no code implementations • 13 Feb 2024 • Joshua Kavner, Lirong Xia
Iterative voting is a natural model of repeated strategic decision-making in social choice when agents have the opportunity to update their votes prior to finalizing the group decision.
no code implementations • 12 Oct 2023 • Inwon Kang, Sikai Ruan, Tyler Ho, Jui-Chien Lin, Farhad Mohsin, Oshani Seneviratne, Lirong Xia
Comparing performances with existing methods, we see that pre-trained LLMs are able to outperform the previous SotA models with no fine-tuning involved.
no code implementations • 11 Oct 2023 • Qishen Han, Amélie Marian, Lirong Xia
An important question in elections is the determine whether a candidate can be a winner when some votes are absent.
no code implementations • 8 May 2023 • Xiaoxi Guo, Sujoy Sikdar, Lirong Xia, Yongzhi Cao, Hanpin Wang
The generalized probabilistic Boston mechanism is also ex-post EF1, and satisfies ex-ante efficiency instead of fairness.
no code implementations • 27 Jun 2022 • Zhechen Li, Ao Liu, Lirong Xia, Yongzhi Cao, Hanpin Wang
Designing private voting rules is an important and pressing problem for trustworthy democracy.
no code implementations • 30 May 2022 • Lirong Xia
However, the ANR impossibility -- there is no voting rule that satisfies anonymity, neutrality, and resolvability (always choosing one winner) -- holds even in the simple setting of two alternatives and two agents.
no code implementations • 28 Feb 2022 • Sujoy Sikdar, Sikai Ruan, Qishen Han, Paween Pitimanaaree, Jeremy Blackthorne, Bulent Yener, Lirong Xia
We develop a game theoretic model of malware protection using the state-of-the-art sandbox method, to characterize and compute optimal defense strategies for anti-malware.
no code implementations • 13 Feb 2022 • Lirong Xia
For centuries, it has been widely believed that the influence of a small coalition of voters is negligible in a large election.
no code implementations • NeurIPS 2021 • Lirong Xia
We initiate the work towards a comprehensive picture of the worst average-case satisfaction of voting axioms in semi-random models, to provide a finer and more realistic foundation for comparing voting rules.
no code implementations • 18 Sep 2021 • Xiaoxi Guo, Sujoy Sikdar, Lirong Xia, Yongzhi Cao, Hanpin Wang
In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner.
no code implementations • 14 Jul 2021 • Lirong Xia
We strengthen previous work by proving the first set of semi-random impossibilities for voting rules to satisfy CC and the more general, group versions of the four desiderata: for any sufficiently large number of voters $n$, any size of the group $1\le B\le \sqrt n$, any voting rule $r$, and under a large class of {\em semi-random} models that include Impartial Culture, the likelihood for $r$ to satisfy CC and Par, CC and HM, CC and MM, or CC and SP is $1-\Omega(\frac{B}{\sqrt n})$.
no code implementations • 4 Jul 2021 • Ao Liu, Xiaoyu Chen, Sijia Liu, Lirong Xia, Chuang Gan
The advantages of our Renyi-Robust-Smooth (RDP-based interpretation method) are three-folds.
no code implementations • 4 Jul 2021 • Ao Liu, Yu-Xiang Wang, Lirong Xia
Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis.
no code implementations • 3 Jun 2021 • Lirong Xia
We initiate the work towards a comprehensive picture of the smoothed satisfaction of voting axioms, to provide a finer and more realistic foundation for comparing voting rules.
no code implementations • 29 Jan 2021 • Sujoy Sikdar, Xiaoxi Guo, Haibin Wang, Lirong Xia, Yongzhi Cao
We study the relationship between properties of the local mechanisms, each responsible for assigning all of the resources of a designated type, and the properties of a sequential mechanism which is composed of these local mechanisms, one for each type, applied sequentially, under lexicographic preferences, a well studied model of preferences over multiple types of resources in artificial intelligence and economics.
1 code implementation • 14 Dec 2020 • Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, Lirong Xia
Envy-freeness up to any good (EFX) provides a strong and intuitive guarantee of fairness in the allocation of indivisible goods.
Fairness Computer Science and Game Theory
no code implementations • 19 Jun 2020 • Lirong Xia
We address the following question in this paper: "What are the most robust statistical methods for social choice?''
no code implementations • NeurIPS 2020 • Lirong Xia
We develop a framework that leverages the smoothed complexity analysis by Spielman and Teng to circumvent paradoxes and impossibility theorems in social choice, motivated by modern applications of social choice powered by AI and ML.
no code implementations • 6 Jun 2020 • Zhibing Zhao, Ao Liu, Lirong Xia
We extend mixtures of RUMs with features to models that generate incomplete preferences and characterize their identifiability.
no code implementations • 17 May 2020 • Zhibing Zhao, Yingce Xia, Tao Qin, Lirong Xia, Tie-Yan Liu
Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc.
no code implementations • 25 Apr 2020 • Xiaoxi Guo, Sujoy Sikdar, Haibin Wang, Lirong Xia, Yongzhi Cao, Hanpin Wang
For MTRAs with divisible items, we show that the existing multi-type probabilistic serial (MPS) mechanism satisfies the stronger efficiency notion of lexi-efficiency, and is sd-envy-free under strict linear preferences, and sd-weak-strategyproof under lexicographic preferences.
1 code implementation • NeurIPS 2019 • Zhibing Zhao, Lirong Xia
We prove that when the dataset consists of combinations of ranked top-$l_1$ and $l_2$-way (or choice data over up to $l_2$ alternatives), mixture of $k$ Plackett-Luce models is not identifiable when $l_1+l_2\le 2k-1$ ($l_2$ is set to $1$ when there are no $l_2$-way orders).
no code implementations • 13 Jun 2019 • Haibin Wang, Sujoy Sikdar, Xiaoxi Guo, Lirong Xia, Yongzhi Cao, Hanpin Wang
We propose multi-type probabilistic serial (MPS) and multi-type random priority (MRP) as extensions of the well known PS and RP mechanisms to the multi-type resource allocation problem (MTRA) with partial preferences.
1 code implementation • 27 May 2019 • Haoming Li, Sujoy Sikdar, Rohit Vaish, Junming Wang, Lirong Xia, Chaonan Ye
Consider the following problem faced by an online voting platform: A user is provided with a list of alternatives, and is asked to rank them in order of preference using only drag-and-drop operations.
no code implementations • 2 May 2019 • Reshef Meir, Ofra Amir, Omer Ben-Porat, Tsviel Ben-Shabat, Gal Cohensius, Lirong Xia
Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources.
no code implementations • 15 Apr 2019 • Ao Liu, Lirong Xia, Andrew Duchowski, Reynold Bailey, Kenneth Holmqvist, Eakta Jain
As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community.
no code implementations • 16 Jan 2019 • Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making.
no code implementations • 9 Jul 2018 • Ao Liu, Qiong Wu, Zhenming Liu, Lirong Xia
Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix.
no code implementations • ICML 2018 • Zhibing Zhao, Lirong Xia
We propose a novel and flexible rank-breaking-then-composite-marginal-likelihood (RBCML) framework for learning random utility models (RUMs), which include the Plackett-Luce model.
no code implementations • 17 May 2018 • Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia
We also propose novel ILP formulations for PUT-winners under STV and RP, respectively.
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 • 23 Mar 2016 • Zhibing Zhao, Peter Piech, Lirong Xia
In this paper we address the identifiability and efficient learning problems of finite mixtures of Plackett-Luce models for rank data.
no code implementations • 26 Nov 2015 • Haris Aziz, Thomas Kalinowski, Toby Walsh, Lirong Xia
Sequential allocation is a simple and attractive mechanism for the allocation of indivisible goods.
no code implementations • 22 Apr 2015 • Erika Mackin, Lirong Xia
Then, we propose a natural extension of serial dictatorships called categorial sequential allocation mechanisms (CSAMs), which allocate the items in multiple rounds: in each round, the active agent chooses an item from a designated category.
no code implementations • 6 Dec 2014 • Haris Aziz, Toby Walsh, Lirong Xia
We focus on possible and necessary allocation problems, checking whether allocations of a given form occur in some or all mechanisms for several commonly used classes of sequential allocation mechanisms.
no code implementations • NeurIPS 2014 • Hossein Azari Soufiani, David C. Parkes, Lirong Xia
In our framework, we are given a statistical ranking model, a decision space, and a loss function defined on (parameter, decision) pairs, and formulate social choice mechanisms as decision rules that minimize expected loss.
no code implementations • NeurIPS 2013 • Hossein Azari Soufiani, William Chen, David C. Parkes, Lirong Xia
In this paper we propose a class of efficient Generalized Method-of-Moments(GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives.
no code implementations • 26 Sep 2013 • Hossein Azari Soufiani, David C. Parkes, Lirong Xia
We also prove uni-modality of the likelihood functions for a class of GRUMs.