Devavrat Shah

Matrix Estimation, Latent Variable Model and Collaborative Filtering

2018 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

Estimating a matrix based on partial, noisy observations is prevalent in variety of modern applications with recommendation system being a prototypical example. The non-parametric latent variable model provides canonical representation for such matrix data when …

Learning RUMs: Reducing Mixture to Single Component via PCA

2018 · arXiv (Cornell University)

We consider the problem of learning a mixture of Random Utility Models (RUMs). Despite the success of RUMs in various domains and the versatility of mixture RUMs to capture the heterogeneity in preferences, there has …

Time varying regression with hidden linear dynamics

2021 · arXiv (Cornell University)

We revisit a model for time-varying linear regression that assumes the unknown parameters evolve according to a linear dynamical system. Counterintuitively, we show that when the underlying dynamics are stable the parameters of this model …