Pradeep K. Ravikumar
29 papers · 2006–2023 · 1 venue · across top CS/AI venues
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Achievements
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π£ Hot Topic Early Bird π Academic Marathon Β· Full πΊοΈ Taxonomy Completionist (68) π Interdisciplinary Bridge Β· Gold π Conference Loyalist (29)
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Deep Specialist
(12)
π§
Keyword Pioneer
π€
Dynamic Duo
(29)
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Keyword Trendsetter Combo
(10)
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Renaissance Researcher
(7)
π₯
Unstoppable Β· Bronze
β
The Questioner
β‘
Prolific Year Β· Productive
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Conference Pioneer
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Century Club Β· Bronze
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Trend Setter
ποΈ
Keyword Collector
(197)
Venues
Top co-authors
Research topics
Machine Learning
(28)
Optimization & Theory
(23)
Core Methods
(19)
Mathematics & Optimization
(15)
Learning Types Β· Machine Learning
(14)
Optimization
(14)
Artificial Intelligence
(12)
Core AI
(9)
Deep Learning
(7)
Bayesian & Probabilistic
(5)
Mathematics
(5)
Learning Types Β· Deep Learning
(4)
Application Areas
(3)
Healthcare & Medicine
(2)
Knowledge & Reasoning
(2)
Keywords
graphical model
(10)
sparse optimization
(5)
structure learning
(4)
causal inference
(3)
high-dimensional statistics
(3)
convex optimization
(3)
sparse learning
(3)
markov random field
(3)
l1 regularization
(3)
high-dimensional estimation
(3)
graph structure
(3)
representation learning
(2)
neighborhood selection
(2)
gaussian markov random field
(2)
high-dimensional optimization
(2)
β1 regularization
(2)
precision matrix
(2)
high-dimensional regression
(2)
sparse estimation
(2)
causal discovery
(2)
Papers
Global Optimality in Bivariate Gradient-based DAG Learning
NeurIPS 2023
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
NeurIPS 2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
NeurIPS 2023
Learning with Explanation Constraints
NeurIPS 2023
Responsible AI (RAI) Games and Ensembles
NeurIPS 2023
Sample based Explanations via Generalized Representers
NeurIPS 2023
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
NeurIPS 2022
First is Better Than Last for Language Data Influence
NeurIPS 2022
Identifiability of deep generative models without auxiliary information
NeurIPS 2022
Masked Prediction: A Parameter Identifiability View
NeurIPS 2022
Boosted CVaR Classification
NeurIPS 2021
Learning latent causal graphs via mixture oracles
NeurIPS 2021
When Is Generalizable Reinforcement Learning Tractable?
NeurIPS 2021
Generalized Boosting
NeurIPS 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
NeurIPS 2020
On Learning Ising Models under Huber's Contamination Model
NeurIPS 2020
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation
NeurIPS 2012
Graphical Models via Generalized Linear Models
NeurIPS 2012
Greedy Algorithms for Structurally Constrained High Dimensional Problems
NeurIPS 2011
Nearest Neighbor based Greedy Coordinate Descent
NeurIPS 2011
On Learning Discrete Graphical Models using Greedy Methods
NeurIPS 2011
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
NeurIPS 2011
A Dirty Model for Multi-task Learning
NeurIPS 2010
A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers
NeurIPS 2009
Information-theoretic lower bounds on the oracle complexity of convex optimization
NeurIPS 2009
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE
NeurIPS 2008
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images
NeurIPS 2008
SpAM: Sparse Additive Models
NeurIPS 2007
High-Dimensional Graphical Model Selection Using β_1-Regularized Logistic Regression
NeurIPS 2006