ML Anthology

Ravikumar, Pradeep K.

64 publications

NeurIPS 2023 Global Optimality in Bivariate Gradient-Based DAG Learning Chang Deng, Kevin Bello, Pradeep K. Ravikumar, Bryon Aragam
NeurIPS 2023 Learning Linear Causal Representations from Interventions Under General Nonlinear Mixing Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep K. Ravikumar
NeurIPS 2023 Learning with Explanation Constraints Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina F Balcan, Pradeep K. Ravikumar
NeurIPS 2023 Responsible AI (RAI) Games and Ensembles Yash Gupta, Runtian Zhai, Arun Suggala, Pradeep K. Ravikumar
NeurIPS 2023 Sample Based Explanations via Generalized Representers Che-Ping Tsai, Chih-Kuan Yeh, Pradeep K. Ravikumar
NeurIPS 2023 iSCAN: Identifying Causal Mechanism Shifts Among Nonlinear Additive Noise Models Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep K. Ravikumar
NeurIPS 2022 DAGMA: Learning DAGs via M-Matrices and a Log-Determinant Acyclicity Characterization Kevin Bello, Bryon Aragam, Pradeep K. Ravikumar
NeurIPS 2022 First Is Better than Last for Language Data Influence Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep K. Ravikumar
NeurIPS 2022 Identifiability of Deep Generative Models Without Auxiliary Information Bohdan Kivva, Goutham Rajendran, Pradeep K. Ravikumar, Bryon Aragam
NeurIPS 2022 Masked Prediction: A Parameter Identifiability View Bingbin Liu, Daniel J. Hsu, Pradeep K. Ravikumar, Andrej Risteski
NeurIPS 2021 Boosted CVaR Classification Runtian Zhai, Chen Dan, Arun Suggala, J. Zico Kolter, Pradeep K. Ravikumar
NeurIPS 2021 Learning Latent Causal Graphs via Mixture Oracles Bohdan Kivva, Goutham Rajendran, Pradeep K. Ravikumar, Bryon Aragam
NeurIPS 2021 When Is Generalizable Reinforcement Learning Tractable? Dhruv Malik, Yuanzhi Li, Pradeep K. Ravikumar
NeurIPS 2020 Generalized Boosting Arun Suggala, Bingbin Liu, Pradeep K. Ravikumar
NeurIPS 2020 On Completeness-Aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, Pradeep K. Ravikumar
NeurIPS 2020 On Learning Ising Models Under Huber's Contamination Model Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep K. Ravikumar
NeurIPS 2019 Game Design for Eliciting Distinguishable Behavior Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep K Ravikumar, Tom M. Mitchell, William W. Cohen
NeurIPS 2019 On Human-Aligned Risk Minimization Liu Leqi, Adarsh Prasad, Pradeep K Ravikumar
NeurIPS 2019 On the (In)fidelity and Sensitivity of Explanations Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, Pradeep K Ravikumar
NeurIPS 2019 Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep K Ravikumar
NeurIPS 2018 Connecting Optimization and Regularization Paths Arun Suggala, Adarsh Prasad, Pradeep K Ravikumar
NeurIPS 2018 DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, Eric P Xing
NeurIPS 2018 MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep K Ravikumar, Shou-De Lin
NeurIPS 2018 Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, Pradeep K Ravikumar
NeurIPS 2018 The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models Chen Dan, Liu Leqi, Bryon Aragam, Pradeep K Ravikumar, Eric P Xing
NeurIPS 2017 On Separability of Loss Functions, and Revisiting Discriminative vs Generative Models Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep K Ravikumar
NeurIPS 2017 The Expxorcist: Nonparametric Graphical Models via Conditional Exponential Densities Arun Suggala, Mladen Kolar, Pradeep K Ravikumar
NeurIPS 2016 Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2015 Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs Vidyashankar Sivakumar, Arindam Banerjee, Pradeep K Ravikumar
NeurIPS 2015 Closed-Form Estimators for High-Dimensional Generalized Linear Models Eunho Yang, Aurelie C. Lozano, Pradeep K Ravikumar
NeurIPS 2015 Collaborative Filtering with Graph Information: Consistency and Scalable Methods Nikhil Rao, Hsiang-Fu Yu, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2015 Consistent Multilabel Classification Oluwasanmi O Koyejo, Nagarajan Natarajan, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2015 Fast Classification Rates for High-Dimensional Gaussian Generative Models Tianyang Li, Adarsh Prasad, Pradeep K Ravikumar
NeurIPS 2015 Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial David I Inouye, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2015 Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2014 A Representation Theory for Ranking Functions Harsh H Pareek, Pradeep K Ravikumar
NeurIPS 2014 Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs David I Inouye, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2014 Consistent Binary Classification with Generalized Performance Metrics Oluwasanmi O Koyejo, Nagarajan Natarajan, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2014 Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods Under High-Dimensional Settings Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2014 Elementary Estimators for Graphical Models Eunho Yang, Aurelie C. Lozano, Pradeep K Ravikumar
NeurIPS 2014 On the Information Theoretic Limits of Learning Ising Models Rashish Tandon, Karthikeyan Shanmugam, Pradeep K Ravikumar, Alexandros G Dimakis
NeurIPS 2014 Proximal Quasi-Newton for Computationally Intensive L1-Regularized M-Estimators Kai Zhong, Ian En-Hsu Yen, Inderjit S Dhillon, Pradeep K Ravikumar
NeurIPS 2014 QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models Cho-Jui Hsieh, Inderjit S Dhillon, Pradeep K Ravikumar, Stephen Becker, Peder A. Olsen
NeurIPS 2014 Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2013 BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, Matyas A Sustik, Inderjit S Dhillon, Pradeep K Ravikumar, Russell Poldrack
NeurIPS 2013 Conditional Random Fields via Univariate Exponential Families Eunho Yang, Pradeep K Ravikumar, Genevera I Allen, Zhandong Liu
NeurIPS 2013 Dirty Statistical Models Eunho Yang, Pradeep K Ravikumar
NeurIPS 2013 Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon
NeurIPS 2013 Learning with Noisy Labels Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, Ambuj Tewari
NeurIPS 2013 On Poisson Graphical Models Eunho Yang, Pradeep K Ravikumar, Genevera I Allen, Zhandong Liu
NeurIPS 2012 A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation Cho-jui Hsieh, Arindam Banerjee, Inderjit S. Dhillon, Pradeep K. Ravikumar
NeurIPS 2012 Graphical Models via Generalized Linear Models Eunho Yang, Genevera Allen, Zhandong Liu, Pradeep K. Ravikumar
NeurIPS 2011 Greedy Algorithms for Structurally Constrained High Dimensional Problems Ambuj Tewari, Pradeep K. Ravikumar, Inderjit S. Dhillon
NeurIPS 2011 Nearest Neighbor Based Greedy Coordinate Descent Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
NeurIPS 2011 On Learning Discrete Graphical Models Using Greedy Methods Ali Jalali, Christopher C. Johnson, Pradeep K. Ravikumar
NeurIPS 2011 Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation Cho-jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Mátyás A. Sustik
NeurIPS 2010 A Dirty Model for Multi-Task Learning Ali Jalali, Sujay Sanghavi, Chao Ruan, Pradeep K. Ravikumar
NeurIPS 2009 A Unified Framework for High-Dimensional Analysis of $m$-Estimators with Decomposable Regularizers Sahand Negahban, Bin Yu, Martin J. Wainwright, Pradeep K. Ravikumar
NeurIPS 2009 Information-Theoretic Lower Bounds on the Oracle Complexity of Convex Optimization Alekh Agarwal, Martin J. Wainwright, Peter L. Bartlett, Pradeep K. Ravikumar
NeurIPS 2008 Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-Regularized MLE Garvesh Raskutti, Bin Yu, Martin J. Wainwright, Pradeep K. Ravikumar
NeurIPS 2008 Nonparametric Sparse Hierarchical Models Describe V1 fMRI Responses to Natural Images Vincent Q. Vu, Bin Yu, Thomas Naselaris, Kendrick Kay, Jack Gallant, Pradeep K. Ravikumar
NeurIPS 2007 SpAM: Sparse Additive Models Han Liu, Larry Wasserman, John D. Lafferty, Pradeep K. Ravikumar
NeurIPS 2006 High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression Martin J. Wainwright, John D. Lafferty, Pradeep K. Ravikumar
NeurIPS 2005 Preconditioner Approximations for Probabilistic Graphical Models John D. Lafferty, Pradeep K. Ravikumar