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Found 38 papers, 4 papers with code
Average-Case Analysis of Iterative Voting
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.
LLM-augmented Preference Learning from Natural Language
Comparing performances with existing methods, we see that pre-trained LLMs are able to outperform the previous SotA models with no fine-tuning involved.
Determining Winners in Elections with Absent Votes
An important question in elections is the determine whether a candidate can be a winner when some votes are absent.
First-Choice Maximality Meets Ex-ante and Ex-post Fairness
The generalized probabilistic Boston mechanism is also ex-post EF1, and satisfies ex-ante efficiency instead of fairness.
Differentially Private Condorcet Voting
Designing private voting rules is an important and pressing problem for trustworthy democracy.
Most Equitable Voting Rules
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.
Anti-Malware Sandbox Games
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.
The Impact of a Coalition: Assessing the Likelihood of Voter Influence in Large Elections
For centuries, it has been widely believed that the influence of a small coalition of voters is negligible in a large election.
The Semi-Random Satisfaction of Voting Axioms
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.
Favoring Eagerness for Remaining Items: Designing Efficient, Fair, and Strategyproof Mechanisms
In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner.
Semi-Random Impossibilities of Condorcet Criterion
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 $1-\Omega(\frac{B}{\sqrt n})$.
Smoothed Differential Privacy
Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis.
Certifiably Robust Interpretation via Renyi Differential Privacy
The advantages of our Renyi-Robust-Smooth (RDP-based interpretation method) are three-folds.
The Smoothed Satisfaction of Voting Axioms
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.
Sequential Mechanisms for Multi-type Resource Allocation
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.
Fair and Efficient Allocations under Lexicographic Preferences
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
Optimal Statistical Hypothesis Testing for Social Choice
We address the following question in this paper: "What are the most robust statistical methods for social choice?''
The Smoothed Possibility of Social Choice
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.
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences
We extend mixtures of RUMs with features to models that generate incomplete preferences and characterize their identifiability.
Dual Learning: Theoretical Study and an Algorithmic Extension
Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc.
Probabilistic Serial Mechanism for Multi-Type Resource Allocation
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.
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
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).
Multi-type Resource Allocation with Partial Preferences
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.
Minimizing Time-to-Rank: A Learning and Recommendation Approach
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.
Frustratingly Easy Truth Discovery
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.
Differential Privacy for Eye-Tracking Data
As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community.
Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making.
Towards Non-Parametric Learning to Rank
Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix.
Composite Marginal Likelihood Methods for Random Utility Models
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.
Practical Algorithms for STV and Ranked Pairs with Parallel Universes Tiebreaking
We also propose novel ILP formulations for PUT-winners under STV and RP, respectively.
A Cost-Effective Framework for Preference Elicitation and Aggregation
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.
Learning Mixtures of Plackett-Luce Models
In this paper we address the identifiability and efficient learning problems of finite mixtures of Plackett-Luce models for rank data.
Welfare of Sequential Allocation Mechanisms for Indivisible Goods
Sequential allocation is a simple and attractive mechanism for the allocation of indivisible goods.
Allocating Indivisible Items in Categorized Domains
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.
Possible and Necessary Allocations via Sequential Mechanisms
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.
A Statistical Decision-Theoretic Framework for Social Choice
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.
Generalized Method-of-Moments for Rank Aggregation
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.
Preference Elicitation For General Random Utility Models
We also prove uni-modality of the likelihood functions for a class of GRUMs.
