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Pradeep K. Ravikumar

29 papers · 2006–2023 · 1 conference · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (24) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (13) πŸƒ Academic Marathon (17) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (7) 🏠 Conference Loyalist (29) 🀝 Dynamic Duo (29) 🌱 Topic Pioneer πŸ’Ž Century Club (29) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (7) ⚑ Prolific Year (6) ❓ The Questioner πŸ—ƒοΈ Keyword Collector (83)

Conferences

NIPS (29)

Top co-authors

Pradeep K Ravikumar (29) Bryon Aragam (6) Inderjit S. Dhillon (4) Martin J. Wainwright (4) Inderjit S Dhillon (4) Goutham Rajendran (3) Arun Suggala (3) Bin Yu (3) Kevin Bello (3) Chih-Kuan Yeh (3)

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) β„“1 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