Pradeep K Ravikumar

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