Search Results for author: Nicholas Mattei

Found 33 papers, 2 papers with code

Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations

no code implementations22 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.

Decision Making

Peer Selection with Noisy Assessments

no code implementations21 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.

Modeling Voters in Multi-Winner Approval Voting

no code implementations4 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.

"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation

no code implementations5 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.

Fairness Recommendation Systems

PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection

1 code implementation30 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.

A Multi-Channel Neural Graphical Event Model with Negative Evidence

no code implementations21 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.

Group Fairness in Bandit Arm Selection

no code implementations9 Dec 2019 Candice Schumann, Zhi Lang, Nicholas Mattei, John P. Dickerson

In this work we explore two definitions of fairness: equal group probability, wherein the probability of pulling an arm from any of the protected groups is the same; and proportional parity, wherein the probability of choosing an arm from a particular group is proportional to the size of that group.

Fairness

Heuristic Strategies in Uncertain Approval Voting Environments

no code implementations29 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.

Decision Making

Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

no code implementations5 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.

Knowledge Graphs Natural Language Inference

Heuristics in Multi-Winner Approval Voting

no code implementations28 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.

Building Ethically Bounded AI

no code implementations10 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.

Decision Making Fairness

Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration

no code implementations21 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.

CPMetric: Deep Siamese Networks for Learning Distances Between Structured Preferences

no code implementations21 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.

Decision Making Metric Learning

Answering Science Exam Questions Using Query Rewriting with Background Knowledge

no code implementations15 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.

Information Retrieval Natural Language Inference +1

Incorporating Behavioral Constraints in Online AI Systems

no code implementations15 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.

A Cost-Effective Framework for Preference Elicitation and Aggregation

no code implementations14 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.

The Conference Paper Assignment Problem: Using Order Weighted Averages to Assign Indivisible Goods

no code implementations19 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.

Decision Making

Ethical Considerations in Artificial Intelligence Courses

no code implementations26 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.

Empirical Evaluation of Real World Tournaments

no code implementations3 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.

Interdependent Scheduling Games

no code implementations31 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.

A Study of Proxies for Shapley Allocations of Transport Costs

no code implementations21 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.

Computational Aspects of Multi-Winner Approval Voting

no code implementations11 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.

How Hard Is It to Control an Election by Breaking Ties?

no code implementations23 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.

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