Publications

 

» An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis.
Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Ravikumar.
In Causal Learning and Reasoning (CLEAR) 3, 2024 (Oral).

» Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression.
Runtian Zhai, Bingbin Liu, Andrej Risteski, J Zico Kolter, Pradeep Ravikumar.
In International Conference on Learning Representations (ICLR) 12, 2024 (Spotlight).

» Spectrally Transformed Kernel Regression.
Runtian Zhai, Rattana Pukdee, Roger Jin, Maria Florina Balcan, Pradeep Ravikumar.
In International Conference on Learning Representations (ICLR) 12, 2024 (Spotlight).

» Identifying Representations for Intervention Extrapolation.
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister, Jonas Peters.
In International Conference on Learning Representations (ICLR) 12, 2024.

» Neuro-Causal Models.
B. Aragam, P. Ravikumar.
Book Chapter in Compendium of Neurosymbolic Artificial Intelligence, Netherlands, IOS Press, 2023.

» Learning Linear Causal Representations from Interventions under General Nonlinear Mixing.
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 36, 2023 (Oral).

» Global Optimality in Bivariate Gradient-based DAG Learning.
Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 36, 2023.

» iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models.
Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 36, 2023.

» Learning with Explanation Constraints.
Rattana Pukdee, Dylan Sam, Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 36, 2023.

» Sample based Explanations via Generalized Representers.
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 36, 2023.

» Responsible AI (RAI) Games and Ensembles.
Yash Gupta, Runtian Zhai, Arun Suggala, Pradeep Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 36, 2023.

» Individual Fairness Guarantee in Learning with Censorship.
Wenbin Zhang, Juyong Kim, Zichong Wang, Pradeep Ravikumar, Jeremy Weiss.
In European Conference on Artificial Intelligence 26, 2023.

» Faith-Shap: The Faithful Shapley Interaction Index.
C.-P. Tsai, C.-K. Yeh, P. Ravikumar.
Journal of Machine Learning Research (JMLR), Vol. 24 (94), pages 1-42, 2023.

» Optimizing NOTEARS objectives via topological swaps.
C. Deng, K. Bello, B. Aragam, P. Ravikumar
In International Conference on Machine Learning (ICML) 39, 2023.

» Representer Point Selection for Explaining Regularized High-dimensional Models.
C.-P. Tsai, J. Zhang, H.-F. Yu, E. Chien, C.-J. Hsieh, P. Ravikumar
In International Conference on Machine Learning (ICML) 39, 2023.

» Understanding Why Generalized Reweighting Does Not Improve Over ERM.
R. Zhai, C. Dan, Z. Kolter, P. Ravikumar.
In International Conference on Learning Representations (ICLR) 11, 2023.

» Label Propagation with Weak Supervision.
R. Pukdee, D. Sam, M.-F. Balcan, P. Ravikumar.
In International Conference on Learning Representations (ICLR) 11, 2023.

» Concept Gradient: Concept-based Interpretation Without Linear Assumption.
A. Bai, C.-K. Yeh, P. Ravikumar, N. Lin, C.-J. Hsieh.
In International Conference on Learning Representations (ICLR) 11, 2023.

» Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games.
M.-F. Balcan, R. Pukdee, P. Ravikumar, H. Zhang.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 26, 2023.

» Fundamental Limits and Tradeoffs in Invariant Representation Learning.
H. Zhao, C. Dan, B. Aragam, T. S. Jaakkola, G. J. Gordon, P. Ravikumar.
Journal of Machine Learning Research (JMLR), Vol. 23 (340), pages 1-49, 2022.

» DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization.
K. Bello, B. Aragam, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 35, 2022.

» Identifiability of deep generative models without auxiliary information.
B. Kivva, G. Rajendran, P. Ravikumar, B. Aragam.
In Advances in Neural Information Processing Systems (NeurIPS) 35, 2022.

» First is Better Than Last for Language Data Influence.
C.-K. Yeh, A. Taly, M. Sundararajan, F. Liu, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 35, 2022.

» Masked Prediction: A Parameter Identifiability View.
B. Liu, D. Hsu, P. Ravikumar, A. Risteski.
In Advances in Neural Information Processing Systems (NeurIPS) 35, 2022.

» AnEMIC: A Framework for Benchmarking ICD Coding Models.
J. Kim, A. Sharma, S. Shanbhogue, J. Weiss, P. Ravikumar.
In Conference on Empirical Methods in Natural Language Processing (EMNLP, System Demonstrations) 2022.

» Building Robust Ensembles via Margin Boosting.
D. Zhang, H. Zhang, A. Courville, Y. Bengio, P. Ravikumar, A. S. Suggala.
In International Conference on Machine Learning (ICML) 38, 2022.

» Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence.
J. Kim, J. Weiss, P. Ravikumar.
In Conference on Health, Inference, and Learning (CHIL), 2022.

» Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation.
B. Liu, E. Rosenfeld, P. Ravikumar, A. Risteski.
In International Conference on Learning Representations (ICLR) 10, 2022 (Spotlight).

» FILM: Following Instructions in Language with Modular Methods.
S. Y. Min, D. S. Chaplot, P. Ravikumar, Y. Bisk, R. Salakhutdinov.
In International Conference on Learning Representations (ICLR) 10, 2022.

» Heavy-tailed Streaming Statistical Estimation.
C.-P. Tsai, A. Prasad, S. Balakrishnan, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 25, 2022 (Oral).

» An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization.
E. Rosenfeld, P. Ravikumar, A. Risteski.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 25, 2022.

» Iterative Alignment Flows.
Z. Zhou, Z. Gong, P. Ravikumar, D. Inouye.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 25, 2022.

» Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations.
C.-K. Yeh, K.-Y. Lee, F. Liu, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 25, 2022.

» Human-Centered Concept Explanations for Neural Networks.
C.-K. Yeh, B. Kim, P. Ravikumar.
Book Chapter in Neuro-Symbolic Artificial Intelligence: The State of the Art, Frontiers in Artificial Intelligence and Applications, Vol. 342, IOS Press, 2022.

» Objective Criteria For Explanations of Machine Learning Models.
C.-K. Yeh, P. Ravikumar.
Applied AI Letters, 2021.

» Learning Latent Causal Graphs Via Mixture Oracles.
B. Kivva, G. Rajendran, P. Ravikumar, B. Aragam.
In Advances in Neural Information Processing Systems (NeurIPS) 34, 2021.

» When Is Generalizable Reinforcement Learning Tractable?
D. Malik, Y. Li, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 34, 2021.

» Boosted CVaR Classification.
R. Zhai, C. Dan, A. Suggala, Z. Kolter, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 34, 2021.

» On Proximal Policy Optimization's Heavy-tailed Gradients [Appendix].
S. Garg, J. Zhanson, E. Parisotto, A. Prasad, Z. Kolter, S. Balakrishnan, Z. Lipton, R. Salakhutdinov, P. Ravikumar.
In International Conference on Machine Learning (ICML) 37, 2021.

» DORO: Distributional and Outlier Robust Optimization [Appendix].
R. Zhai, C. Dan, Z. Kolter, P. Ravikumar.
In International Conference on Machine Learning (ICML) 37, 2021.

» Improving Compositional Generalization in Classification Tasks via Structure Annotations.
J. Kim, P. Ravikumar, J. Ainslie, S. Ontanon.
In The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJNLP), 2021.

» Subseasonal Climate Prediction in the Western US using Bayesian Spatial Models [Appendix].
V. Srinivasan, J. Khim, A. Banerjee, P. Ravikumar.
In Uncertainty in Artificial Intelligence (UAI) 37, 2021.

» Efficient Bandit Convex Optimization: Beyond Linear Losses.
A. Suggala, P. Ravikumar, P. Netrapalli.
In Conference on Learning Theory (COLT), 2021.

» Contrastive learning of strong-mixing continuous-time stochastic processes.
B. Liu, P. Ravikumar, A. Risteski.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 24, 2021.

» The Risks of Invariant Risk Minimization.
E. Rosenfeld, P. Ravikumar, A. Risteski.
In International Conference on Learning Representations (ICLR) 9, 2021.

» Evaluations and Methods for Explanation through Robustness Analysis.
C.-Y. Hsieh, C.-K. Yeh, X. Liu, P. Ravikumar, S. Kim, S. Kumar, C.-J. Hsieh.
In International Conference on Learning Representations (ICLR) 9, 2021.

» Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances.
S. He, X. Li, T. DelSole, P. Ravikumar, A. Banerjee.
In AAAI Conference on Artificial Intelligence (AAAI) 35, 2021.

» Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches.
J. Kim, L. Gong, J. Khim, J. Weiss, P. Ravikumar.
In Machine Learning for Health (ML4H), 2020.

» Generalized Boosting [Appendix].
A. Suggala, B. Liu, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 33, 2020.

» On Learning Ising Models under Huber's Contamination Model [Appendix].
A. Prasad, V. Srinivasan, S. Balakrishnan, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 33, 2020.

» On Completeness-aware Concept-Based Explanations in Deep Neural Networks.
C.-K. Yeh, B. Kim, S. Arik, C.-L. Li, T. Pfister, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 33, 2020.

» Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification [Appendix].
C. Dan, Y. Wei, P. Ravikumar.
In International Conference on Machine Learning (ICML) 36, 2020.

» Certified Robustness to Label-Flipping Attacks via Randomized Smoothing.
E. Rosenfeld, E. Winston, P. Ravikumar, Z. Kolter.
In International Conference on Machine Learning (ICML) 36, 2020.

» Class-Weighted Classification: Trade-offs and Robust Approaches [Appendix] [Code].
Z. Xu, C. Dan, J. Khim, P. Ravikumar.
In International Conference on Machine Learning (ICML) 36, 2020.

» Uniform Convergence of Rank-weighted Learning [Appendix].
L. Leqi, J. Khim, A. Prasad, P. Ravikumar.
In International Conference on Machine Learning (ICML) 36, 2020.

» Automated Dependence Plots [Code].
D. Inouye, L. Leqi, J. S. Kim, B. Aragam, P. Ravikumar.
In Uncertainty in Artificial Intelligence (UAI), 2020 .

» Robust Estimation via Robust Gradient Estimation.
A. Prasad, A. Suggala, S. Balakrishnan, P. Ravikumar.
Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSSB), Vol. 82 (3), pages 601-627, 2020.

» A Robust Univariate Mean Estimator is All You Need [Appendix].
A. Prasad, S. Balakrishnan, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 23, 2020.

» Learning Sparse Nonparametric DAGs [Appendix].
X. Zheng, C. Dan, B. Aragam, P. Ravikumar, E. Xing.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 23, 2020.

» MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius [Code].
R. Zhai, C. Dan, D. He, H. Zhang, B. Gong, P. Ravikumar, C.-J. Hsieh, L. Wang.
In International Conference on Learning Representations (ICLR) 8, 2020.

» Minimizing FLOPs to Learn Efficient Sparse Representations [Code].
B. Paria, C.-K. Yeh, I. En-Hsu Yen, N. Xu, P. Ravikumar, B. Poczos.
In International Conference on Learning Representations (ICLR) 8, 2020.

» Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering.
B. Aragam, C. Dan, E. Xing, P. Ravikumar.
Annals of Statistics, Vol. 48, No. 4, pages 2277-2302, 2020.

» Building Human-Machine Trust via Interpretability.
U. Bhatt, P. Ravikumar, J. M. F. Moura
In Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9919-9920, 2019.

» On the (in)fidelity and sensitivity of explanations.
C.-K. Yeh, C.-Y. Hsieh, A. Suggala, D. Inouye, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 32, 2019.

» Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation [Appendix].
C. Dan, H. Wang, H. Zhang, Y. Zhou, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 32, 2019.

» On Human-Aligned Risk Minimization [Appendix].
L. Leqi, A. Prasad, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 32, 2019.

» Game Design for Eliciting Distinguishable Behavior.
F. Yang, L. Leqi, Y. Wu, Z. Lipton, P. Ravikumar, T. Mitchell, W. Cohen.
In Advances in Neural Information Processing Systems (NeurIPS) 32, 2019.

» Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression.
A. Suggala, K. Bhatia, P. Ravikumar, P. Jain.
In Conference on Learning Theory (COLT) 32, 2019.

» Revisiting Adversarial Risk.
A. Suggala, A. Prasad, V. Nagarajan, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019.

» Representer Point Selection for Explaining Deep Neural Networks [Appendix].
C.-K. Yeh, J. Sik Kim, I. En-Hsu Yen, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.

» DAGs with NO TEARS: Continuous Optimization for Structure Learning [Appendix].
X. Zheng, B. Aragam, P. Ravikumar, E. Xing.
In Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.

» Connecting Optimization and Regularization Paths [Appendix].
A. Suggala, A. Prasad, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.

» Sample Complexity of Nonparametric Semi-Supervised Learning [Appendix].
C. Dan, L. Leqi, B. Aragam, P. Ravikumar, E. Xing.
In Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.

» MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization [Appendix].
I. En-Hsu Yen, W.-C. Lee, K. Zhong, S.-E. Chang, P. Ravikumar, S.-D. Lin.
In Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.

» Word Mover's Embedding: From Word2Vec to Document Embedding.
L. Wu, I. En-Hsu Yen, K. Xu, F. Xu, A. Balakrishnan, P.-Yu Chen, P. Ravikumar and M. J. Witbrock.
In Empirical Methods in Natural Language Processing (EMNLP) 2018.

» Deep Density Destructors [Appendix].
D. Inouye, P. Ravikumar.
In International Conference on Machine Learning (ICML) 35, 2018.

» Binary Classification with Karmic, Threshold-Quasi-Concave Metrics [Appendix].
B. Yan, S. Koyejo, K. Zhong, P. Ravikumar.
In International Conference on Machine Learning (ICML) 35, 2018.

» Loss Decomposition for Fast Learning in Large Output Spaces.
I. En-Hsu Yen, S. Kale, F. Yu, D. Holtmann-Rice, S. Kumar, P. Ravikumar.
In International Conference on Machine Learning (ICML) 35, 2018.

» Cost-Sensitive Learning with Noisy Labels.
N. Natarajan, I. Dhillon, P. Ravikumar, A. Tewari.
Journal of Machine Learning Research (JMLR), Vol. 18, pages 1-33, 2018.

» A Voting-Based System for Ethical Decision Making.
R. Noothigattu, S. S. Gaikwad, E. Awad, S. Dsouza, I. Rahwan, P. Ravikumar, A. D. Procaccia.
In AAAI Conference on Artificial Intelligence (AAAI) 32, 2018.

» The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities [Appendix].
A. Suggala, M. Kolar, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 30, 2017.

» On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models [Appendix].
A. Prasad, A. Niculescu-Mizil, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 30, 2017 (Spotlight).

» Ordinal Graphical Models: A Tale of Two Approaches [Appendix].
A. S. Suggala, E. Yang, P. Ravikumar.
In International Conference on Machine Learning (ICML) 34, 2017.

» Latent Feature Lasso [Appendix].
I. En-Hsu Yen, W.-C. Li, S.-E. Chang, A. S. Suggala, S.-D. Lin, P. Ravikumar.
In International Conference on Machine Learning (ICML) 34, 2017.

» Doubly Greedy Primal-Dual Coordinate Methods for Sparse Empirical Risk Minimization [Appendix].
Q. Lei, I. En-Hsu Yen, C.-Y. Wu, I. Dhillon, P. Ravikumar.
In International Conference on Machine Learning (ICML) 34, 2017.

» PPDSparse: A Parallel Primal-Dual Sparse Method for Extreme Classification.
I. En-Hsu Yen, X. Huang, W. Dai, P. Ravikumar, I. S. Dhillon and E. P. Xing.
In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 23, 2017 (Oral).

» A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.
D. Inouye, E. Yang, G. Allen, P. Ravikumar.
Wiley Interdisciplinary Reviews (WIREs): Computational Statistics, Vol. 9, No. 3, 2017.

» Shape based image reconstruction using linearized deformations.
O. Oktem, C. Chen, N. O. Domanic, P. Ravikumar, C. Bajaj.
Inverse Problems, Vol. 33, No. 3, 2017.

» Minimax Gaussian Classification & Clustering [Appendix].
T. Li, X. Yi, C. Caramanis, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 20, 2017.

» Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition [Appendix].
J. Zhang, I. En-Hsu Yen, P. Ravikumar, I. Dhillon.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 20, 2017.

» Greedy Direction Method of Multipliers for MAP Inference with Large Output Domain [Appendix].
X. Huang, I. En-Hsu Yen, R. Zhang, Q. Huang, P. Ravikumar, I. Dhillon.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 20, 2017.

» XMRF: an R package to fit Markov Networks to high-throughput genetics data.
Y.-W. Wan and G. I. Allen and Y. Baker and E. Yang and P. Ravikumar and M. Anderson and Z. Li.
BMC Systems Biology, Vol. 10, No. 3, Pages 69, 2016.

» Dual Decomposed Learning with Factorwise Oracle for Structural SVMs with Large Output Domain [Appendix].
I. En-Hsu Yen, X. Huang, K. Zhong, R. Zhang, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 29, 2016.

» Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies [Appendix] .
D. Inouye, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML) 33, 2016.

» Optimal Classification with Multivariate Losses [Appendix] .
N. Natarajan, O. Koyejo, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML) 33, 2016.

» A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery [Appendix] .
I. En-Hsu Yen, X. Lin, J. Zhang, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML) 33, 2016.

» PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification [Appendix] .
I. En-Hsu Yen, X. Huang, P. Ravikumar, K. Zhong, I. Dhillon.
In International Conference on Machine Learning (ICML) 33, 2016.

» Closed-form Estimators for High-dimensional Generalized Linear Models [Appendix]
E. Yang, A. Lozano, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 28, 2015.

» Fast Classification Rates for High-dimensional Gaussian Generative Models.
T. Li, A. Prasad, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 28, 2015.

» Consistent Multilabel Classification [Appendix].
S. Koyejo, N. Natarajan, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 28, 2015.

» Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial [Appendix].
D. Inouye, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 28, 2015.

» Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent [Appendix].
I. En-Hsu Yen, K. Zhong, C.-J. Hsieh, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 28, 2015.

» Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs.
V. Sivakumar, A. Banerjee, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 28, 2015.

» Collaborative Filtering with Graph Information: Consistency and Scalable Methods [Appendix].
N. Rao, H.-F. Yu, I. Dhillon, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 28, 2015.

» Graphical Models via Univariate Exponential Family Distributions.
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
Journal of Machine Learning Research (JMLR), Vol. 16, pages 3813-3847, 2015.

» Learning-based Analytical Cross-Platform Performance Prediction.
X. Zheng, P. Ravikumar, L. K. John, A. Gerstlauer.
In International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, 2015.
[Stamatis Vassiliadis Best Paper Award].

» Tracking with Ranked Signals [Appendix].
T. Li, H. Pareek, P. Ravikumar, D. Balwada, K. Speer.
In Uncertainty in Artificial Intelligence (UAI) 31, 2015 (Oral).

» Distributional Rank Aggregration, and an Axiomatic Analysis [Appendix].
A. Prasad, H. Pareek, P. Ravikumar.
In International Conference on Machine Learning (ICML) 32, 2015.

» Vector-Space Markov Random Fields via Exponential Families [Appendix].
W. Tansey, O. Padilla, A. Suggala, P. Ravikumar.
In International Conference on Machine Learning (ICML) 32, 2015.

» A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models [Appendix] .
I. En-Hsu Yen, X. Lin, K. Zhong, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML) 32, 2015.

» Sparsistency of l1-Regularized M-Estimators [Appendix].
Y.-H. Li, J. Scarlett, P. Ravikumar, V. Cevher.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 18, 2015 (Oral).

» Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli.
V. Sridhara, A. G. Meyer, P. Rai, J. E. Barrick, P. Ravikumar, D. Segre, C. O. Wilke.
PLoS ONE 9(12): e114608, December 2014.

» A Representation Theory for Ranking Functions [Appendix].
H. Pareek, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» Elementary Estimators for Graphical Models [Appendix].
E. Yang, A. Lozano, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» On the Information Theoretic Limits of Learning Ising Models [Appendix].
R. Tandon, K. Shanmugam, P. Ravikumar, A. Dimakis.
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings [Appendix].
I. En-Hsu Yen, C.-J. Hsieh, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» Sparse Random Feature Algorithms as Coordinate Descent in Hilbert Space [Appendix].
I. En-Hsu Yen, T.-W. Lin, S.-D. Lin, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» Consistent Binary Classification with Generalized Performance Metrics [Appendix].
N. Natarajan, S. Koyejo, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs [Appendix].
D. Inouye, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models [Appendix].
C.-J. Hsieh, I. Dhillon, P. Ravikumar, S. Becker, P. Olsen .
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» Proximal Quasi-Newton for Computationally Intensive l1-regularized M-estimators [Appendix].
K. Zhong, I. En-Hsu Yen, I. Dhillon, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 27, 2014.

» QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation.
C.-J. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
Journal of Machine Learning Research (JMLR), Vol. 15, Pages 2911-2947, 2014.

» Elementary Estimators for High-Dimensional Linear Regression.
E. Yang, A. Lozano, P. Ravikumar.
In International Conference on Machine Learning (ICML) 31, 2014.

» Elementary Estimators for Sparse Covariance Matrices and other Structured Moments.
E. Yang, A. Lozano, P. Ravikumar.
In International Conference on Machine Learning (ICML) 31, 2014.

» Exponential Family Matrix Completion under Structural Constraints [Appendix].
S. Gunasekar, P. Ravikumar, J. Ghosh.
In International Conference on Machine Learning (ICML) 31, 2014.

» Admixtures of Poisson MRFs: A Topic Model with Word Dependencies [Appendix].
D. Inouye, P. Ravikumar, I. Dhillon.
In International Conference on Machine Learning (ICML) 31, 2014.

» Learning Graphs with a Few Hubs [Appendix].
R. Tandon, P. Ravikumar.
In International Conference on Machine Learning (ICML) 31, 2014.

» Mixed Graphical Models via Exponential Families [Appendix].
E. Yang, Y. Baker, P. Ravikumar, G. Allen, Z. Liu.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 17, 2014 (Oral).

» BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables.
C.-J. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar, R. Poldrack.
In Advances in Neural Information Processing Systems (NeurIPS) 26, 2013 (Oral).

» Dirty Statistical Models.
E. Yang, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 26, 2013.

» On Poisson Graphical Models.
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Advances in Neural Information Processing Systems (NeurIPS) 26, 2013.

» Conditional Random Fields via Univariate Exponential Families.
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Advances in Neural Information Processing Systems (NeurIPS) 26, 2013.

» Learning with Noisy Labels.
N. Natarajan, I. Dhillon, P. Ravikumar, A. Tewari.
In Advances in Neural Information Processing Systems (NeurIPS) 26, 2013.

» Large Scale Distributed Sparse Precision Estimation.
H. Wang, A. Banerjee, C.-J. Hsieh, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 26, 2013.

» On the Difficulty of Learning Power Law Graphical Models.
R. Tandon, P. Ravikumar.
In IEEE International Symposium on Information Theory (ISIT), 2013.

» On Robust Estimation of High Dimensional Generalized Linear Models.
E. Yang, A. Tewari, P. Ravikumar.
In International Joint Conference on Artificial Intelligence (IJCAI) 13, 2013.

» A Dirty Model for Multiple Sparse Regression.
A. Jalali, P. Ravikumar, S. Sanghavi.
IEEE Transactions on Information Theory, Vol. 59, No. 12, pages 7947-7968, 2013.

» Human Boosting.
H. Pareek, P. Ravikumar.
In International Conference on Machine Learning (ICML) 30, 2013.

» Graphical Models via Generalized Linear Models.
E. Yang, P. Ravikumar, G. Allen, Z. Liu.
In Advances in Neural Information Processing Systems (NeurIPS) 25, 2012 (Oral).

» A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation.
C.-J. Hsieh, I. Dhillon, P. Ravikumar, A. Banerjee.
In Advances in Neural Information Processing Systems (NeurIPS) 25, 2012.

» Perturbation based Large Margin Approach for Ranking.
E. Yang, A. Tewari, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 15, 2012.

» High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods.
A. Jalali, C. Johnson, P. Ravikumar.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 15, 2012.

» A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers.
S. Negahban, P. Ravikumar, M. J. Wainwright and B. Yu.
Statistical Science, Vol. 27, No. 4, pages 538-557, 2012.

» Information-theoretic lower bounds on the oracle complexity of convex optimization.
A. Agarwal, P. Bartlett, P. Ravikumar, M. Wainwright.
IEEE Transactions on Information Theory, Vol. 58, No. 5, pages 3235-3249, 2012.

» On Learning Discrete Graphical Models using Greedy Methods.
A. Jalali, C. Johnson, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 24, 2011.

» Greedy Algorithms for Structurally Constrained High Dimensional Problems.
A. Tewari, P. Ravikumar, I. Dhillon.
In Advances in Neural Information Processing Systems (NeurIPS) 24, 2011.

» Nearest Neighbor based Greedy Coordinate Descent.
I. Dhillon, P. Ravikumar, A. Tewari.
In Advances in Neural Information Processing Systems (NeurIPS) 24, 2011.

» Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation.
C.-J. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
In Advances in Neural Information Processing Systems (NeurIPS) 24, 2011.

» High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence.
P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu.
Electronic Journal of Statistics, Vol. 5, Pages 935-980, 2011.

» On the Use of Variational Inference for Learning Discrete Graphical Models.
E. Yang and P. Ravikumar.
In International Conference on Machine learning (ICML) 28, 2011.

» Encoding and Decoding V1 fMRI Responses to Natural Images with Sparse Nonparametric Models .
V. Vu, P. Ravikumar, T. Naselaris, K. Kay, J. Gallant and B. Yu.
Annals of Applied Statistics, Vol. 5, No. 2B, pages 1159-1182, 2011.

» On NDCG Consistency of Listwise Ranking Methods.
P. Ravikumar, A. Tewari, E. Yang.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 14, 2011.

» On Learning Discrete Graphical Models using Group-Sparse Regularization.
A. Jalali, P. Ravikumar, V. Vasuki, S. Sanghavi.
In International Conference on Artificial Intelligence and Statistics (AISTATS) 14, 2011.

» A Dirty Model for Multi-task Learning[Appendix].
A. Jalali, P. Ravikumar, S. Sanghavi, C. Ruan.
In Advances in Neural Information Processing Systems (NeurIPS) 23, 2010 (Oral).

» Information-theoretic lower bounds on the oracle complexity of sparse convex optimization.
A. Agarwal, P. Bartlett, P. Ravikumar, M. Wainwright.
In International Workshop on Optimization for Machine Learning (OPT) 3, 2010.

» Message-passing for graph-structured linear programs: proximal methods and rounding schemes.
P. Ravikumar, A. Agarwal, and M. J. Wainwright.
Journal of Machine Learning Research (JMLR), Vol. 11, Pages 1043-1080, March 2010.

» High-dimensional Ising model selection using l1-regularized logistic regression.
P. Ravikumar, M. J. Wainwright and J. Lafferty.
Annals of Statistics, Vol. 38, Number 3, Pages 1287-1319, 2010.

» A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers.
S. Negahban, P. Ravikumar, M. J. Wainwright and B. Yu.
In Advances in Neural Information Processing Systems (NeurIPS) 23, 2009 (Oral).

» Information-theoretic lower bounds on the oracle complexity of convex optimization.
A. Agarwal, P. Bartlett, P. Ravikumar, M. Wainwright.
In Advances in Neural Information Processing Systems (NeurIPS) 22, 2009.

» Error-correcting tournaments.
Alina Beygelzimer, John Langford, and Pradeep Ravikumar.
In International Conference on Algorithmic Learning Theory (ALT) 20, 2009.

Earlier Version: » Multiclass classification with filter-trees.
Alina Beygelzimer, John Langford, and Pradeep Ravikumar.

» Sparse Additive Models.
P. Ravikumar, J. Lafferty, H. Liu and L. Wasserman.
Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSSB), Vol. 71(5), pages 1009-1030, 2009.

Earlier Version: » SpAM: Sparse Additive Models.
In Advances in Neural Information Processing Systems (NeurIPS) 20, pages 1201-1208, 2008.

» Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes.
In International Conference on Machine learning (ICML) 25, pages 800-807, 2008.

» Model selection in Gaussian graphical models: High-dimensional consistency of l1-regularized MLE.
P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu.
In Advances in Neural Information Processing Systems (NeurIPS) 21, 2008.

» Nonparametric sparse hierarchical models describe V1 fmri responses to natural images.
P. Ravikumar, V. Vu, B. Yu, T. Naselaris, K. Kay, and J. Gallant.
In Advances in Neural Information Processing Systems (NeurIPS) 21, 2008.

» Single Index Convex Experts: Efficient Estimation via Adapted Bregman Losses.
P. Ravikumar, M. J. Wainwright and B. Yu.
Presented at the Learning Workshop, Snowbird 2008.

» Approximate inference, structure learning and feature estimation in Markov random fields.
P. Ravikumar.
Technical Report CMU-ML-07-115, Ph.D. Thesis, Carnegie Mellon University, 2007.
ACM SIGKDD Explorations, Vol. 10(2), pages 32-33, December 2008.

» High-Dimensional Graphical Model Selection Using l1-Regularized Logistic Regression.
M. J. Wainwright, P. Ravikumar and J. Lafferty.
In Advances in Neural Information Processing Systems (NeurIPS) 19, pages 1465-1472, 2007.

» Quadratic programming relaxations for metric labeling and Markov random field map estimation.
P. Ravikumar and J. Lafferty.
In International Conference on Machine learning (ICML) 23, pages 737-744, 2006.

» Preconditioner approximations for probabilistic graphical models.
P. Ravikumar and J. Lafferty.
In Advances in Neural Information Processing Systems (NeurIPS) 18, pages 1113-1120, 2006 (Oral)

» Variational Chernoff bounds for graphical models.
P. Ravikumar and J. Lafferty.
In Uncertainty in Artificial Intelligence (UAI) 20, pages 462-469, 2004.

» A Hierarchical Graphical Model for Record Linkage.
P. Ravikumar and W. W. Cohen.
In Uncertainty in Artificial Intelligence (UAI) 20, pages 454-461, 2004.

» Comments: The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers.
W. W. Cohen, S. Fienberg, P. Ravikumar.
Journal of Business and Economic Statistics, Vol. 23(2), pages 160-162, 2005.

» A Secure Protocol for Computing String Distance Metrics.
P. Ravikumar, W. W. Cohen, S. E. Fienberg.
In IEEE International Conference on Data Mining (ICDM) 04, Workshop on Privacy and Security Aspects of Data Mining, 2004.

» A Comparison of String Metrics for Matching Names and Records.
W. W. Cohen, P. Ravikumar, S. Fienberg.
In ACM International Conference on Knowledge Discovery and Data Mining (KDD) 09, Workshop on Data Cleaning, Record Linkage, and Object Consolidation, 2003.

Earlier Version: » A Comparison of String Distance Metrics for Name-Matching Tasks.
In International Joint Conference on Artificial Intelligence (IJCAI) 18, Workshop on Information Integration on the Web, 2003.

» Adaptive Name Matching in Information Integration.
M. Bilenko, R. Mooney, W. W. Cohen, P. Ravikumar, S. Fienberg.
IEEE Intelligent Systems, Vol. 18(5), pages 16-23, 2003.