Search Results for author:
Found 10 papers, 2 papers with code
Approximating a RUM from Distributions on k-Slates
In this work we consider the problem of fitting Random Utility Models (RUMs) to user choices.
Online Facility Location with Multiple Advice
Clustering is a central topic in unsupervised learning and its online formulation has received a lot of attention in recent years.
Spectral Robustness for Correlation Clustering Reconstruction in Semi-Adversarial Models
We study the reconstruction version of this problem in which one is seeking to reconstruct a latent clustering that has been corrupted by random noise and adversarial modifications.
On Additive Approximate Submodularity
We show that an approximately submodular function defined on a ground set of $n$ elements is $O(n^2)$ pointwise-close to a submodular function.
Mallows Models for Top-k Lists
The classic Mallows model is a widely-used tool to realize distributions on per- mutations.
A Reduction for Efficient LDA Topic Reconstruction
We present a novel approach for LDA (Latent Dirichlet Allocation) topic reconstruction.
Learning a Mixture of Two Multinomial Logits
In this model, a user is offered a slate of choices (a subset of a finite universe of $n$ items), and selects exactly one item from the slate, each with probability proportional to its (positive) weight.
Fair Clustering Through Fairlets
We show that any fair clustering problem can be decomposed into first finding good fairlets, and then using existing machinery for traditional clustering algorithms.
Algorithms for $\ell_p$ Low-Rank Approximation
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise $\ell_p$-approximation error, for any $p \geq 1$; the case $p = 2$ is the classical SVD problem.
Reconstructing Patterns of Information Diffusion from Incomplete Observations
There is a tree T that we cannot observe directly (representing the structure along which the information has spread), and certain nodes randomly decide to make their copy of the information public.
