← Authors

Pradeep K Ravikumar

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

Achievements

πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (28) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (17) 🌈 Renaissance Researcher (6) 🐝 Cross-Pollinator (13) 🏠 Conference Loyalist (63) 🌟 Keyword Trendsetter Combo (7) πŸ† Keyword Champion (2) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (11) 🀝 Dynamic Duo (29) πŸ—ƒοΈ Keyword Collector (136) πŸ’Ž Century Club (63) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (18) ⚑ Prolific Year (7) ❓ The Questioner

Conferences

NIPS (63)

Top co-authors

Pradeep K. Ravikumar (29) Inderjit S Dhillon (18) Bryon Aragam (8) Ian En-Hsu Yen (7) Cho-jui Hsieh (7) Arun Suggala (6) Eunho Yang (6) Adarsh Prasad (5) Chih-Kuan Yeh (5) Inderjit S. Dhillon (4)

Keywords

graphical model (16) sparse optimization (8) convex optimization (7) high-dimensional estimation (6) high-dimensional statistics (6) structure learning (5) sparse estimation (4) graph structure (4) exponential family (4) markov random field (4) l1 regularization (4) high-dimensional optimization (4) sparse learning (4) maximum likelihood estimation (3) sparse inverse covariance (3) exponential families (3) sparse regression (3) statistical learning (3) structured prediction (3) binary classification (3)

Papers

Global Optimality in Bivariate Gradient-based DAG Learning NIPS 2023
Learning with Explanation Constraints NIPS 2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing NIPS 2023
Responsible AI (RAI) Games and Ensembles NIPS 2023
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models NIPS 2023
Sample based Explanations via Generalized Representers NIPS 2023
Identifiability of deep generative models without auxiliary information NIPS 2022
First is Better Than Last for Language Data Influence NIPS 2022
Masked Prediction: A Parameter Identifiability View NIPS 2022
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization NIPS 2022
Learning latent causal graphs via mixture oracles NIPS 2021
Boosted CVaR Classification NIPS 2021
When Is Generalizable Reinforcement Learning Tractable? NIPS 2021
On Completeness-aware Concept-Based Explanations in Deep Neural Networks NIPS 2020
On Learning Ising Models under Huber's Contamination Model NIPS 2020
Generalized Boosting NIPS 2020
On Human-Aligned Risk Minimization NIPS 2019
Game Design for Eliciting Distinguishable Behavior NIPS 2019
On the (In)fidelity and Sensitivity of Explanations NIPS 2019
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation NIPS 2019
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization NIPS 2018
Connecting Optimization and Regularization Paths NIPS 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning NIPS 2018
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models NIPS 2018
Representer Point Selection for Explaining Deep Neural Networks NIPS 2018
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities NIPS 2017
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models NIPS 2017
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain NIPS 2016
Consistent Multilabel Classification NIPS 2015
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent NIPS 2015
Closed-form Estimators for High-dimensional Generalized Linear Models NIPS 2015
Fast Classification Rates for High-dimensional Gaussian Generative Models NIPS 2015
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial NIPS 2015
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs NIPS 2015
Collaborative Filtering with Graph Information: Consistency and Scalable Methods NIPS 2015
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs NIPS 2014
On the Information Theoretic Limits of Learning Ising Models NIPS 2014
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings NIPS 2014
A Representation Theory for Ranking Functions NIPS 2014
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators NIPS 2014
Consistent Binary Classification with Generalized Performance Metrics NIPS 2014
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space NIPS 2014
Elementary Estimators for Graphical Models NIPS 2014
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models NIPS 2014
Conditional Random Fields via Univariate Exponential Families NIPS 2013
Dirty Statistical Models NIPS 2013
Large Scale Distributed Sparse Precision Estimation NIPS 2013
On Poisson Graphical Models NIPS 2013
Learning with Noisy Labels NIPS 2013
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NIPS 2013
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation NIPS 2012
Graphical Models via Generalized Linear Models NIPS 2012
Nearest Neighbor based Greedy Coordinate Descent NIPS 2011
Greedy Algorithms for Structurally Constrained High Dimensional Problems 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