Pradeep Ravikumar

247 papers A* 68A 24B 2Journal 102Unranked 48

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	What Does Preference Learning Recover from Pairwise Comparison Data? CoRR Rattana Pukdee, Maria-Florina Balcan, Pradeep Ravikumar
2025	J	`jnl`	Contextures: Representations from Contexts. CoRR Runtian Zhai, Kai Yang, Che-Ping Tsai, Burak Varici, Zico Kolter, Pradeep Ravikumar
2025	J	`jnl`	Eigenfunction Extraction for Ordered Representation Learning. CoRR Burak Varici, Che-Ping Tsai, Ritabrata Ray, Nicholas M. Boffi, Pradeep Ravikumar
2025	—	`ed.`	International Conference on Neuro-symbolic Systems, 28-30 May 2025, University of Pennsylvania, Philadelphia, Pennsylvania, USA. NeuS George J. Pappas, Pradeep Ravikumar, Sanjit A. Seshia
2025	—	`conf`	Knowledge-Enriched Machine Learning for Tabular Data. NeuS Juyong Kim, Chandler Squires, Pradeep Ravikumar
2024	A*	`conf`	Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. NeurIPS Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2024	J	`jnl`	Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. CoRR Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2024	A*	`conf`	From Causal to Concept-Based Representation Learning. NeurIPS Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2024	A*	`conf`	Identifying General Mechanism Shifts in Linear Causal Representations. NeurIPS Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar
2024	J	`jnl`	Identifying General Mechanism Shifts in Linear Causal Representations. CoRR Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar
2024	J	`jnl`	LLM-Select: Feature Selection with Large Language Models. CoRR Daniel P. Jeong, Zachary C. Lipton, Pradeep Ravikumar
2024	J	`jnl`	Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2024	J	`jnl`	Likelihood-based Differentiable Structure Learning. CoRR Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
2024	A*	`conf`	LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation. NeurIPS Bowen Li, Zhaoyu Li, Qiwei Du, Jinqi Luo, Wenshan Wang, Yaqi Xie, Simon Stepputtis, Chen Wang, Katia P. Sycara, Pradeep Ravikumar, Alexander G. Gray, Xujie Si, Sebastian A. Scherer
2024	A*	`conf`	Markov Equivalence and Consistency in Differentiable Structure Learning. NeurIPS Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
2024	J	`jnl`	On the Origins of Linear Representations in Large Language Models. CoRR Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch
2024	J	`jnl`	Spectrally Transformed Kernel Regression. CoRR Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Ravikumar
2023	J	`jnl`	An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CoRR Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Ravikumar
2023	J	`jnl`	Faith-Shap: The Faithful Shapley Interaction Index. J. Mach. Learn. Res. Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
2023	A*	`conf`	Global Optimality in Bivariate Gradient-based DAG Learning. NeurIPS Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
2023	J	`jnl`	Global Optimality in Bivariate Gradient-based DAG Learning. CoRR Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2023	J	`jnl`	Identifying Representations for Intervention Extrapolation. CoRR Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister, Jonas Peters
2023	J	`jnl`	Individual Fairness Guarantee in Learning with Censorship. CoRR Wenbin Zhang, Juyong Kim, Zichong Wang, Pradeep Ravikumar, Jeremy C. Weiss
2023	A	`conf`	Individual Fairness Under Uncertainty. ECAI Wenbin Zhang, Zichong Wang, Juyong Kim, Cheng Cheng, Thomas Oommen, Pradeep Ravikumar, Jeremy C. Weiss
2023	A*	`conf`	Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2023	J	`jnl`	Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2023	A*	`conf`	Learning with Explanation Constraints. NeurIPS Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar
2023	J	`jnl`	Learning with Explanation Constraints. CoRR Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar
2023	A	`conf`	Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. AISTATS Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang
2023	—	`ch.`	Neuro-Causal Models. Compendium of Neurosymbolic Artificial Intelligence Bryon Aragam, Pradeep Ravikumar
2023	J	`jnl`	Optimizing NOTEARS Objectives via Topological Swaps. CoRR Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2023	J	`jnl`	Representer Point Selection for Explaining Regularized High-dimensional Models. CoRR Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar
2023	A*	`conf`	Responsible AI (RAI) Games and Ensembles. NeurIPS Yash Gupta, Runtian Zhai, Arun Suggala, Pradeep Ravikumar
2023	J	`jnl`	Responsible AI (RAI) Games and Ensembles. CoRR Yash Gupta, Runtian Zhai, Arun Suggala, Pradeep Ravikumar
2023	A*	`conf`	Sample based Explanations via Generalized Representers. NeurIPS Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
2023	J	`jnl`	Sample based Explanations via Generalized Representers. CoRR Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
2023	J	`jnl`	Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation. CoRR Runtian Zhai, Bingbin Liu, Andrej Risteski, Zico Kolter, Pradeep Ravikumar
2023	A*	`conf`	iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. NeurIPS Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2023	J	`jnl`	iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. CoRR Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2022	A	`conf`	An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. AISTATS Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
2022	—	`conf`	AnEMIC: A Framework for Benchmarking ICD Coding Models. EMNLP (Demos) Juyong Kim, Abheesht Sharma, Suhas Shanbhogue, Jeremy C. Weiss, Pradeep Ravikumar
2022	A*	`conf`	Building Robust Ensembles via Margin Boosting. ICML Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
2022	J	`jnl`	Building Robust Ensembles via Margin Boosting. CoRR Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
2022	J	`jnl`	Concept Gradient: Concept-based Interpretation Without Linear Assumption. CoRR Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar, Neil Y. C. Lin, Cho-Jui Hsieh
2022	—	`conf`	Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence. CHIL Juyong Kim, Jeremy C. Weiss, Pradeep Ravikumar
2022	A*	`conf`	DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. NeurIPS Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2022	J	`jnl`	DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. CoRR Kevin Bello, Bryon Aragam, Pradeep Ravikumar
2022	J	`jnl`	Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization. CoRR Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
2022	J	`jnl`	Faith-Shap: The Faithful Shapley Interaction Index. CoRR Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
2022	A*	`conf`	First is Better Than Last for Language Data Influence. NeurIPS Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar
2022	J	`jnl`	First is Better Than Last for Training Data Influence. CoRR Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar
2022	J	`jnl`	Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
2022	A	`conf`	Heavy-tailed Streaming Statistical Estimation. AISTATS Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2022	J	`jnl`	Human-Centered Concept Explanations for Neural Networks. CoRR Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar
2022	J	`jnl`	Identifiability of deep generative models under mixture priors without auxiliary information. CoRR Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2022	A*	`conf`	Identifiability of deep generative models without auxiliary information. NeurIPS Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2022	A	`conf`	Iterative Alignment Flows. AISTATS Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar, David I. Inouye
2022	J	`jnl`	Label Propagation with Weak Supervision. CoRR Rattana Pukdee, Dylan Sam, Maria-Florina Balcan, Pradeep Ravikumar
2022	A*	`conf`	Masked Prediction: A Parameter Identifiability View. NeurIPS Bingbin Liu, Daniel J. Hsu, Pradeep Ravikumar, Andrej Risteski
2022	J	`jnl`	Masked prediction tasks: a parameter identifiability view. CoRR Bingbin Liu, Daniel Hsu, Pradeep Ravikumar, Andrej Risteski
2022	J	`jnl`	Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. CoRR Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang
2022	A	`conf`	Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. AISTATS Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar
2022	J	`jnl`	Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. CoRR Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar
2022	J	`jnl`	Understanding Why Generalized Reweighting Does Not Improve Over ERM. CoRR Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar
2021	J	`jnl`	An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. CoRR Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
2021	J	`jnl`	Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. CoRR Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
2021	A*	`conf`	Boosted CVaR Classification. NeurIPS Runtian Zhai, Chen Dan, Arun Sai Suggala, J. Zico Kolter, Pradeep Ravikumar
2021	J	`jnl`	Boosted CVaR Classification. CoRR Runtian Zhai, Chen Dan, Arun Sai Suggala, J. Zico Kolter, Pradeep Ravikumar
2021	A	`conf`	Contrastive learning of strong-mixing continuous-time stochastic processes. AISTATS Bingbin Liu, Pradeep Ravikumar, Andrej Risteski
2021	J	`jnl`	Contrastive learning of strong-mixing continuous-time stochastic processes. CoRR Bingbin Liu, Pradeep Ravikumar, Andrej Risteski
2021	A*	`conf`	DORO: Distributional and Outlier Robust Optimization. ICML Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar
2021	J	`jnl`	DORO: Distributional and Outlier Robust Optimization. CoRR Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar
2021	A*	`conf`	Efficient Bandit Convex Optimization: Beyond Linear Losses. COLT Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli
2021	J	`jnl`	FILM: Following Instructions in Language with Modular Methods. CoRR So Yeon Min, Devendra Singh Chaplot, Pradeep Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov
2021	J	`jnl`	Heavy-tailed Streaming Statistical Estimation. CoRR Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2021	—	`ch.`	Human-Centered Concept Explanations for Neural Networks. Neuro-Symbolic Artificial Intelligence Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar
2021	—	`conf`	Improving Compositional Generalization in Classification Tasks via Structure Annotations. ACL/IJCNLP (2) Juyong Kim, Pradeep Ravikumar, Joshua Ainslie, Santiago Ontañón
2021	J	`jnl`	Improving Compositional Generalization in Classification Tasks via Structure Annotations. CoRR Juyong Kim, Pradeep Ravikumar, Joshua Ainslie, Santiago Ontañón
2021	J	`jnl`	Iterative Barycenter Flows. CoRR David I. Inouye, Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar
2021	A*	`conf`	Learning latent causal graphs via mixture oracles. NeurIPS Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2021	J	`jnl`	Learning latent causal graphs via mixture oracles. CoRR Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2021	A*	`conf`	On Proximal Policy Optimization's Heavy-tailed Gradients. ICML Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar
2021	J	`jnl`	On Proximal Policy Optimization's Heavy-tailed Gradients. CoRR Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Sivaraman Balakrishnan, Zachary C. Lipton, Ruslan Salakhutdinov, Pradeep Ravikumar
2021	A*	`conf`	Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. AAAI Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, Arindam Banerjee
2021	A	`conf`	Subseasonal climate prediction in the western US using Bayesian spatial models. UAI Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar
2021	A*	`conf`	When Is Generalizable Reinforcement Learning Tractable? NeurIPS Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar
2021	J	`jnl`	When Is Generalizable Reinforcement Learning Tractable? CoRR Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar
2020	A	`conf`	A Robust Univariate Mean Estimator is All You Need. AISTATS Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2020	A	`conf`	Automated Dependence Plots. UAI David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
2020	A*	`conf`	Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. ICML Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter
2020	J	`jnl`	Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. CoRR Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter
2020	A*	`conf`	Class-Weighted Classification: Trade-offs and Robust Approaches. ICML Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
2020	J	`jnl`	Class-Weighted Classification: Trade-offs and Robust Approaches. CoRR Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
2020	J	`jnl`	Evaluations and Methods for Explanation through Robustness Analysis. CoRR Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh
2020	J	`jnl`	Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
2020	A*	`conf`	Generalized Boosting. NeurIPS Arun Sai Suggala, Bingbin Liu, Pradeep Ravikumar
2020	—	`conf`	Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches. ML4H@NeurIPS Juyong Kim, Linyuan Gong, Justin Khim, Jeremy C. Weiss, Pradeep Ravikumar
2020	J	`jnl`	Learning Minimax Estimators via Online Learning. CoRR Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar
2020	A	`conf`	Learning Sparse Nonparametric DAGs. AISTATS Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2020	A*	`conf`	MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. ICLR Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang
2020	J	`jnl`	MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. CoRR Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang
2020	A*	`conf`	Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
2020	J	`jnl`	Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
2020	A*	`conf`	On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar
2020	A*	`conf`	On Learning Ising Models under Huber's Contamination Model. NeurIPS Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar
2020	J	`jnl`	Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification. CoRR Chen Dan, Yuting Wei, Pradeep Ravikumar
2020	A*	`conf`	Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification. ICML Chen Dan, Yuting Wei, Pradeep Ravikumar
2020	J	`jnl`	Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. CoRR Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, Arindam Banerjee
2020	J	`jnl`	The Risks of Invariant Risk Minimization. CoRR Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
2020	A*	`conf`	Uniform Convergence of Rank-weighted Learning. ICML Justin Khim, Liu Leqi, Adarsh Prasad, Pradeep Ravikumar
2019	J	`jnl`	A Unified Approach to Robust Mean Estimation. CoRR Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2019	A*	`conf`	Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. COLT Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain
2019	J	`jnl`	Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. CoRR Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain
2019	A*	`conf`	Building Human-Machine Trust via Interpretability. AAAI Umang Bhatt, Pradeep Ravikumar, José M. F. Moura
2019	J	`jnl`	Diagnostic Curves for Black Box Models. CoRR David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
2019	A*	`conf`	Game Design for Eliciting Distinguishable Behavior. NeurIPS Fan Yang, Liu Leqi, Yifan Wu, Zachary Chase Lipton, Pradeep Ravikumar, Tom M. Mitchell, William W. Cohen
2019	J	`jnl`	Game Design for Eliciting Distinguishable Behavior. CoRR Fan Yang, Liu Leqi, Yifan Wu, Zachary C. Lipton, Pradeep Ravikumar, William W. Cohen, Tom M. Mitchell
2019	J	`jnl`	How Sensitive are Sensitivity-Based Explanations? CoRR Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar
2019	J	`jnl`	Learning Sparse Nonparametric DAGs. CoRR Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2019	J	`jnl`	On Concept-Based Explanations in Deep Neural Networks. CoRR Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister
2019	A*	`conf`	On Human-Aligned Risk Minimization. NeurIPS Liu Leqi, Adarsh Prasad, Pradeep Ravikumar
2019	A*	`conf`	On the (In)fidelity and Sensitivity of Explanations. NeurIPS Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar
2019	J	`jnl`	Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation. CoRR Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep Ravikumar
2019	A*	`conf`	Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation. NeurIPS Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep Ravikumar
2019	A	`conf`	Revisiting Adversarial Risk. AISTATS Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar
2019	J	`jnl`	Towards Aggregating Weighted Feature Attributions. CoRR Umang Bhatt, Pradeep Ravikumar, José M. F. Moura
2018	A*	`conf`	A Voting-Based System for Ethical Decision Making. AAAI Ritesh Noothigattu, Snehalkumar (Neil) S. Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, Ariel D. Procaccia
2018	A*	`conf`	Binary Classification with Karmic, Threshold-Quasi-Concave Metrics. ICML Bowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar
2018	J	`jnl`	Binary Classification with Karmic, Threshold-Quasi-Concave Metrics. CoRR Bowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar
2018	A*	`conf`	Connecting Optimization and Regularization Paths. NeurIPS Arun Sai Suggala, Adarsh Prasad, Pradeep Ravikumar
2018	J	`jnl`	D2KE: From Distance to Kernel and Embedding. CoRR Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock
2018	A*	`conf`	DAGs with NO TEARS: Continuous Optimization for Structure Learning. NeurIPS Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2018	J	`jnl`	DAGs with NO TEARS: Smooth Optimization for Structure Learning. CoRR Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2018	A*	`conf`	Deep Density Destructors. ICML David I. Inouye, Pradeep Ravikumar
2018	J	`jnl`	Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. CoRR Bryon Aragam, Chen Dan, Pradeep Ravikumar, Eric P. Xing
2018	J	`jnl`	Learning Tensor Latent Features. CoRR Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu
2018	A*	`conf`	Loss Decomposition for Fast Learning in Large Output Spaces. ICML Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar
2018	A*	`conf`	MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. NeurIPS Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin
2018	J	`jnl`	On Adversarial Risk and Training. CoRR Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar
2018	A*	`conf`	Representer Point Selection for Explaining Deep Neural Networks. NeurIPS Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar
2018	J	`jnl`	Representer Point Selection for Explaining Deep Neural Networks. CoRR Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar
2018	J	`jnl`	Robust Estimation via Robust Gradient Estimation. CoRR Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar
2018	J	`jnl`	Robust Nonparametric Regression under Huber's ε-contamination Model. CoRR Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh
2018	J	`jnl`	Sample Complexity of Nonparametric Semi-Supervised Learning. CoRR Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2018	A*	`conf`	The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. NeurIPS Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2018	A*	`conf`	Word Mover's Embedding: From Word2Vec to Document Embedding. EMNLP Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
2018	J	`jnl`	Word Mover's Embedding: From Word2Vec to Document Embedding. CoRR Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
2017	J	`jnl`	A Voting-Based System for Ethical Decision Making. CoRR Ritesh Noothigattu, Snehalkumar (Neil) S. Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, Ariel D. Procaccia
2017	J	`jnl`	Cost-Sensitive Learning with Noisy Labels. J. Mach. Learn. Res. Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari
2017	A*	`conf`	Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization. ICML Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar
2017	A	`conf`	Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. AISTATS Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon
2017	A*	`conf`	Latent Feature Lasso. ICML Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar
2017	A	`conf`	Minimax Gaussian Classification & Clustering. AISTATS Tianyang Li, Xinyang Yi, Constantine Caramanis, Pradeep Ravikumar
2017	—	`conf`	On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models. NIPS Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep Ravikumar
2017	A*	`conf`	Ordinal Graphical Models: A Tale of Two Approaches. ICML Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar
2017	A*	`conf`	PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification. KDD Ian En-Hsu Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit S. Dhillon, Eric P. Xing
2017	A	`conf`	Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. AISTATS Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon
2017	—	`conf`	The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities. NIPS Arun Sai Suggala, Mladen Kolar, Pradeep Ravikumar
2016	A*	`conf`	A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery. ICML Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
2016	—	`conf`	Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain. NIPS Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
2016	J	`jnl`	Kernel Ridge Regression via Partitioning. CoRR Rashish Tandon, Si Si, Pradeep Ravikumar, Inderjit S. Dhillon
2016	A*	`conf`	Optimal Classification with Multivariate Losses. ICML Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon
2016	A*	`conf`	PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification. ICML Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon
2016	A*	`conf`	Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies. ICML David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon
2016	J	`jnl`	XMRF: an R package to fit Markov Networks to high-throughput genetics data. BMC Syst. Biol. Ying-Wooi Wan, Genevera I. Allen, Yulia Baker, Eunho Yang, Pradeep Ravikumar, Matthew Anderson, Zhandong Liu
2015	A*	`conf`	A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. ICML Ian En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit S. Dhillon
2015	—	`conf`	Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs. NIPS Vidyashankar Sivakumar, Arindam Banerjee, Pradeep Ravikumar
2015	—	`conf`	Closed-form Estimators for High-dimensional Generalized Linear Models. NIPS Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar
2015	—	`conf`	Collaborative Filtering with Graph Information: Consistency and Scalable Methods. NIPS Nikhil Rao, Hsiang-Fu Yu, Pradeep Ravikumar, Inderjit S. Dhillon
2015	—	`conf`	Consistent Multilabel Classification. NIPS Oluwasanmi Koyejo, Nagarajan Natarajan, Pradeep Ravikumar, Inderjit S. Dhillon
2015	A*	`conf`	Distributional Rank Aggregation, and an Axiomatic Analysis. ICML Adarsh Prasad, Harsh H. Pareek, Pradeep Ravikumar
2015	J	`jnl`	Exponential Family Matrix Completion under Structural Constraints. CoRR Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh
2015	—	`conf`	Fast Classification Rates for High-dimensional Gaussian Generative Models. NIPS Tianyang Li, Adarsh Prasad, Pradeep Ravikumar
2015	—	`conf`	Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial. NIPS David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon
2015	J	`jnl`	Graphical models via univariate exponential family distributions. J. Mach. Learn. Res. Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
2015	—	`conf`	Learning-based analytical cross-platform performance prediction. SAMOS Xinnian Zheng, Pradeep Ravikumar, Lizy K. John, Andreas Gerstlauer
2015	J	`jnl`	Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics. CoRR Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon
2015	—	`conf`	Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent. NIPS Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
2015	A	`conf`	Sparsistency of 1-Regularized M-Estimators. AISTATS Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher
2015	A	`conf`	Tracking with ranked signals. UAI Tianyang Li, Harsh H. Pareek, Pradeep Ravikumar, Dhruv Balwada, Kevin Speer
2015	A*	`conf`	Vector-Space Markov Random Fields via Exponential Families. ICML Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar
2014	—	`conf`	A Representation Theory for Ranking Functions. NIPS Harsh H. Pareek, Pradeep Ravikumar
2014	A*	`conf`	Admixture of Poisson MRFs: A Topic Model with Word Dependencies. ICML David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon
2014	—	`conf`	Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs. NIPS David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon
2014	—	`conf`	Consistent Binary Classification with Generalized Performance Metrics. NIPS Oluwasanmi Koyejo, Nagarajan Natarajan, Pradeep Ravikumar, Inderjit S. Dhillon
2014	—	`conf`	Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings. NIPS Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
2014	—	`conf`	Elementary Estimators for Graphical Models. NIPS Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar
2014	A*	`conf`	Elementary Estimators for High-Dimensional Linear Regression. ICML Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar
2014	A*	`conf`	Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. ICML Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar
2014	A*	`conf`	Exponential Family Matrix Completion under Structural Constraints. ICML Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh
2014	A*	`conf`	Learning Graphs with a Few Hubs. ICML Rashish Tandon, Pradeep Ravikumar
2014	A	`conf`	Mixed Graphical Models via Exponential Families. AISTATS Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
2014	—	`conf`	On the Information Theoretic Limits of Learning Ising Models. NIPS Rashish Tandon, Karthikeyan Shanmugam, Pradeep Ravikumar, Alexandros G. Dimakis
2014	J	`jnl`	On the Information Theoretic Limits of Learning Ising Models. CoRR Karthikeyan Shanmugam, Rashish Tandon, Alexandros G. Dimakis, Pradeep Ravikumar
2014	—	`conf`	Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators. NIPS Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep Ravikumar
2014	—	`conf`	QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models. NIPS Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Stephen Becker, Peder A. Olsen
2014	J	`jnl`	QUIC: quadratic approximation for sparse inverse covariance estimation. J. Mach. Learn. Res. Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar
2014	—	`conf`	Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space. NIPS Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep Ravikumar, Inderjit S. Dhillon
2013	J	`jnl`	A Dirty Model for Multiple Sparse Regression. IEEE Trans. Inf. Theory Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi
2013	—	`conf`	BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables. NIPS Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Russell A. Poldrack
2013	—	`conf`	Conditional Random Fields via Univariate Exponential Families. NIPS Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
2013	—	`conf`	Dirty Statistical Models. NIPS Eunho Yang, Pradeep Ravikumar
2013	—	`conf`	Human Boosting. ICML (1) Harsh H. Pareek, Pradeep Ravikumar
2013	—	`conf`	Large Scale Distributed Sparse Precision Estimation. NIPS Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
2013	—	`conf`	Learning with Noisy Labels. NIPS Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari
2013	—	`conf`	On Poisson Graphical Models. NIPS Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
2013	A*	`conf`	On Robust Estimation of High Dimensional Generalized Linear Models. IJCAI Eunho Yang, Ambuj Tewari, Pradeep Ravikumar
2013	B	`conf`	On the difficulty of learning power law graphical models. ISIT Rashish Tandon, Pradeep Ravikumar
2013	J	`jnl`	Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. CoRR Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar
2012	—	`conf`	A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation. NIPS Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Arindam Banerjee
2012	J	`jnl`	A Hierarchical Graphical Model for Record Linkage CoRR Pradeep Ravikumar, William W. Cohen
2012	—	`conf`	Graphical Models via Generalized Linear Models. NIPS Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
2012	A	`conf`	High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods. AISTATS Christopher C. Johnson, Ali Jalali, Pradeep Ravikumar
2012	J	`jnl`	Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization. IEEE Trans. Inf. Theory Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright
2012	A	`conf`	Perturbation based Large Margin Approach for Ranking. AISTATS Eunho Yang, Ambuj Tewari, Pradeep Ravikumar
2012	—	`conf`	Sparse inverse covariance matrix estimation using quadratic approximation. MLSLP Inderjit S. Dhillon, Cho-Jui Hsieh, Mátyás A. Sustik, Pradeep Ravikumar
2012	J	`jnl`	Variational Chernoff Bounds for Graphical Models CoRR Pradeep Ravikumar, John D. Lafferty
2011	J	`jnl`	A Dirty Model for Multiple Sparse Regression CoRR Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi
2011	—	`conf`	Greedy Algorithms for Structurally Constrained High Dimensional Problems. NIPS Ambuj Tewari, Pradeep Ravikumar, Inderjit S. Dhillon
2011	J	`jnl`	High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods CoRR Christopher C. Johnson, Ali Jalali, Pradeep Ravikumar
2011	—	`conf`	Nearest Neighbor based Greedy Coordinate Descent. NIPS Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari
2011	J	`jnl`	On Learning Discrete Graphical Models Using Greedy Methods CoRR Ali Jalali, Christopher C. Johnson, Pradeep Ravikumar
2011	—	`conf`	On Learning Discrete Graphical Models using Greedy Methods. NIPS Ali Jalali, Christopher C. Johnson, Pradeep Ravikumar
2011	A	`conf`	On Learning Discrete Graphical Models using Group-Sparse Regularization. AISTATS Ali Jalali, Pradeep Ravikumar, Vishvas Vasuki, Sujay Sanghavi
2011	A	`conf`	On NDCG Consistency of Listwise Ranking Methods. AISTATS Pradeep Ravikumar, Ambuj Tewari, Eunho Yang
2011	A*	`conf`	On the Use of Variational Inference for Learning Discrete Graphical Model. ICML Eunho Yang, Pradeep Ravikumar
2011	—	`conf`	Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. NIPS Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar
2010	—	`conf`	A Dirty Model for Multi-task Learning. NIPS Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi, Chao Ruan
2010	J	`jnl`	A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers CoRR Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu
2010	J	`jnl`	Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization. CoRR Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright
2010	J	`jnl`	Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. J. Mach. Learn. Res. Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright
2009	—	`conf`	A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers. NIPS Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu
2009	J	`jnl`	Error-Correcting Tournaments CoRR Alina Beygelzimer, John Langford, Pradeep Ravikumar
2009	B	`conf`	Error-Correcting Tournaments. ALT Alina Beygelzimer, John Langford, Pradeep Ravikumar
2009	—	`conf`	Information-theoretic lower bounds on the oracle complexity of convex optimization. NIPS Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright
2008	J	`jnl`	Approximate inference, structure learning and feature estimation in Markov random fields: thesis abstract. SIGKDD Explor. Pradeep Ravikumar
2008	A*	`conf`	Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes. ICML Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright
2008	—	`conf`	Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l NIPS Pradeep Ravikumar, Garvesh Raskutti, Martin J. Wainwright, Bin Yu
2008	—	`conf`	Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images. NIPS Pradeep Ravikumar, Vincent Q. Vu, Bin Yu, Thomas Naselaris, Kendrick N. Kay, Jack L. Gallant
2007	—	`conf`	SpAM: Sparse Additive Models. NIPS Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman
2006	—	`conf`	High-Dimensional Graphical Model Selection Using ℓ NIPS Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty
2006	A*	`conf`	Quadratic programming relaxations for metric labeling and Markov random field MAP estimation. ICML Pradeep Ravikumar, John D. Lafferty
2005	—	`conf`	Preconditioner Approximations for Probabilistic Graphical Models. NIPS Pradeep Ravikumar, John D. Lafferty
2004	A	`conf`	A Hierarchical Graphical Model for Record Linkage. UAI Pradeep Ravikumar, William W. Cohen
2004	A	`conf`	Variational Chernoff Bounds for Graphical Models. UAI Pradeep Ravikumar, John D. Lafferty
2003	—	`conf`	A Comparison of String Distance Metrics for Name-Matching Tasks. IIWeb William W. Cohen, Pradeep Ravikumar, Stephen E. Fienberg
2003	J	`jnl`	Adaptive Name Matching in Information Integration. IEEE Intell. Syst. Mikhail Bilenko, Raymond J. Mooney, William W. Cohen, Pradeep Ravikumar, Stephen E. Fienberg