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Found 16 papers, 6 papers with code
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i. e., row and column sums).
Learning Rich Rankings
Although the foundations of ranking are well established, the ranking literature has primarily been focused on simple, unimodal models, e. g. the Mallows and Plackett-Luce models, that define distributions centered around a single total ordering.
On Sinkhorn's Algorithm and Choice Modeling
For a broad class of choice and ranking models based on Luce's choice axiom, including the Bradley--Terry--Luce and Plackett--Luce models, we show that the associated maximum likelihood estimation problems are equivalent to a classic matrix balancing problem with target row and column sums.
Counterfactual Evaluation of Peer-Review Assignment Policies
We consider estimates of (i) the effect on review quality when changing weights in the assignment algorithm, e. g., weighting reviewers' bids vs. textual similarity (between the review's past papers and the submission), and (ii) the "cost of randomization", capturing the difference in expected quality between the perturbed and unperturbed optimal match.
Choice Set Confounding in Discrete Choice
Standard methods in preference learning involve estimating the parameters of discrete choice models from data of selections (choices) made by individuals from a discrete set of alternatives (the choice set).
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice
The Multinomial Logit (MNL) model and the axiom it satisfies, the Independence of Irrelevant Alternatives (IIA), are together the most widely used tools of discrete choice.
Discovering Context Effects from Raw Choice Data
An important class of such contexts is the composition of the choice set.
Social Choice Random Utility Models of Intransitive Pairwise Comparisons
There is a growing need for discrete choice models that account for the complex nature of human choices, escaping traditional behavioral assumptions such as the transitivity of pairwise preferences.
Choosing to Rank
Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict.
Improving pairwise comparison models using Empirical Bayes shrinkage
As such, different models of the comparison process lead to different shrinkage estimators.
Comparison-Based Choices
In this work we study comparison-based choice functions, a simple but surprisingly rich class of functions capable of exhibiting so-called choice-set effects.
Configuring Random Graph Models with Fixed Degree Sequences
We place particular emphasis on the importance of specifying the appropriate graph labeling (stub-labeled or vertex-labeled) under which to consider a null model, a choice that closely connects the study of random graphs to the study of random contingency tables.
Methodology Social and Information Networks Data Analysis, Statistics and Probability Physics and Society Quantitative Methods
Pairwise Choice Markov Chains
As datasets capturing human choices grow in richness and scale -- particularly in online domains -- there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom.
A concave regularization technique for sparse mixture models
Latent variable mixture models are a powerful tool for exploring the structure in large datasets.
Four Degrees of Separation
It is not completely clear whether the selected individual is part of the five, so this could actually allude to distance five or six in the language of graph theory, but the "six degrees of separation" phrase stuck after John Guare's 1990 eponymous play.
Social and Information Networks Physics and Society
The Anatomy of the Facebook Social Graph
Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure.
Social and Information Networks Physics and Society
