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ORCIDPradeep Ravikumar
Pradeep Kumar Ravikumar
Person information
- affiliation: University of Texas in Austin, Department of Computer Science, USA
- affiliation: University of California, Berkeley, Department of Statistics, USA
- affiliation: Carnegie Mellon University, School of Computer Science, USA
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Journal Articles
- 2023
[j11]Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Faith-Shap: The Faithful Shapley Interaction Index. J. Mach. Learn. Res. 24: 94:1-94:42 (2023)- 2022
- [j10]Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar:
Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. 23: 340:1-340:49 (2022) - 2017
- [j9]Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Cost-Sensitive Learning with Noisy Labels. J. Mach. Learn. Res. 18: 155:1-155:33 (2017) - 2016
- [j8]Ying-Wooi Wan, Genevera I. Allen, Yulia Baker, Eunho Yang, Pradeep Ravikumar, Matthew Anderson
, Zhandong Liu:
XMRF: an R package to fit Markov Networks to high-throughput genetics data. BMC Syst. Biol. 10(S-3): 69 (2016) - 2015
- [j7]Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
Graphical models via univariate exponential family distributions. J. Mach. Learn. Res. 16: 3813-3847 (2015) - 2014
- [j6]Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar:
QUIC: quadratic approximation for sparse inverse covariance estimation. J. Mach. Learn. Res. 15(1): 2911-2947 (2014) - 2013
- [j5]Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi:
A Dirty Model for Multiple Sparse Regression. IEEE Trans. Inf. Theory 59(12): 7947-7968 (2013) - 2012
- [j4]Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright:
Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization. IEEE Trans. Inf. Theory 58(5): 3235-3249 (2012) - 2010
- [j3]Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright:
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. J. Mach. Learn. Res. 11: 1043-1080 (2010) - 2008
- [j2]Pradeep Ravikumar:
Approximate inference, structure learning and feature estimation in Markov random fields: thesis abstract. SIGKDD Explor. 10(2): 32-33 (2008) - 2003
- [j1]Mikhail Bilenko, Raymond J. Mooney, William W. Cohen, Pradeep Ravikumar, Stephen E. Fienberg:
Adaptive Name Matching in Information Integration. IEEE Intell. Syst. 18(5): 16-23 (2003)
Conference and Workshop Papers
- 2024
- [c150]Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar:
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CLeaR 2024: 41-70 - [c149]Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Kumar Ravikumar, Niklas Pfister, Jonas Peters:
Identifying Representations for Intervention Extrapolation. ICLR 2024 - [c148]Runtian Zhai, Bingbin Liu, Andrej Risteski, J. Zico Kolter, Pradeep Kumar Ravikumar:
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression. ICLR 2024 - [c147]Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Kumar Ravikumar:
Spectrally Transformed Kernel Regression. ICLR 2024 - [c146]Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch:
On the Origins of Linear Representations in Large Language Models. ICML 2024 - 2023
- [c145]Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang:
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. AISTATS 2023: 9607-9636 - [c144]Wenbin Zhang, Zichong Wang, Juyong Kim, Cheng Cheng, Thomas Oommen, Pradeep Ravikumar, Jeremy C. Weiss:
Individual Fairness Under Uncertainty. ECAI 2023: 3042-3049 - [c143]Andrew Bai, Chih-Kuan Yeh, Neil Y. C. Lin, Pradeep Kumar Ravikumar, Cho-Jui Hsieh:
Concept Gradient: Concept-based Interpretation Without Linear Assumption. ICLR 2023 - [c142]Rattana Pukdee, Dylan Sam, Pradeep Kumar Ravikumar, Nina Balcan:
Label Propagation with Weak Supervision. ICLR 2023 - [c141]Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Kumar Ravikumar:
Understanding Why Generalized Reweighting Does Not Improve Over ERM. ICLR 2023 - [c140]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar:
Optimizing NOTEARS Objectives via Topological Swaps. ICML 2023: 7563-7595 - [c139]Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. ICML 2023: 34469-34490 - [c138]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS 2023 - [c137]Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. NeurIPS 2023 - [c136]Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam:
Global Optimality in Bivariate Gradient-based DAG Learning. NeurIPS 2023 - [c135]Yash Gupta, Runtian Zhai, Arun Sai Suggala, Pradeep Ravikumar:
Responsible AI (RAI) Games and Ensembles. NeurIPS 2023 - [c134]Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar:
Learning with Explanation Constraints. NeurIPS 2023 - [c133]Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Sample based Explanations via Generalized Representers. NeurIPS 2023 - 2022
- [c132]Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
Heavy-tailed Streaming Statistical Estimation. AISTATS 2022: 1251-1282 - [c131]Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar:
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. AISTATS 2022: 1485-1502 - [c130]Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski:
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. AISTATS 2022: 2641-2657 - [c129]Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar, David I. Inouye:
Iterative Alignment Flows. AISTATS 2022: 6409-6444 - [c128]Juyong Kim, Jeremy C. Weiss, Pradeep Ravikumar:
Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence. CHIL 2022: 234-247 - [c127]Juyong Kim, Abheesht Sharma, Suhas Shanbhogue, Jeremy C. Weiss, Pradeep Ravikumar:
AnEMIC: A Framework for Benchmarking ICD Coding Models. EMNLP (Demos) 2022: 109-120 - [c126]Bingbin Liu, Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski:
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. ICLR 2022 - [c125]So Yeon Min, Devendra Singh Chaplot, Pradeep Kumar Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov:
FILM: Following Instructions in Language with Modular Methods. ICLR 2022 - [c124]Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala:
Building Robust Ensembles via Margin Boosting. ICML 2022: 26669-26692 - [c123]Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. NeurIPS 2022 - [c122]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Identifiability of deep generative models without auxiliary information. NeurIPS 2022 - [c121]Bingbin Liu, Daniel J. Hsu, Pradeep Ravikumar, Andrej Risteski:
Masked Prediction: A Parameter Identifiability View. NeurIPS 2022 - [c120]Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar:
First is Better Than Last for Language Data Influence. NeurIPS 2022 - 2021
- [c119]Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, Arindam Banerjee:
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. AAAI 2021: 169-177 - [c118]Juyong Kim, Pradeep Ravikumar, Joshua Ainslie, Santiago Ontañón:
Improving Compositional Generalization in Classification Tasks via Structure Annotations. ACL/IJCNLP (2) 2021: 637-645 - [c117]Bingbin Liu, Pradeep Ravikumar, Andrej Risteski:
Contrastive learning of strong-mixing continuous-time stochastic processes. AISTATS 2021: 3151-3159 - [c116]Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli:
Efficient Bandit Convex Optimization: Beyond Linear Losses. COLT 2021: 4008-4067 - [c115]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. ICLR 2021 - [c114]Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski:
The Risks of Invariant Risk Minimization. ICLR 2021 - [c113]Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar:
On Proximal Policy Optimization's Heavy-tailed Gradients. ICML 2021: 3610-3619 - [c112]Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar:
DORO: Distributional and Outlier Robust Optimization. ICML 2021: 12345-12355 - [c111]Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar:
When Is Generalizable Reinforcement Learning Tractable? NeurIPS 2021: 8032-8045 - [c110]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Learning latent causal graphs via mixture oracles. NeurIPS 2021: 18087-18101 - [c109]Runtian Zhai, Chen Dan, Arun Sai Suggala, J. Zico Kolter, Pradeep Ravikumar:
Boosted CVaR Classification. NeurIPS 2021: 21860-21871 - [c108]Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar:
Subseasonal climate prediction in the western US using Bayesian spatial models. UAI 2021: 961-970 - 2020
- [c107]Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. AISTATS 2020: 3414-3425 - [c106]Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
A Robust Univariate Mean Estimator is All You Need. AISTATS 2020: 4034-4044 - [c105]Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos:
Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR 2020 - [c104]Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang:
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. ICLR 2020 - [c103]Chen Dan, Yuting Wei, Pradeep Ravikumar:
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification. ICML 2020: 2345-2355 - [c102]Justin Khim, Liu Leqi, Adarsh Prasad, Pradeep Ravikumar:
Uniform Convergence of Rank-weighted Learning. ICML 2020: 5254-5263 - [c101]Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter:
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. ICML 2020: 8230-8241 - [c100]Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar:
Class-Weighted Classification: Trade-offs and Robust Approaches. ICML 2020: 10544-10554 - [c99]Juyong Kim, Linyuan Gong, Justin Khim, Jeremy C. Weiss, Pradeep Ravikumar:
Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches. ML4H@NeurIPS 2020: 161-178 - [c98]Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar:
On Learning Ising Models under Huber's Contamination Model. NeurIPS 2020 - [c97]Arun Sai Suggala, Bingbin Liu, Pradeep Ravikumar:
Generalized Boosting. NeurIPS 2020 - [c96]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar:
On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS 2020 - [c95]David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar:
Automated Dependence Plots. UAI 2020: 1238-1247 - 2019
- [c94]Umang Bhatt, Pradeep Ravikumar, José M. F. Moura:
Building Human-Machine Trust via Interpretability. AAAI 2019: 9919-9920 - [c93]Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar:
Revisiting Adversarial Risk. AISTATS 2019: 2331-2339 - [c92]Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain:
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. COLT 2019: 2892-2897 - [c91]Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep Ravikumar:
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation. NeurIPS 2019: 2537-2548 - [c90]Fan Yang, Liu Leqi, Yifan Wu, Zachary Chase Lipton, Pradeep Ravikumar, Tom M. Mitchell, William W. Cohen:
Game Design for Eliciting Distinguishable Behavior. NeurIPS 2019: 4686-4695 - [c89]Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
On the (In)fidelity and Sensitivity of Explanations. NeurIPS 2019: 10965-10976 - [c88]Liu Leqi, Adarsh Prasad, Pradeep Ravikumar:
On Human-Aligned Risk Minimization. NeurIPS 2019: 15029-15038 - 2018
- [c87]Ritesh Noothigattu, Snehalkumar (Neil) S. Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, Ariel D. Procaccia:
A Voting-Based System for Ethical Decision Making. AAAI 2018: 1587-1594 - [c86]Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock:
Word Mover's Embedding: From Word2Vec to Document Embedding. EMNLP 2018: 4524-4534 - [c85]David I. Inouye, Pradeep Ravikumar:
Deep Density Destructors. ICML 2018: 2172-2180 - [c84]Bowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar:
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics. ICML 2018: 5527-5536 - [c83]Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar:
Loss Decomposition for Fast Learning in Large Output Spaces. ICML 2018: 5626-5635 - [c82]Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar:
Representer Point Selection for Explaining Deep Neural Networks. NeurIPS 2018: 9311-9321 - [c81]Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. NeurIPS 2018: 9344-9354 - [c80]Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Continuous Optimization for Structure Learning. NeurIPS 2018: 9492-9503 - [c79]Arun Sai Suggala, Adarsh Prasad, Pradeep Ravikumar:
Connecting Optimization and Regularization Paths. NeurIPS 2018: 10631-10641 - [c78]Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin:
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. NeurIPS 2018: 10891-10899 - 2017
- [c77]Tianyang Li, Xinyang Yi, Constantine Caramanis, Pradeep Ravikumar:
Minimax Gaussian Classification & Clustering. AISTATS 2017: 1-9 - [c76]Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon:
Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. AISTATS 2017: 1514-1522 - [c75]Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon:
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. AISTATS 2017: 1550-1559 - [c74]Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar:
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization. ICML 2017: 2034-2042 - [c73]Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar:
Ordinal Graphical Models: A Tale of Two Approaches. ICML 2017: 3260-3269 - [c72]Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar:
Latent Feature Lasso. ICML 2017: 3949-3957 - [c71]Ian En-Hsu Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit S. Dhillon, Eric P. Xing:
PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification. KDD 2017: 545-553 - [c70]Arun Sai Suggala, Mladen Kolar, Pradeep Ravikumar:
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities. NIPS 2017: 4446-4456 - [c69]Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep Ravikumar:
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models. NIPS 2017: 7050-7059 - 2016
- [c68]Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon:
Optimal Classification with Multivariate Losses. ICML 2016: 1530-1538 - [c67]Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon:
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery. ICML 2016: 2272-2280 - [c66]David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies. ICML 2016: 2445-2453 - [c65]Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon:
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification. ICML 2016: 3069-3077 - [c64]Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon:
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain. NIPS 2016: 5024-5032 - 2015
- [c63]Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher:
Sparsistency of 1-Regularized M-Estimators. AISTATS 2015 - [c62]Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar:
Vector-Space Markov Random Fields via Exponential Families. ICML 2015: 684-692 - [c61]Adarsh Prasad, Harsh H. Pareek, Pradeep Ravikumar:
Distributional Rank Aggregation, and an Axiomatic Analysis. ICML 2015: 2104-2112 - [c60]Ian En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit S. Dhillon:
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. ICML 2015: 2418-2426 - [c59]Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Closed-form Estimators for High-dimensional Generalized Linear Models. NIPS 2015: 586-594 - [c58]Tianyang Li, Adarsh Prasad, Pradeep Ravikumar:
Fast Classification Rates for High-dimensional Gaussian Generative Models. NIPS 2015: 1054-1062 - [c57]Nikhil Rao, Hsiang-Fu Yu, Pradeep Ravikumar, Inderjit S. Dhillon:
Collaborative Filtering with Graph Information: Consistency and Scalable Methods. NIPS 2015: 2107-2115 - [c56]Vidyashankar Sivakumar, Arindam Banerjee, Pradeep Ravikumar:
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs. NIPS 2015: 2206-2214 - [c55]Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent. NIPS 2015: 2368-2376 - [c54]David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial. NIPS 2015: 3213-3221 - [c53]Oluwasanmi Koyejo, Nagarajan Natarajan, Pradeep Ravikumar, Inderjit S. Dhillon:
Consistent Multilabel Classification. NIPS 2015: 3321-3329 - [c52]Xinnian Zheng, Pradeep Ravikumar, Lizy K. John, Andreas Gerstlauer:
Learning-based analytical cross-platform performance prediction. SAMOS 2015: 52-59 - [c51]Tianyang Li, Harsh H. Pareek, Pradeep Ravikumar, Dhruv Balwada, Kevin Speer:
Tracking with ranked signals. UAI 2015: 474-483 - 2014
- [c50]Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
Mixed Graphical Models via Exponential Families. AISTATS 2014: 1042-1050 - [c49]Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for High-Dimensional Linear Regression. ICML 2014: 388-396 - [c48]Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. ICML 2014: 397-405 - [c47]Rashish Tandon, Pradeep Ravikumar:
Learning Graphs with a Few Hubs. ICML 2014: 602-610 - [c46]David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Admixture of Poisson MRFs: A Topic Model with Word Dependencies. ICML 2014: 683-691 - [c45]Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh:
Exponential Family Matrix Completion under Structural Constraints. ICML 2014: 1917-1925 - [c44]Harsh H. Pareek, Pradeep Ravikumar:
A Representation Theory for Ranking Functions. NIPS 2014: 361-369 - [c43]Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings. NIPS 2014: 1008-1016 - [c42]Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Stephen Becker, Peder A. Olsen:
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models. NIPS 2014: 2006-2014 - [c41]Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for Graphical Models. NIPS 2014: 2159-2167 - [c40]Rashish Tandon, Karthikeyan Shanmugam, Pradeep Ravikumar, Alexandros G. Dimakis:
On the Information Theoretic Limits of Learning Ising Models. NIPS 2014: 2303-2311 - [c39]Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep Ravikumar:
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators. NIPS 2014: 2375-2383 - [c38]Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep Ravikumar, Inderjit S. Dhillon:
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space. NIPS 2014: 2456-2464 - [c37]Oluwasanmi Koyejo, Nagarajan Natarajan, Pradeep Ravikumar, Inderjit S. Dhillon:
Consistent Binary Classification with Generalized Performance Metrics. NIPS 2014: 2744-2752 - [c36]David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs. NIPS 2014: 3158-3166 - 2013
- [c35]Harsh H. Pareek, Pradeep Ravikumar:
Human Boosting. ICML (1) 2013: 338-346 - [c34]Eunho Yang, Ambuj Tewari, Pradeep Ravikumar:
On Robust Estimation of High Dimensional Generalized Linear Models. IJCAI 2013: 1834-1840 - [c33]Rashish Tandon, Pradeep Ravikumar:
On the difficulty of learning power law graphical models. ISIT 2013: 2493-2497 - [c32]Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Large Scale Distributed Sparse Precision Estimation. NIPS 2013: 584-592 - [c31]Eunho Yang, Pradeep Ravikumar:
Dirty Statistical Models. NIPS 2013: 611-619 - [c30]Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
Conditional Random Fields via Univariate Exponential Families. NIPS 2013: 683-691 - [c29]Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Learning with Noisy Labels. NIPS 2013: 1196-1204 - [c28]Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
On Poisson Graphical Models. NIPS 2013: 1718-1726 - [c27]Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Russell A. Poldrack:
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables. NIPS 2013: 3165-3173 - 2012
- [c26]Inderjit S. Dhillon, Cho-Jui Hsieh, Mátyás A. Sustik, Pradeep Ravikumar:
Sparse inverse covariance matrix estimation using quadratic approximation. MLSLP 2012 - [c25]Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
Graphical Models via Generalized Linear Models. NIPS 2012: 1367-1375 - [c24]Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Arindam Banerjee:
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation. NIPS 2012: 2339-2347 - [c23]Christopher C. Johnson, Ali Jalali, Pradeep Ravikumar:
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods. AISTATS 2012: 574-582 - [c22]Eunho Yang, Ambuj Tewari, Pradeep Ravikumar:
Perturbation based Large Margin Approach for Ranking. AISTATS 2012: 1358-1366 - 2011
- [c21]Eunho Yang, Pradeep Ravikumar:
On the Use of Variational Inference for Learning Discrete Graphical Model. ICML 2011: 1009-1016 - [c20]Ambuj Tewari, Pradeep Ravikumar, Inderjit S. Dhillon:
Greedy Algorithms for Structurally Constrained High Dimensional Problems. NIPS 2011: 882-890 - [c19]Ali Jalali, Christopher C. Johnson, Pradeep Ravikumar:
On Learning Discrete Graphical Models using Greedy Methods. NIPS 2011: 1935-1943 - [c18]Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Nearest Neighbor based Greedy Coordinate Descent. NIPS 2011: 2160-2168 - [c17]Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar:
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. NIPS 2011: 2330-2338 - [c16]Ali Jalali, Pradeep Ravikumar, Vishvas Vasuki, Sujay Sanghavi:
On Learning Discrete Graphical Models using Group-Sparse Regularization. AISTATS 2011: 378-387 - [c15]Pradeep Ravikumar, Ambuj Tewari, Eunho Yang:
On NDCG Consistency of Listwise Ranking Methods. AISTATS 2011: 618-626 - 2010
- [c14]Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi, Chao Ruan:
A Dirty Model for Multi-task Learning. NIPS 2010: 964-972 - 2009
- [c13]Alina Beygelzimer, John Langford, Pradeep Ravikumar:
Error-Correcting Tournaments. ALT 2009: 247-262 - [c12]Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright:
Information-theoretic lower bounds on the oracle complexity of convex optimization. NIPS 2009: 1-9 - [c11]Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu:
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers. NIPS 2009: 1348-1356 - 2008
- [c10]Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright:
Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes. ICML 2008: 800-807 - [c9]Pradeep Ravikumar, Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE. NIPS 2008: 1329-1336 - [c8]Pradeep Ravikumar, Vincent Q. Vu, Bin Yu, Thomas Naselaris, Kendrick N. Kay, Jack L. Gallant:
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images. NIPS 2008: 1337-1344 - 2007
- [c7]Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman:
SpAM: Sparse Additive Models. NIPS 2007: 1201-1208 - 2006
- [c6]Pradeep Ravikumar, John D. Lafferty:
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation. ICML 2006: 737-744 - [c5]Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty:
High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression. NIPS 2006: 1465-1472 - 2005
- [c4]Pradeep Ravikumar, John D. Lafferty:
Preconditioner Approximations for Probabilistic Graphical Models. NIPS 2005: 1113-1120 - 2004
- [c3]Pradeep Ravikumar, William W. Cohen:
A Hierarchical Graphical Model for Record Linkage. UAI 2004: 454-461 - [c2]Pradeep Ravikumar, John D. Lafferty:
Variational Chernoff Bounds for Graphical Models. UAI 2004: 462-469 - 2003
- [c1]William W. Cohen, Pradeep Ravikumar, Stephen E. Fienberg:
A Comparison of String Distance Metrics for Name-Matching Tasks. IIWeb 2003: 73-78
Parts in Books or Collections
- 2023
- [p2]Bryon Aragam, Pradeep Ravikumar:
Neuro-Causal Models. Compendium of Neurosymbolic Artificial Intelligence 2023: 153-177 - 2021
- [p1]Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar:
Human-Centered Concept Explanations for Neural Networks. Neuro-Symbolic Artificial Intelligence 2021: 337-352
Informal and Other Publications
- 2024
- [i86]Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Ravikumar:
Spectrally Transformed Kernel Regression. CoRR abs/2402.00645 (2024) - [i85]Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR abs/2402.09236 (2024) - [i84]Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch:
On the Origins of Linear Representations in Large Language Models. CoRR abs/2403.03867 (2024) - [i83]Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. CoRR abs/2406.18400 (2024) - [i82]Daniel P. Jeong, Zachary C. Lipton, Pradeep Ravikumar:
LLM-Select: Feature Selection with Large Language Models. CoRR abs/2407.02694 (2024) - 2023
- [i81]Wenbin Zhang, Juyong Kim, Zichong Wang, Pradeep Ravikumar, Jeremy C. Weiss:
Individual Fairness Guarantee in Learning with Censorship. CoRR abs/2302.08015 (2023) - [i80]Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar:
Learning with Explanation Constraints. CoRR abs/2303.14496 (2023) - [i79]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
Optimizing NOTEARS Objectives via Topological Swaps. CoRR abs/2305.17277 (2023) - [i78]Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. CoRR abs/2305.20002 (2023) - [i77]Runtian Zhai, Bingbin Liu, Andrej Risteski, Zico Kolter, Pradeep Ravikumar:
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation. CoRR abs/2306.00788 (2023) - [i76]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR abs/2306.02235 (2023) - [i75]Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. CoRR abs/2306.17361 (2023) - [i74]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
Global Optimality in Bivariate Gradient-based DAG Learning. CoRR abs/2306.17378 (2023) - [i73]Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister, Jonas Peters:
Identifying Representations for Intervention Extrapolation. CoRR abs/2310.04295 (2023) - [i72]Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Sample based Explanations via Generalized Representers. CoRR abs/2310.18526 (2023) - [i71]Yash Gupta, Runtian Zhai, Arun Sai Suggala, Pradeep Ravikumar:
Responsible AI (RAI) Games and Ensembles. CoRR abs/2310.18832 (2023) - [i70]Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Ravikumar:
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CoRR abs/2311.18048 (2023) - 2022
- [i69]Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar:
Understanding Why Generalized Reweighting Does Not Improve Over ERM. CoRR abs/2201.12293 (2022) - [i68]Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski:
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization. CoRR abs/2202.06856 (2022) - [i67]Bingbin Liu, Daniel Hsu, Pradeep Ravikumar, Andrej Risteski:
Masked prediction tasks: a parameter identifiability view. CoRR abs/2202.09305 (2022) - [i66]Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar:
First is Better Than Last for Training Data Influence. CoRR abs/2202.11844 (2022) - [i65]Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar:
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. CoRR abs/2202.11919 (2022) - [i64]Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar:
Human-Centered Concept Explanations for Neural Networks. CoRR abs/2202.12451 (2022) - [i63]Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Faith-Shap: The Faithful Shapley Interaction Index. CoRR abs/2203.00870 (2022) - [i62]Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala:
Building Robust Ensembles via Margin Boosting. CoRR abs/2206.03362 (2022) - [i61]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Identifiability of deep generative models under mixture priors without auxiliary information. CoRR abs/2206.10044 (2022) - [i60]Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar, Neil Y. C. Lin, Cho-Jui Hsieh:
Concept Gradient: Concept-based Interpretation Without Linear Assumption. CoRR abs/2208.14966 (2022) - [i59]Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. CoRR abs/2209.08037 (2022) - [i58]Rattana Pukdee, Dylan Sam, Maria-Florina Balcan, Pradeep Ravikumar:
Label Propagation with Weak Supervision. CoRR abs/2210.03594 (2022) - [i57]Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang:
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. CoRR abs/2210.12606 (2022) - 2021
- [i56]Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar:
When Is Generalizable Reinforcement Learning Tractable? CoRR abs/2101.00300 (2021) - [i55]Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Sivaraman Balakrishnan, Zachary C. Lipton, Ruslan Salakhutdinov, Pradeep Ravikumar:
On Proximal Policy Optimization's Heavy-tailed Gradients. CoRR abs/2102.10264 (2021) - [i54]Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski:
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. CoRR abs/2102.13128 (2021) - [i53]Bingbin Liu, Pradeep Ravikumar, Andrej Risteski:
Contrastive learning of strong-mixing continuous-time stochastic processes. CoRR abs/2103.02740 (2021) - [i52]David I. Inouye, Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar:
Iterative Barycenter Flows. CoRR abs/2104.07232 (2021) - [i51]Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar:
DORO: Distributional and Outlier Robust Optimization. CoRR abs/2106.06142 (2021) - [i50]Juyong Kim, Pradeep Ravikumar, Joshua Ainslie, Santiago Ontañón:
Improving Compositional Generalization in Classification Tasks via Structure Annotations. CoRR abs/2106.10434 (2021) - [i49]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Learning latent causal graphs via mixture oracles. CoRR abs/2106.15563 (2021) - [i48]Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
Heavy-tailed Streaming Statistical Estimation. CoRR abs/2108.11483 (2021) - [i47]So Yeon Min, Devendra Singh Chaplot, Pradeep Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov:
FILM: Following Instructions in Language with Modular Methods. CoRR abs/2110.07342 (2021) - [i46]Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski:
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. CoRR abs/2110.11271 (2021) - [i45]Runtian Zhai, Chen Dan, Arun Sai Suggala, J. Zico Kolter, Pradeep Ravikumar:
Boosted CVaR Classification. CoRR abs/2110.13948 (2021) - 2020
- [i44]Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang:
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. CoRR abs/2001.02378 (2020) - [i43]Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter:
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. CoRR abs/2002.03018 (2020) - [i42]Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos:
Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR abs/2004.05665 (2020) - [i41]Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar:
Class-Weighted Classification: Trade-offs and Robust Approaches. CoRR abs/2005.12914 (2020) - [i40]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. CoRR abs/2006.00442 (2020) - [i39]Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, Arindam Banerjee:
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. CoRR abs/2006.07972 (2020) - [i38]Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar:
Learning Minimax Estimators via Online Learning. CoRR abs/2006.11430 (2020) - [i37]Chen Dan, Yuting Wei, Pradeep Ravikumar:
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification. CoRR abs/2006.16384 (2020) - [i36]Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski:
The Risks of Invariant Risk Minimization. CoRR abs/2010.05761 (2020) - [i35]Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar:
Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR abs/2012.10713 (2020) - 2019
- [i34]Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
How Sensitive are Sensitivity-Based Explanations? CoRR abs/1901.09392 (2019) - [i33]Umang Bhatt, Pradeep Ravikumar, José M. F. Moura:
Towards Aggregating Weighted Feature Attributions. CoRR abs/1901.10040 (2019) - [i32]Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain:
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. CoRR abs/1903.08192 (2019) - [i31]Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar:
A Unified Approach to Robust Mean Estimation. CoRR abs/1907.00927 (2019) - [i30]Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. CoRR abs/1909.13189 (2019) - [i29]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister:
On Concept-Based Explanations in Deep Neural Networks. CoRR abs/1910.07969 (2019) - [i28]Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep Ravikumar:
Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation. CoRR abs/1910.13618 (2019) - [i27]David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar:
Diagnostic Curves for Black Box Models. CoRR abs/1912.01108 (2019) - [i26]Fan Yang, Liu Leqi, Yifan Wu, Zachary C. Lipton, Pradeep Ravikumar, William W. Cohen, Tom M. Mitchell:
Game Design for Eliciting Distinguishable Behavior. CoRR abs/1912.06074 (2019) - 2018
- [i25]Bryon Aragam, Chen Dan, Pradeep Ravikumar, Eric P. Xing:
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. CoRR abs/1802.04397 (2018) - [i24]Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock:
D2KE: From Distance to Kernel and Embedding. CoRR abs/1802.04956 (2018) - [i23]Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar:
Robust Estimation via Robust Gradient Estimation. CoRR abs/1802.06485 (2018) - [i22]Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Smooth Optimization for Structure Learning. CoRR abs/1803.01422 (2018) - [i21]Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh:
Robust Nonparametric Regression under Huber's ε-contamination Model. CoRR abs/1805.10406 (2018) - [i20]Bowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar:
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics. CoRR abs/1806.00640 (2018) - [i19]Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar:
On Adversarial Risk and Training. CoRR abs/1806.02924 (2018) - [i18]Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Sample Complexity of Nonparametric Semi-Supervised Learning. CoRR abs/1809.03073 (2018) - [i17]Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu:
Learning Tensor Latent Features. CoRR abs/1810.04754 (2018) - [i16]Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock:
Word Mover's Embedding: From Word2Vec to Document Embedding. CoRR abs/1811.01713 (2018) - [i15]Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar:
Representer Point Selection for Explaining Deep Neural Networks. CoRR abs/1811.09720 (2018) - 2017
- [i14]Ritesh Noothigattu, Snehalkumar (Neil) S. Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, Ariel D. Procaccia:
A Voting-Based System for Ethical Decision Making. CoRR abs/1709.06692 (2017) - 2016
- [i13]Rashish Tandon, Si Si, Pradeep Ravikumar, Inderjit S. Dhillon:
Kernel Ridge Regression via Partitioning. CoRR abs/1608.01976 (2016) - 2015
- [i12]Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon:
Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics. CoRR abs/1505.01802 (2015) - [i11]Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh:
Exponential Family Matrix Completion under Structural Constraints. CoRR abs/1509.04397 (2015) - 2014
- [i10]Karthikeyan Shanmugam, Rashish Tandon, Alexandros G. Dimakis, Pradeep Ravikumar:
On the Information Theoretic Limits of Learning Ising Models. CoRR abs/1411.1434 (2014) - 2013
- [i9]Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar:
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. CoRR abs/1306.3212 (2013) - 2012
- [i8]Pradeep Ravikumar, John D. Lafferty:
Variational Chernoff Bounds for Graphical Models. CoRR abs/1207.4172 (2012) - [i7]Pradeep Ravikumar, William W. Cohen:
A Hierarchical Graphical Model for Record Linkage. CoRR abs/1207.4180 (2012) - 2011
- [i6]Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi:
A Dirty Model for Multiple Sparse Regression. CoRR abs/1106.5826 (2011) - [i5]Ali Jalali, Christopher C. Johnson, Pradeep Ravikumar:
On Learning Discrete Graphical Models Using Greedy Methods. CoRR abs/1107.3258 (2011) - [i4]Christopher C. Johnson, Ali Jalali, Pradeep Ravikumar:
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods. CoRR abs/1112.6411 (2011) - 2010
- [i3]Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright:
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization. CoRR abs/1009.0571 (2010) - [i2]Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu:
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers. CoRR abs/1010.2731 (2010) - 2009
- [i1]Alina Beygelzimer, John Langford, Pradeep Ravikumar:
Error-Correcting Tournaments. CoRR abs/0902.3176 (2009)
Coauthor Index
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