate the development of methods to find counterfactual explanations for decision making processes in which multiple, dependent actions are taken sequentially over time.
1 code implementation • 30 Jun 2021 • Stratis Tsirtsis, Abir De, Lars Lorch, Manuel Gomez-Rodriguez
Testing is recommended for all close contacts of confirmed COVID-19 patients.
2 code implementations • NeurIPS 2021 • Nastaran Okati, Abir De, Manuel Gomez-Rodriguez
However, the interplay between the prediction accuracy of the model and the human experts under algorithmic triage is not well understood.
1 code implementation • 9 Oct 2020 • Utkarsh Upadhyay, Graham Lancashire, Christoph Moser, Manuel Gomez-Rodriguez
Our randomized controlled trial also reveals that the learners whose study sessions are optimized using machine learning are $\sim$50% more likely to return to the app within 4-7 days.
1 code implementation • 21 Jun 2020 • Abir De, Nastaran Okati, Ali Zarezade, Manuel Gomez-Rodriguez
Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone.
2 code implementations • 15 Apr 2020 • Lars Lorch, Heiner Kremer, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, Manuel Gomez-Rodriguez
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.
no code implementations • 11 Feb 2020 • Vahid Balazadeh, Abir De, Adish Singla, Manuel Gomez-Rodriguez
Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions.
1 code implementation • NeurIPS 2020 • Stratis Tsirtsis, Manuel Gomez-Rodriguez
In this paper, our goal is to find policies and counterfactual explanations that are optimal in terms of utility in such a strategic setting.
1 code implementation • 6 Sep 2019 • Abir De, Nastaran Okati, Paramita Koley, Niloy Ganguly, Manuel Gomez-Rodriguez
In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels.
no code implementations • 1 Sep 2019 • Abir De, Adish Singla, Utkarsh Upadhyay, Manuel Gomez-Rodriguez
As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback.
1 code implementation • 22 May 2019 • Stratis Tsirtsis, Behzad Tabibian, Moein Khajehnejad, Adish Singla, Bernhard Schölkopf, Manuel Gomez-Rodriguez
Using this characterization, we first show that, in general, we cannot expect to find optimal decision policies in polynomial time and there are cases in which deterministic policies are suboptimal.
1 code implementation • 8 Feb 2019 • Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility.
1 code implementation • Proceedings of the National Academy of Sciences (PNAS) 2019 • Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Schölkopf, Manuel Gomez-Rodriguez
Spaced repetition is a technique for efficient memorization which uses repeated review of content following a schedule determined by a spaced repetition algorithm to improve long-term retention.
no code implementations • 19 Nov 2018 • Khashayar Gatmiry, Manuel Gomez-Rodriguez
Then, we show that the same greedy algorithm offers a constant approximation factor of $(1 + 1/(1-\alpha))^{-1}$, where $\alpha$ is the generalized curvature of the function.
no code implementations • 30 Oct 2018 • Lars Lorch, Abir De, Samir Bhatt, William Trouleau, Utkarsh Upadhyay, Manuel Gomez-Rodriguez
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps.
1 code implementation • NeurIPS 2018 • Utkarsh Upadhyay, Abir De, Manuel Gomez-Rodriguez
In this paper, we address the above problem from the perspective of deep reinforcement learning of marked temporal point processes, where both the actions taken by an agent and the feedback it receives from the environment are asynchronous stochastic discrete events characterized using marked temporal point processes.
1 code implementation • 19 Feb 2018 • Utkarsh Upadhyay, Abir De, Aasish Pappu, Manuel Gomez-Rodriguez
Sports, and the Newsroom app suggest that unidimensional opinion models may often be unable to accurately represent online discussions, provide insights into human judgements and opinions, and show that our framework is able to circumvent language nuances such as sarcasm or humor by relying on human judgements instead of textual analysis.
no code implementations • 19 Feb 2018 • Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez
At a network level, they may increase activity by incentivizing a few influential users to take more actions, which in turn will trigger additional actions by other users.
2 code implementations • 14 Feb 2018 • Bidisha Samanta, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, Manuel Gomez-Rodriguez
Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates.
no code implementations • 5 Dec 2017 • Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Schoelkopf, Manuel Gomez-Rodriguez
Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition?
1 code implementation • 27 Nov 2017 • Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schoelkopf, Manuel Gomez-Rodriguez
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 story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking.
no code implementations • 14 Dec 2016 • Utkarsh Upadhyay, Isabel Valera, Manuel Gomez-Rodriguez
In this paper, we present a probabilistic modeling framework of crowdlearning, which uncovers the evolution of a user's expertise over time by leveraging other users' assessments of her contributions.
no code implementations • 8 Dec 2016 • Nan Du, YIngyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha, Le Song
A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time.
no code implementations • 24 Oct 2016 • Behzad Tabibian, Isabel Valera, Mehrdad Farajtabar, Le Song, Bernhard Schölkopf, Manuel Gomez-Rodriguez
Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness.
no code implementations • 22 May 2016 • Mohammad Reza Karimi, Erfan Tavakoli, Mehrdad Farajtabar, Le Song, Manuel Gomez-Rodriguez
Many users in online social networks are constantly trying to gain attention from their followers by broadcasting posts to them.
no code implementations • 12 May 2014 • Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schoelkopf
Can we recover the hidden network structures from these observed cascades?