Browsing Rensselaer Libraries by Author "Xia, Lirong"

Now showing items 1-16 of 16

    • A network and agent basedmodel of political polarization 

      Tabin, Daniel R. (Rensselaer Polytechnic Institute, Troy, NY, 2021-05)
      In this thesis we discuss the model from the paper “Polarization and Tipping Points” which the author of this thesis coauthored. We go into depth about the decisioning and reasoning behind multiple features of the model ...

    • An algorithm to find potential alternative narrative news articles 

      Yu, Ziniu (Rensselaer Polytechnic Institute, Troy, NY, 2018-05)
      The polarization in politics often leads to a large number of news articles with different points of view. Some of the news contains biased or incorrect information. Some may include conspiracy theories or even made up ...

    • Analysis of inefficiencies in systems with many independent agents 

      Postl, John (Rensselaer Polytechnic Institute, Troy, NY, 2016-05)
      In group formation games, our objective is to find a partition of agents that maximizes the total utility received by the agents. However, if the agents are selfish and only care to maximize their own individual utilities, ...

    • Applications of deep network signatures to subgraph classification, quantification of network structure and topologically heterogeneous node classification 

      Hegde, Kshiteesh (Rensselaer Polytechnic Institute, Troy, NY, 2018-08)
      1. sample a large network such that downstream computations on the sparsified network produce results that are faithful to the full network. Given a large network, we employ several ways of obtaining its sparsified version ...

    • Deciding who, what, why, and how : aggregating preferences over agents, alternatives, axioms, and rules 

      Abramowitz, Ben (Rensselaer Polytechnic Institute, Troy, NY, 2021-08)
      Collective decision making requires dealing with competing preferences. One probably needs little convincing that such problems can be complex and difficult to solve. There are many types of preferences, many different ...

    • Efficient preference learning for AI-powered group decision-making 

      Zhao, Zhibing (Rensselaer Polytechnic Institute, Troy, NY, 2020-05)
      Moreover, this dissertation proposes a group decision-making framework with consists of all the three aforementioned phases: a cost-effective preference elicitation method that actively collects informative preferences ...

    • Embedding fairness and ethics in collective decision-making 

      Mohsin, Farhad (Rensselaer Polytechnic Institute, Troy, NY, 2023-08)
      Collective decision-making is the problem where we have to aggregate individual preferences to collectively make a choice. Voting is one of the most commonly studied methods of making collective decisions. The task of ...

    • Generalized method of moments algorithm for learning mixtures of Plackett-Luce models 

      Piech, Peter D. (Rensselaer Polytechnic Institute, Troy, NY, 2016-05)
      It is often the primary interest of certain voting systems to be able to generate an aggregate ranking over a set of candidates or alternatives from the preferences of individual agents or voters. The Plackett-Luce model ...

    • Group decision makings from partial preferences 

      Liu, Ao (Rensselaer Polytechnic Institute, Troy, NY, 2023-05)
      Group decision making is the situation that a group of agents makes collective choices over a set of alternatives. The input of group decision making is the partial preferences of agents, and its output is one (or more) ...

    • Matching, social welfare and ordinal approximation 

      Zhu, Wennan (Rensselaer Polytechnic Institute, Troy, NY, 2020-05)
      Many important problems involve agents with preferences for different outcomes. Such settings include, for example, social choice and matching problems. Although the quality of an outcome to an agent may be measured by a ...

    • Non-discriminative algorithmic pricing: decentralized resource allocation in markets 

      Sekar, Shreyas (Rensselaer Polytechnic Institute, Troy, NY, 2017-05)
      2) Pricing under uncertainty: Even when the seller only has access to a distribution over (submodular) buyer valuations, and items have a non-linear production cost, we show that it is possible to efficiently compute prices ...

    • Optimal multi-attribute decision making in social choice problems 

      Sikdar, Sujoy Kumar (Rensselaer Polytechnic Institute, Troy, NY, 2018-12)
      Direction 2: Learning Preferences From Data. I model preferences using representation schema inspired by work on lexicographic heuristic decision involving multiple factors from the psychology literature, as well as using ...

    • Robust news veracity detection 

      Horne, Benjamin D. (Rensselaer Polytechnic Institute, Troy, NY, 2020-05)
      Second, we learn from news source behavior and relationships. In this approach, we explore content sharing behavior by both mainstream and alternative news sources. Specifically, we construct content sharing networks from ...

    • Towards an understanding of information credibility on online social networks 

      Sikdar, Sujoy Kumar (Rensselaer Polytechnic Institute, Troy, NY, 2015-05)
      A related task is that of identifying what pieces of information published on the social network are true. One approach to solve this problem treats humans on the social network as sensors with unknown reliability who sense ...

    • Towards the computation problems in multi-stage and multi-winner voting rules 

      Wang, Jun (Rensselaer Polytechnic Institute, Troy, NY, 2022-08)
      Multi-stage and multi-winner voting rules are playing an increasingly important role in society. The former consists of a large number of various procedures of multiple rounds based on repeated ballots and/or sequential ...

    • Unsupervised learning : evaluation, distributed setting, and privacy 

      Tsikhanovich, Maksim (Rensselaer Polytechnic Institute, Troy, NY, 2018-05)
      Chapter 1 is an overview of topic modeling as a set of unsupervised learning tasks. We present the Latent Dirichlet Allocation (LDA) model, and show how k-means as well as non- negative matrix factorization (NMF) can also ...