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- Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
- NIPS
- 2012

Undirected graphical models, also known as Markov networks, enjoy popularity in a variety of applications. The popular instances of these models such as Gaussian Markov Random Fields (GMRFs), Ising models, and multinomial discrete models, however do not capture the characteristics of data in many settings. We introduce a new class of graphical models based… (More)

- Pradeep Ravikumar, Ambuj Tewari, Eunho Yang
- AISTATS
- 2011

We study the consistency of listwise ranking methods with respect to the popular Normalized Discounted Cumulative Gain (NDCG) criterion. State of the art listwise approaches replace NDCG with a surrogate loss that is easier to optimize. We characterize NDCG consistency of surrogate losses to discover a surprising fact: several commonly used surrogates are… (More)

- Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
- Journal of Machine Learning Research
- 2015

Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical… (More)

- Sung-Min Ahn, Se Jin Jang, +26 authors Gu Kong
- Hepatology
- 2014

UNLABELLED
Hepatic resection is the most curative treatment option for early-stage hepatocellular carcinoma, but is associated with a high recurrence rate, which exceeds 50% at 5 years after surgery. Understanding the genetic basis of hepatocellular carcinoma at surgically curable stages may enable the identification of new molecular biomarkers that… (More)

- Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
- AISTATS
- 2014

Markov Random Fields, or undirected graphical models are widely used to model highdimensional multivariate data. Classical instances of these models, such as Gaussian Graphical and Ising Models, as well as recent extensions (Yang et al., 2012) to graphical models specified by univariate exponential families, assume all variables arise from the same… (More)

- Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
- NIPS
- 2013

Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables. These standard instances, however, are ill-suited to modeling count data, which are increasingly ubiquitous in big-data settings such as… (More)

- Eunho Yang, Aurelie C. Lozano, Pradeep Ravikumar
- NIPS
- 2014

We propose a class of closed-form estimators for sparsity-structured graphical models, expressed as exponential family distributions, under high-dimensional settings. Our approach builds on observing the precise manner in which the classical graphical model MLE “breaks down” under high-dimensional settings. Our estimator uses a carefully constructed,… (More)

- Eunho Yang, Pradeep Ravikumar
- NIPS
- 2013

We provide a unified framework for the high-dimensional analysis of<lb>“superposition-structured” or “dirty” statistical models: where the model param-<lb>eters are a superposition of structurally constrained parameters. We allow for any<lb>number and types of structures, and any statistical model. We consider the gen-<lb>eral class of M -estimators that… (More)

- Eunho Yang, Aurelie C. Lozano, Pradeep Ravikumar
- ICML
- 2014

We consider the problem of estimating expectations of vector-valued feature functions; a special case of which includes estimating the covariance matrix of a random vector. We are interested in recovery under high-dimensional settings, where the number of features p is potentially larger than the number of samples n, and where we need to impose structural… (More)

- Eunho Yang, Aurelie C. Lozano, Pradeep Ravikumar
- ICML
- 2014

We consider the problem of structurally constrained high-dimensional linear regression. This has attracted considerable attention over the last decade, with state of the art statistical estimators based on solving regularized convex programs. While these typically non-smooth convex programs can be solved by the state of the art optimization methods in… (More)