Pradeep K. Ravikumar
29 papers · 2006–2023 · 1 conference · across top CS/AI conferences
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Conferences
NIPS (29)
Top co-authors
Keywords
graphical model
(10)
structure learning
(4)
sparse optimization
(4)
causal inference
(3)
sparse learning
(3)
markov random field
(3)
high-dimensional statistics
(3)
convex optimization
(3)
high-dimensional estimation
(3)
graph structure
(3)
β1 regularization
(2)
high-dimensional optimization
(2)
representation learning
(2)
neighborhood selection
(2)
gaussian markov random field
(2)
high-dimensional regression
(2)
precision matrix
(2)
sparse inverse covariance
(2)
sparse estimation
(2)
causal discovery
(2)
Papers
Responsible AI (RAI) Games and Ensembles
NIPS 2023
Learning with Explanation Constraints
NIPS 2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
NIPS 2023
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
NIPS 2023
Sample based Explanations via Generalized Representers
NIPS 2023
Global Optimality in Bivariate Gradient-based DAG Learning
NIPS 2023
Masked Prediction: A Parameter Identifiability View
NIPS 2022
First is Better Than Last for Language Data Influence
NIPS 2022
Identifiability of deep generative models without auxiliary information
NIPS 2022
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
NIPS 2022
Boosted CVaR Classification
NIPS 2021
When Is Generalizable Reinforcement Learning Tractable?
NIPS 2021
Learning latent causal graphs via mixture oracles
NIPS 2021
On Learning Ising Models under Huber's Contamination Model
NIPS 2020
Generalized Boosting
NIPS 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
NIPS 2020
Graphical Models via Generalized Linear Models
NIPS 2012
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation
NIPS 2012
Greedy Algorithms for Structurally Constrained High Dimensional Problems
NIPS 2011
Nearest Neighbor based Greedy Coordinate Descent
NIPS 2011
On Learning Discrete Graphical Models using Greedy Methods
NIPS 2011
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
NIPS 2011
A Dirty Model for Multi-task Learning
NIPS 2010
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
NIPS 2009
Information-theoretic lower bounds on the oracle complexity of convex optimization
NIPS 2009
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE
NIPS 2008
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images
NIPS 2008
SpAM: Sparse Additive Models
NIPS 2007
High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression
NIPS 2006