Pradeep K Ravikumar

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 Game Design for Eliciting Distinguishable Behavior NeurIPS 2019 On Human-Aligned Risk Minimization NeurIPS 2019 On the (In)fidelity and Sensitivity of Explanations NeurIPS 2019 Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation NeurIPS 2019 Connecting Optimization and Regularization Paths NeurIPS 2018 DAGs with NO TEARS: Continuous Optimization for Structure Learning NeurIPS 2018 MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization NeurIPS 2018 Representer Point Selection for Explaining Deep Neural Networks NeurIPS 2018 The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models NeurIPS 2018 On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models NeurIPS 2017 The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities NeurIPS 2017 Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain NeurIPS 2016 Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs NeurIPS 2015 Closed-form Estimators for High-dimensional Generalized Linear Models NeurIPS 2015 Collaborative Filtering with Graph Information: Consistency and Scalable Methods NeurIPS 2015 Consistent Multilabel Classification NeurIPS 2015 Fast Classification Rates for High-dimensional Gaussian Generative Models NeurIPS 2015 Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial NeurIPS 2015 Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent NeurIPS 2015 A Representation Theory for Ranking Functions NeurIPS 2014 Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs NeurIPS 2014 Consistent Binary Classification with Generalized Performance Metrics NeurIPS 2014 Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings NeurIPS 2014 Elementary Estimators for Graphical Models NeurIPS 2014 On the Information Theoretic Limits of Learning Ising Models NeurIPS 2014 Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators NeurIPS 2014 QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models NeurIPS 2014 Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space NeurIPS 2014 BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NeurIPS 2013 Conditional Random Fields via Univariate Exponential Families NeurIPS 2013 Dirty Statistical Models NeurIPS 2013 Large Scale Distributed Sparse Precision Estimation NeurIPS 2013 Learning with Noisy Labels NeurIPS 2013 On Poisson Graphical Models NeurIPS 2013 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 ℓ_1-Regularized Logistic Regression NeurIPS 2006