NIPS Proceedings
^{β}
Books
Pradeep K. Ravikumar
30 Papers
Connecting Optimization and Regularization Paths
(2018)
DAGs with NO TEARS: Continuous Optimization for Structure Learning
(2018)
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization
(2018)
Representer Point Selection for Explaining Deep Neural Networks
(2018)
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
(2018)
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
(2017)
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
(2017)
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
(2016)
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
(2015)
Closed-form Estimators for High-dimensional Generalized Linear Models
(2015)
Collaborative Filtering with Graph Information: Consistency and Scalable Methods
(2015)
Consistent Multilabel Classification
(2015)
Fast Classification Rates for High-dimensional Gaussian Generative Models
(2015)
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial
(2015)
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent
(2015)
A Representation Theory for Ranking Functions
(2014)
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs
(2014)
Consistent Binary Classification with Generalized Performance Metrics
(2014)
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings
(2014)
Elementary Estimators for Graphical Models
(2014)
On the Information Theoretic Limits of Learning Ising Models
(2014)
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
(2014)
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
(2014)
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space
(2014)
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables
(2013)
Conditional Random Fields via Univariate Exponential Families
(2013)
Dirty Statistical Models
(2013)
Large Scale Distributed Sparse Precision Estimation
(2013)
Learning with Noisy Labels
(2013)
On Poisson Graphical Models
(2013)