International Workshop on Combinatorial Optimization and Algorithmic Game Theory

International Workshop on Combinatorial Optimization and Algorithmic Game Theory

Information

Speakers

  • Haris Aziz (UNSW Sydney) 
  • Kristóf Bérczi (Eötvös Loránd University) 
  • Endre Boros (Rutgers University) 
  • Chandra Chekuri (University of Illinois, Urbana-Champaign) 
  • Naoyuki Kamiyama (Kyushu University) 
  • Telikepalli Kavitha (Tata Institute of Fundamental Research) 
  • Yusuke Kobayashi (Kyoto University) 
  • Euiwoong Lee (New York University) 

Program

  • January 13
    • 10:20--10:30
      • Opening 
    • 10:30--11:40
      • Haris Aziz (UNSW Sydney)
        Fair and efficient allocation of indivisible goods and chores 
    • 11:40--13:30
      • Lunch Break
    • 13:30--14:40
      • Naoyuki Kamiyama (Kyushu University)
        Matching problems under preferences with ties and matroid constraints 
    • 14:50--16:00
      • Kristóf Bérczi (Eötvös Loránd University)
        Supermodularity in unweighted graph optimization 
    • 16:00--16:20
      • Coffee Break
    • 16:20--17:30
      • Chandra Chekuri (University of Illinois, Urbana-Champaign)
        Parallel algorithms for submodular function maximization 
  • January 14
    • 9:30--10:40
      • Telikepalli Kavitha (Tata Institute of Fundamental Research)
        Popular matchings: Good, bad, and mixed 
    • 10:50--12:00
      • Endre Boros (Rutgers University)
        Two-person zero-sum stochastic mean payoff games 
    • 12:00--14:00
      • Lunch Break
    • 14:00--15:10
      • Yusuke Kobayashi (Kyoto University)
        Restricted 2-matching problems 
    • 15:10--15:30
      • Coffee Break
    • 15:30--16:40
      • Euiwoong Lee (New York University)
        The number of minimum k-cuts: Improving the Karger-Stein bound 
    • 16:40--16:50
      • Closing

Comments

Post a Comment

Popular posts from this blog

Smart Lotteries in School Choice Ex-ante Pareto-Improvement with Ex-post Stability

Image
Working Paper: https://cgi.cse.unsw.edu.au/~haziz/PIRMES.pdf  Abstract: In a typical school choice application, the students have strict preferences over the schools while the schools have coarse priorities over the students based on their distance and their enrolled siblings. The outcome of a centralized admission mechanism is then usually obtained by the Deferred Acceptance (DA) algorithm with random tie-breaking. Therefore, every possible outcome of this mechanism is a stable solution for the coarse priorities that will arise with certain probability. This implies a probabilistic assignment, where the admission probability for each student-school pair is specified. In this paper, we propose a new efficiency-improving stable `smart lottery' mechanism. We aim to improve the probabilistic assignment ex-ante in a stochastic dominance sense, while ensuring that the improved random matching is still ex-post stable, meaning that it can be decomposed into stable matchings regarding the ...
Read more

Proportionally Representative Clustering

Image
H. Aziz, B. Lee, S. Morota Chu, and J. Vollen. Proportionally Representative Clustering.  WINE 2024 .  [  Pdf  ]  In recent years, there has been a surge in effort to formalize notions of fairness in machine learning. We focus on centroid clustering— one of the fundamental tasks in unsupervised machine learning. We pro- pose a new axiom “proportionally representative fairness” (PRF) that is designed for clustering problems where the selection of centroids re- flects the distribution of data points and how tightly they are clustered together. Our fairness concept is not satisfied by existing fair cluster- ing algorithms. We design efficient algorithms to achieve PRF both for unconstrained and discrete clustering problems. Our algorithm for the unconstrained setting is also the first known polynomial-time approxi- mation algorithm for the well-studied Proportional Fairness (PF) axiom [ 13 ]. Our algorithm for the discrete setting also matches the best kn...
Read more

Fair budgets from ranked ballots: PB-EAR

How do you split a shared budget fairly when people rank projects that cost very different amounts? PB-EAR, from our AAAI 2021 paper  Proportionally Representative Participatory Budgeting with Ordinal Preferences  ( AAAI version ), gives every voter an equal slice of the budget and lets groups pool their slices to fund the projects they rank highest, looking further down the ballots only when nothing is yet affordable. Every cohesive group is guaranteed its proportional share, and the rule extends our  Expanding Approvals Rule  for elections to budgets with project costs, ties and unequal voter weights.  That makes it a natural fit for DAO grant rounds such as   TheDAO Security Fund 's ETHSecurity Initiatives, where badge holders will rank the security initiatives listed on   the app  to allocate the fund's budget. The two-minute video below walks through a worked example. Here's an educational tool someone built to show how PB-EAR works. 
Read more