1 code implementation • 10 Sep 2023 • Amanda Aird, Cassidy All, Paresha Farastu, Elena Stefancova, Joshua Sun, Nicholas Mattei, Robin Burke
Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations.
no code implementations • 14 Jul 2023 • Marianna B. Ganapini, Francesco Fabiano, Lior Horesh, Andrea Loreggia, Nicholas Mattei, Keerthiram Murugesan, Vishal Pallagani, Francesca Rossi, Biplav Srivastava, Brent Venable
Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities.
no code implementations • 2 Mar 2023 • Amanda Aird, Paresha Farastu, Joshua Sun, Elena Štefancová, Cassidy All, Amy Voida, Nicholas Mattei, Robin Burke
Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks.
no code implementations • 18 Nov 2022 • Xiaolin Sun, Jacob Masur, Ben Abramowitz, Nicholas Mattei, Zizhan Zheng
We introduce a novel formal model of \emph{pandering}, or strategic preference reporting by candidates seeking to be elected, and examine the resilience of two democratic voting systems to pandering within a single round and across multiple rounds.
no code implementations • 15 Nov 2022 • Ben Abramowitz, Omer Lev, Nicholas Mattei
We consider the problem of determining a binary ground truth using advice from a group of independent reviewers (experts) who express their guess about a ground truth correctly with some independent probability (competence).
no code implementations • 15 Nov 2022 • Ben Abramowitz, Nicholas Mattei
Agents care not only about the outcomes of collective decisions but also about how decisions are made.
no code implementations • 8 Sep 2022 • Paresha Farastu, Nicholas Mattei, Robin Burke
The concern is that a bossy user may be able to shift the cost of fairness to others, improving their own outcomes and worsening those for others.
no code implementations • 3 Jun 2022 • Ben Abramowitz, Nicholas Mattei
While a full answer depends on the type of signal, correlation of signals, and desired output, a problem common to all of these applications is that of differentiating sources based on their quality and weighting them accordingly.
no code implementations • 21 Feb 2022 • Arie Glazier, Andrea Loreggia, Nicholas Mattei, Taher Rahgooy, Francesca Rossi, Brent Venable
Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency?
no code implementations • 19 Jan 2022 • Edmond Awad, Sydney Levine, Andrea Loreggia, Nicholas Mattei, Iyad Rahwan, Francesca Rossi, Kartik Talamadupula, Joshua Tenenbaum, Max Kleiman-Weiner
We can invent novel rules on the fly.
no code implementations • 18 Jan 2022 • Marianna B. Ganapini, Murray Campbell, Francesco Fabiano, Lior Horesh, Jon Lenchner, Andrea Loreggia, Nicholas Mattei, Taher Rahgooy, Francesca Rossi, Biplav Srivastava, Brent Venable
Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning.
no code implementations • 5 Oct 2021 • Marianna Bergamaschi Ganapini, Murray Campbell, Francesco Fabiano, Lior Horesh, Jon Lenchner, Andrea Loreggia, Nicholas Mattei, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable
AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life.
no code implementations • 22 Sep 2021 • Arie Glazier, Andrea Loreggia, Nicholas Mattei, Taher Rahgooy, Francesca Rossi, K. Brent Venable
To this end, we propose a novel inverse reinforcement learning (IRL) method for learning implicit hard and soft constraints from demonstrations, enabling agents to quickly adapt to new settings.
no code implementations • 21 Jul 2021 • Omer Lev, Nicholas Mattei, Paolo Turrini, Stanislav Zhydkov
In the peer selection problem a group of agents must select a subset of themselves as winners for, e. g., peer-reviewed grants or prizes.
no code implementations • 4 Dec 2020 • Jaelle Scheuerman, Jason Harman, Nicholas Mattei, K. Brent Venable
In multi-winner approval voting (AV), an agent submits a ballot consisting of approvals for as many candidates as they wish, and winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most approvals.
no code implementations • 12 Oct 2020 • Grady Booch, Francesco Fabiano, Lior Horesh, Kiran Kate, Jon Lenchner, Nick Linck, Andrea Loreggia, Keerthiram Murugesan, Nicholas Mattei, Francesca Rossi, Biplav Srivastava
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making.
no code implementations • 5 Sep 2020 • Nasim Sonboli, Robin Burke, Nicholas Mattei, Farzad Eskandanian, Tian Gao
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit.
1 code implementation • 30 Apr 2020 • Nicholas Mattei, Paolo Turrini, Stanislav Zhydkov
In particular, it does not require an explicit partitioning of the agents, as previous algorithms in the literature.
no code implementations • 21 Feb 2020 • Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei
Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains.
1 code implementation • 9 Dec 2019 • Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson
We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting.
no code implementations • 29 Nov 2019 • Jaelle Scheuerman, Jason L. Harman, Nicholas Mattei, K. Brent Venable
In real world voting scenarios, people often do not have complete information about other voter preferences and it can be computationally complex to identify a strategy that will maximize their expected utility.
no code implementations • 5 Nov 2019 • Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
no code implementations • 28 May 2019 • Jaelle Scheuerman, Jason L. Harman, Nicholas Mattei, K. Brent Venable
In multi-winner approval voting (AV), an agent may vote for as many candidates as they wish.
no code implementations • 10 Dec 2018 • Francesca Rossi, Nicholas Mattei
We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight.
no code implementations • AKBC 2019 • Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
no code implementations • EMNLP 2018 • Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock
Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set.
no code implementations • 21 Sep 2018 • Andrea Loreggia, Nicholas Mattei, Francesca Rossi, K. Brent Venable
CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations.
no code implementations • 21 Sep 2018 • Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush Varshney, Murray Campbell, Moninder Singh, Francesca Rossi
To ensure that agents behave in ways aligned with the values of the societies in which they operate, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society.
Multi-Objective Reinforcement Learning reinforcement-learning
no code implementations • 15 Sep 2018 • Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa, Mo Yu, Kartik Talamadupula, Ibrahim Abdelaziz, Maria Chang, Achille Fokoue, Bassem Makni, Nicholas Mattei, Michael Witbrock
To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain.
no code implementations • 15 Sep 2018 • Avinash Balakrishnan, Djallel Bouneffouf, Nicholas Mattei, Francesca Rossi
To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints.
no code implementations • 15 Sep 2018 • Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
no code implementations • WS 2018 • Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue-Nkoutche, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock
We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset.
1 code implementation • 14 May 2018 • Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey Kephart, Nicholas Mattei, Hui Su, Lirong Xia
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.
no code implementations • 19 May 2017 • Jing Wu Lian, Nicholas Mattei, Renee Noble, Toby Walsh
Motivated by the common academic problem of allocating papers to referees for conference reviewing we propose a novel mechanism for solving the assignment problem when we have a two sided matching problem with preferences from one side (the agents/reviewers) over the other side (the objects/papers) and both sides have capacity constraints.
no code implementations • 26 Jan 2017 • Emanuelle Burton, Judy Goldsmith, Sven Koenig, Benjamin Kuipers, Nicholas Mattei, Toby Walsh
The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses.
no code implementations • 3 Aug 2016 • Nicholas Mattei, Toby Walsh
Computational Social Choice (ComSoc) is a rapidly developing field at the intersection of computer science, economics, social choice, and political science.
no code implementations • 31 May 2016 • Andres Abeliuk, Haris Aziz, Gerardo Berbeglia, Serge Gaspers, Petr Kalina, Nicholas Mattei, Dominik Peters, Paul Stursberg, Pascal Van Hentenryck, Toby Walsh
We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently.
1 code implementation • 13 Apr 2016 • Haris Aziz, Omer Lev, Nicholas Mattei, Jeffrey S. Rosenschein, Toby Walsh
Peer reviews, evaluations, and selections are a fundamental aspect of modern science.
no code implementations • 21 Aug 2014 • Haris Aziz, Casey Cahan, Charles Gretton, Phillip Kilby, Nicholas Mattei, Toby Walsh
We propose and evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting.
no code implementations • 11 Jul 2014 • Haris Aziz, Serge Gaspers, Joachim Gudmundsson, Simon Mackenzie, Nicholas Mattei, Toby Walsh
We study computational aspects of three prominent voting rules that use approval ballots to elect multiple winners.
no code implementations • 23 Apr 2013 • Nicholas Mattei, Nina Narodytska, Toby Walsh
Indeed, we prove that it can be NP-hard to control an election by breaking ties even with a two-stage voting rule.