A Comparison of String Distance Metrics for Name-Matching Tasks. - William W. Cohen; Pradeep Ravikumar; Stephen E. Fienberg (IIWeb, 2003)

Adaptive Name Matching in Information Integration. - Mikhail Bilenko; Raymond J. Mooney; William W. Cohen; Pradeep Ravikumar; Stephen E. Fienberg (IEEE Intell. Syst., 2003)

A Hierarchical Graphical Model for Record Linkage. - Pradeep Ravikumar; William W. Cohen (UAI, 2004)

Variational Chernoff Bounds for Graphical Models. - Pradeep Ravikumar; John D. Lafferty (UAI, 2004)

Preconditioner Approximations for Probabilistic Graphical Models. - Pradeep Ravikumar; John D. Lafferty (NIPS, 2005)

Quadratic programming relaxations for metric labeling and Markov random field MAP estimation. - Pradeep Ravikumar; John D. Lafferty (ICML, 2006)

High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression. - Martin J. Wainwright; Pradeep Ravikumar; John D. Lafferty (NIPS, 2006)

SpAM: Sparse Additive Models. - Pradeep Ravikumar; Han Liu 001; John D. Lafferty; Larry A. Wasserman (NIPS, 2007)

Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes. - Pradeep Ravikumar; Alekh Agarwal; Martin J. Wainwright (ICML, 2008)

Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE. - Pradeep Ravikumar; Garvesh Raskutti; Martin J. Wainwright; Bin Yu 001 (NIPS, 2008)

Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images. - Pradeep Ravikumar; Vincent Q. Vu; Bin Yu 001; Thomas Naselaris; Kendrick N. Kay; Jack L. Gallant (NIPS, 2008)

Approximate inference, structure learning and feature estimation in Markov random fields: thesis abstract. - Pradeep Ravikumar (SIGKDD Explor., 2008)

Error-Correcting Tournaments. - Alina Beygelzimer; John Langford 001; Pradeep Ravikumar (ALT, 2009)

Information-theoretic lower bounds on the oracle complexity of convex optimization. - Alekh Agarwal; Peter L. Bartlett; Pradeep Ravikumar; Martin J. Wainwright (NIPS, 2009)

A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers. - Sahand N. Negahban; Pradeep Ravikumar; Martin J. Wainwright; Bin Yu 001 (NIPS, 2009)

A Dirty Model for Multi-task Learning. - Ali Jalali; Pradeep Ravikumar; Sujay Sanghavi; Chao Ruan (NIPS, 2010)

Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. - Pradeep Ravikumar; Alekh Agarwal; Martin J. Wainwright (J. Mach. Learn. Res., 2010)

On the Use of Variational Inference for Learning Discrete Graphical Model. - Eunho Yang; Pradeep Ravikumar (ICML, 2011)

Nearest Neighbor based Greedy Coordinate Descent. - Inderjit S. Dhillon; Pradeep Ravikumar; Ambuj Tewari (NIPS, 2011)

Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. - Cho-Jui Hsieh; Mátyás A. Sustik; Inderjit S. Dhillon; Pradeep Ravikumar (NIPS, 2011)

On Learning Discrete Graphical Models using Greedy Methods. - Ali Jalali; Christopher C. Johnson; Pradeep Ravikumar (NIPS, 2011)

Greedy Algorithms for Structurally Constrained High Dimensional Problems. - Ambuj Tewari; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2011)

On Learning Discrete Graphical Models using Group-Sparse Regularization. - Ali Jalali; Pradeep Ravikumar; Vishvas Vasuki; Sujay Sanghavi (AISTATS, 2011)

On NDCG Consistency of Listwise Ranking Methods. - Pradeep Ravikumar; Ambuj Tewari; Eunho Yang (AISTATS, 2011)

A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation. - Cho-Jui Hsieh; Inderjit S. Dhillon; Pradeep Ravikumar; Arindam Banerjee 001 (NIPS, 2012)

Graphical Models via Generalized Linear Models. - Eunho Yang; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (NIPS, 2012)

High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods. - Christopher C. Johnson; Ali Jalali; Pradeep Ravikumar (AISTATS, 2012)

Perturbation based Large Margin Approach for Ranking. - Eunho Yang; Ambuj Tewari; Pradeep Ravikumar (AISTATS, 2012)

Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization. - Alekh Agarwal; Peter L. Bartlett; Pradeep Ravikumar; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2012)

Human Boosting. - Harsh H. Pareek; Pradeep Ravikumar (ICML (1), 2013)

On Robust Estimation of High Dimensional Generalized Linear Models. - Eunho Yang; Ambuj Tewari; Pradeep Ravikumar (IJCAI, 2013)

On the difficulty of learning power law graphical models. - Rashish Tandon; Pradeep Ravikumar (ISIT, 2013)

BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables. - Cho-Jui Hsieh; Mátyás A. Sustik; Inderjit S. Dhillon; Pradeep Ravikumar; Russell A. Poldrack (NIPS, 2013)

Learning with Noisy Labels. - Nagarajan Natarajan; Inderjit S. Dhillon; Pradeep Ravikumar; Ambuj Tewari (NIPS, 2013)

Large Scale Distributed Sparse Precision Estimation. - Huahua Wang; Arindam Banerjee 001; Cho-Jui Hsieh; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2013)

Dirty Statistical Models. - Eunho Yang; Pradeep Ravikumar (NIPS, 2013)

Conditional Random Fields via Univariate Exponential Families. - Eunho Yang; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (NIPS, 2013)

On Poisson Graphical Models. - Eunho Yang; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (NIPS, 2013)

A Dirty Model for Multiple Sparse Regression. - Ali Jalali; Pradeep Ravikumar; Sujay Sanghavi (IEEE Trans. Inf. Theory, 2013)

Mixed Graphical Models via Exponential Families. - Eunho Yang; Yulia Baker; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (AISTATS, 2014)

Exponential Family Matrix Completion under Structural Constraints. - Suriya Gunasekar; Pradeep Ravikumar; Joydeep Ghosh (ICML, 2014)

Admixture of Poisson MRFs: A Topic Model with Word Dependencies. - David I. Inouye; Pradeep Ravikumar; Inderjit S. Dhillon (ICML, 2014)

Learning Graphs with a Few Hubs. - Rashish Tandon; Pradeep Ravikumar (ICML, 2014)

Elementary Estimators for High-Dimensional Linear Regression. - Eunho Yang; Aurélie C. Lozano; Pradeep Ravikumar (ICML, 2014)

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. - Eunho Yang; Aurélie C. Lozano; Pradeep Ravikumar (ICML, 2014)

QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models. - Cho-Jui Hsieh; Inderjit S. Dhillon; Pradeep Ravikumar; Stephen Becker; Peder A. Olsen (NIPS, 2014)

Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs. - David I. Inouye; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2014)

Consistent Binary Classification with Generalized Performance Metrics. - Oluwasanmi Koyejo; Nagarajan Natarajan; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2014)

A Representation Theory for Ranking Functions. - Harsh H. Pareek; Pradeep Ravikumar (NIPS, 2014)

On the Information Theoretic Limits of Learning Ising Models. - Rashish Tandon; Karthikeyan Shanmugam; Pradeep Ravikumar; Alexandros G. Dimakis (NIPS, 2014)

Elementary Estimators for Graphical Models. - Eunho Yang; Aurélie C. Lozano; Pradeep Ravikumar (NIPS, 2014)

Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings. - Ian En-Hsu Yen; Cho-Jui Hsieh; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2014)

Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space. - Ian En-Hsu Yen; Ting-Wei Lin; Shou-De Lin; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2014)

Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators. - Kai Zhong; Ian En-Hsu Yen; Inderjit S. Dhillon; Pradeep Ravikumar (NIPS, 2014)

QUIC: quadratic approximation for sparse inverse covariance estimation. - Cho-Jui Hsieh; Mátyás A. Sustik; Inderjit S. Dhillon; Pradeep Ravikumar (J. Mach. Learn. Res., 2014)

Sparsistency of 1-Regularized M-Estimators. - Yen-Huan Li; Jonathan Scarlett; Pradeep Ravikumar; Volkan Cevher (AISTATS, 2015)

Distributional Rank Aggregation, and an Axiomatic Analysis. - Adarsh Prasad; Harsh H. Pareek; Pradeep Ravikumar (ICML, 2015)

Vector-Space Markov Random Fields via Exponential Families. - Wesley Tansey; Oscar Hernan Madrid Padilla; Arun Sai Suggala; Pradeep Ravikumar (ICML, 2015)

A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. - Ian En-Hsu Yen; Xin Lin; Kai Zhong; Pradeep Ravikumar; Inderjit S. Dhillon (ICML, 2015)

Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial. - David I. Inouye; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2015)

Consistent Multilabel Classification. - Oluwasanmi Koyejo; Nagarajan Natarajan; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2015)

Fast Classification Rates for High-dimensional Gaussian Generative Models. - Tianyang Li; Adarsh Prasad; Pradeep Ravikumar (NIPS, 2015)

Collaborative Filtering with Graph Information: Consistency and Scalable Methods. - Nikhil Rao 001; Hsiang-Fu Yu; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2015)

Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs. - Vidyashankar Sivakumar; Arindam Banerjee 001; Pradeep Ravikumar (NIPS, 2015)

Closed-form Estimators for High-dimensional Generalized Linear Models. - Eunho Yang; Aurélie C. Lozano; Pradeep Ravikumar (NIPS, 2015)

Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent. - Ian En-Hsu Yen; Kai Zhong; Cho-Jui Hsieh; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2015)

Learning-based analytical cross-platform performance prediction. - Xinnian Zheng; Pradeep Ravikumar; Lizy K. John; Andreas Gerstlauer (SAMOS, 2015)

Tracking with ranked signals. - Tianyang Li; Harsh H. Pareek; Pradeep Ravikumar; Dhruv Balwada; Kevin Speer (UAI, 2015)

Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics. - Nagarajan Natarajan; Oluwasanmi Koyejo; Pradeep Ravikumar; Inderjit S. Dhillon (CoRR, 2015)

Graphical models via univariate exponential family distributions. - Eunho Yang; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (J. Mach. Learn. Res., 2015)

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies. - David I. Inouye; Pradeep Ravikumar; Inderjit S. Dhillon (ICML, 2016)

Optimal Classification with Multivariate Losses. - Nagarajan Natarajan; Oluwasanmi Koyejo; Pradeep Ravikumar; Inderjit S. Dhillon (ICML, 2016)

PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification. - Ian En-Hsu Yen; Xiangru Huang; Pradeep Ravikumar; Kai Zhong; Inderjit S. Dhillon (ICML, 2016)

A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery. - Ian En-Hsu Yen; Xin Lin; Jiong Zhang; Pradeep Ravikumar; Inderjit S. Dhillon (ICML, 2016)

Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain. - Ian En-Hsu Yen; Xiangru Huang; Kai Zhong; Ruohan Zhang; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2016)

XMRF: an R package to fit Markov Networks to high-throughput genetics data. - Ying-Wooi Wan; Genevera I. Allen; Yulia Baker; Eunho Yang; Pradeep Ravikumar; Matthew Anderson 002; Zhandong Liu (BMC Syst. Biol., 2016)

Kernel Ridge Regression via Partitioning. - Rashish Tandon; Si Si; Pradeep Ravikumar; Inderjit S. Dhillon (CoRR, 2016)

Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. - Xiangru Huang; Ian En-Hsu Yen; Ruohan Zhang; Qixing Huang; Pradeep Ravikumar; Inderjit S. Dhillon (AISTATS, 2017)

Minimax Gaussian Classification & Clustering. - Tianyang Li; Xinyang Yi; Constantine Caramanis; Pradeep Ravikumar (AISTATS, 2017)

Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. - Jiong Zhang; Ian En-Hsu Yen; Pradeep Ravikumar; Inderjit S. Dhillon (AISTATS, 2017)

Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization. - Qi Lei; Ian En-Hsu Yen; Chao-Yuan Wu; Inderjit S. Dhillon; Pradeep Ravikumar (ICML, 2017)

Ordinal Graphical Models: A Tale of Two Approaches. - Arun Sai Suggala; Eunho Yang; Pradeep Ravikumar (ICML, 2017)

Latent Feature Lasso. - Ian En-Hsu Yen; Wei-Cheng Lee; Sung-En Chang; Arun Sai Suggala; Shou-De Lin; Pradeep Ravikumar (ICML, 2017)

PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification. - Ian En-Hsu Yen; Xiangru Huang; Wei Dai 003; Pradeep Ravikumar; Inderjit S. Dhillon; Eric P. Xing (KDD, 2017)

On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models. - Adarsh Prasad; Alexandru Niculescu-Mizil; Pradeep Ravikumar (NIPS, 2017)

The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities. - Arun Sai Suggala; Mladen Kolar; Pradeep Ravikumar (NIPS, 2017)

Cost-Sensitive Learning with Noisy Labels. - Nagarajan Natarajan; Inderjit S. Dhillon; Pradeep Ravikumar; Ambuj Tewari (J. Mach. Learn. Res., 2017)

A Voting-Based System for Ethical Decision Making. - Ritesh Noothigattu; Snehalkumar (Neil) S. Gaikwad; Edmond Awad; Sohan Dsouza; Iyad Rahwan; Pradeep Ravikumar; Ariel D. Procaccia (AAAI, 2018)

Word Mover's Embedding: From Word2Vec to Document Embedding. - Lingfei Wu 001; Ian En-Hsu Yen; Kun Xu 005; Fangli Xu; Avinash Balakrishnan; Pin-Yu Chen; Pradeep Ravikumar; Michael J. Witbrock (EMNLP, 2018)

Deep Density Destructors. - David I. Inouye; Pradeep Ravikumar (ICML, 2018)

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics. - Bowei Yan; Oluwasanmi Koyejo; Kai Zhong; Pradeep Ravikumar (ICML, 2018)

Loss Decomposition for Fast Learning in Large Output Spaces. - Ian En-Hsu Yen; Satyen Kale; Felix X. Yu; Daniel Niels Holtmann-Rice; Sanjiv Kumar; Pradeep Ravikumar (ICML, 2018)

The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. - Chen Dan 001; Liu Leqi; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (NeurIPS, 2018)

Connecting Optimization and Regularization Paths. - Arun Sai Suggala; Adarsh Prasad; Pradeep Ravikumar (NeurIPS, 2018)

Representer Point Selection for Explaining Deep Neural Networks. - Chih-Kuan Yeh; Joon Sik Kim; Ian En-Hsu Yen; Pradeep Ravikumar (NeurIPS, 2018)

MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. - Ian En-Hsu Yen; Wei-Cheng Lee; Kai Zhong; Sung-En Chang; Pradeep Ravikumar; Shou-De Lin (NeurIPS, 2018)

DAGs with NO TEARS: Continuous Optimization for Structure Learning. - Xun Zheng; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (NeurIPS, 2018)

Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. - Bryon Aragam; Chen Dan 001; Pradeep Ravikumar; Eric P. Xing (CoRR, 2018)

D2KE: From Distance to Kernel and Embedding. - Lingfei Wu 001; Ian En-Hsu Yen; Fangli Xu; Pradeep Ravikumar; Michael Witbrock (CoRR, 2018)

Robust Estimation via Robust Gradient Estimation. - Adarsh Prasad; Arun Sai Suggala; Sivaraman Balakrishnan; Pradeep Ravikumar (CoRR, 2018)

DAGs with NO TEARS: Smooth Optimization for Structure Learning. - Xun Zheng; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (CoRR, 2018)

Robust Nonparametric Regression under Huber's ε-contamination Model. - Simon S. Du; Yining Wang 001; Sivaraman Balakrishnan; Pradeep Ravikumar; Aarti Singh (CoRR, 2018)

On Adversarial Risk and Training. - Arun Sai Suggala; Adarsh Prasad; Vaishnavh Nagarajan; Pradeep Ravikumar (CoRR, 2018)

Sample Complexity of Nonparametric Semi-Supervised Learning. - Chen Dan 001; Liu Leqi; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (CoRR, 2018)

Learning Tensor Latent Features. - Sung-En Chang; Xun Zheng; Ian En-Hsu Yen; Pradeep Ravikumar; Rose Yu (CoRR, 2018)

Building Human-Machine Trust via Interpretability. - Umang Bhatt; Pradeep Ravikumar; José M. F. Moura (AAAI, 2019)

Revisiting Adversarial Risk. - Arun Sai Suggala; Adarsh Prasad; Vaishnavh Nagarajan; Pradeep Ravikumar (AISTATS, 2019)

Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. - Arun Sai Suggala; Kush Bhatia; Pradeep Ravikumar; Prateek Jain 002 (COLT, 2019)

Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation. - Chen Dan 001; Hong Wang; Hongyang Zhang 001; Yuchen Zhou; Pradeep Ravikumar (NeurIPS, 2019)

On Human-Aligned Risk Minimization. - Liu Leqi; Adarsh Prasad; Pradeep Ravikumar (NeurIPS, 2019)

Game Design for Eliciting Distinguishable Behavior. - Fan Yang 058; Liu Leqi; Yifan Wu; Zachary Chase Lipton; Pradeep Ravikumar; Tom M. Mitchell; William W. Cohen (NeurIPS, 2019)

On the (In)fidelity and Sensitivity of Explanations. - Chih-Kuan Yeh; Cheng-Yu Hsieh; Arun Sai Suggala; David I. Inouye; Pradeep Ravikumar (NeurIPS, 2019)

How Sensitive are Sensitivity-Based Explanations? - Chih-Kuan Yeh; Cheng-Yu Hsieh; Arun Sai Suggala; David I. Inouye; Pradeep Ravikumar (CoRR, 2019)

Towards Aggregating Weighted Feature Attributions. - Umang Bhatt; Pradeep Ravikumar; José M. F. Moura (CoRR, 2019)

A Unified Approach to Robust Mean Estimation. - Adarsh Prasad; Sivaraman Balakrishnan; Pradeep Ravikumar (CoRR, 2019)

On Concept-Based Explanations in Deep Neural Networks. - Chih-Kuan Yeh; Been Kim; Sercan Ömer Arik; Chun-Liang Li; Pradeep Ravikumar; Tomas Pfister (CoRR, 2019)

Diagnostic Curves for Black Box Models. - David I. Inouye; Liu Leqi; Joon Sik Kim; Bryon Aragam; Pradeep Ravikumar (CoRR, 2019)

A Robust Univariate Mean Estimator is All You Need. - Adarsh Prasad; Sivaraman Balakrishnan; Pradeep Ravikumar (AISTATS, 2020)

Learning Sparse Nonparametric DAGs. - Xun Zheng; Chen Dan 001; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (AISTATS, 2020)

Minimizing FLOPs to Learn Efficient Sparse Representations. - Biswajit Paria; Chih-Kuan Yeh; Ian En-Hsu Yen; Ning Xu; Pradeep Ravikumar; Barnabás Póczos (ICLR, 2020)

MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. - Runtian Zhai; Chen Dan 001; Di He 001; Huan Zhang 001; Boqing Gong; Pradeep Ravikumar; Cho-Jui Hsieh; Liwei Wang 001 (ICLR, 2020)

Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification. - Chen Dan 001; Yuting Wei 001; Pradeep Ravikumar (ICML, 2020)

Uniform Convergence of Rank-weighted Learning. - Justin Khim; Liu Leqi; Adarsh Prasad; Pradeep Ravikumar (ICML, 2020)

Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. - Elan Rosenfeld; Ezra Winston; Pradeep Ravikumar; J. Zico Kolter (ICML, 2020)

Class-Weighted Classification: Trade-offs and Robust Approaches. - Ziyu Xu 001; Chen Dan 001; Justin Khim; Pradeep Ravikumar (ICML, 2020)

Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches. - Juyong Kim 002; Linyuan Gong; Justin Khim; Jeremy C. Weiss; Pradeep Ravikumar (ML4H@NeurIPS, 2020)

On Learning Ising Models under Huber's Contamination Model. - Adarsh Prasad; Vishwak Srinivasan; Sivaraman Balakrishnan; Pradeep Ravikumar (NeurIPS, 2020)

Generalized Boosting. - Arun Sai Suggala; Bingbin Liu; Pradeep Ravikumar (NeurIPS, 2020)

On Completeness-aware Concept-Based Explanations in Deep Neural Networks. - Chih-Kuan Yeh; Been Kim; Sercan Ömer Arik; Chun-Liang Li; Tomas Pfister; Pradeep Ravikumar (NeurIPS, 2020)

Automated Dependence Plots. - David I. Inouye; Liu Leqi; Joon Sik Kim; Bryon Aragam; Pradeep Ravikumar (UAI, 2020)

Learning Minimax Estimators via Online Learning. - Kartik Gupta; Arun Sai Suggala; Adarsh Prasad; Praneeth Netrapalli; Pradeep Ravikumar (CoRR, 2020)

Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification. - Chen Dan 001; Yuting Wei 001; Pradeep Ravikumar (CoRR, 2020)

Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. - Sijie He; Xinyan Li; Timothy DelSole; Pradeep Ravikumar; Arindam Banerjee 001 (AAAI, 2021)

Improving Compositional Generalization in Classification Tasks via Structure Annotations. - Juyong Kim 002; Pradeep Ravikumar; Joshua Ainslie; Santiago Ontañón (ACL/IJCNLP (2), 2021)

Contrastive learning of strong-mixing continuous-time stochastic processes. - Bingbin Liu; Pradeep Ravikumar; Andrej Risteski (AISTATS, 2021)

Efficient Bandit Convex Optimization: Beyond Linear Losses. - Arun Sai Suggala; Pradeep Ravikumar; Praneeth Netrapalli (COLT, 2021)

Evaluations and Methods for Explanation through Robustness Analysis. - Cheng-Yu Hsieh; Chih-Kuan Yeh; Xuanqing Liu; Pradeep Kumar Ravikumar; Seungyeon Kim 001; Sanjiv Kumar; Cho-Jui Hsieh (ICLR, 2021)

The Risks of Invariant Risk Minimization. - Elan Rosenfeld; Pradeep Kumar Ravikumar; Andrej Risteski (ICLR, 2021)

On Proximal Policy Optimization's Heavy-tailed Gradients. - Saurabh Garg; Joshua Zhanson; Emilio Parisotto; Adarsh Prasad; J. Zico Kolter; Zachary C. Lipton; Sivaraman Balakrishnan; Ruslan Salakhutdinov; Pradeep Ravikumar (ICML, 2021)

DORO: Distributional and Outlier Robust Optimization. - Runtian Zhai; Chen Dan 001; J. Zico Kolter; Pradeep Ravikumar (ICML, 2021)

Learning latent causal graphs via mixture oracles. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2021)

When Is Generalizable Reinforcement Learning Tractable? - Dhruv Malik; Yuanzhi Li; Pradeep Ravikumar (NeurIPS, 2021)

Boosted CVaR Classification. - Runtian Zhai; Chen Dan 001; Arun Sai Suggala; J. Zico Kolter; Pradeep Ravikumar (NeurIPS, 2021)

Subseasonal climate prediction in the western US using Bayesian spatial models. - Vishwak Srinivasan; Justin Khim; Arindam Banerjee 001; Pradeep Ravikumar (UAI, 2021)

Iterative Barycenter Flows. - David I. Inouye; Zeyu Zhou; Ziyu Gong; Pradeep Ravikumar (CoRR, 2021)

Human-Centered Concept Explanations for Neural Networks. - Chih-Kuan Yeh; Been Kim; Pradeep Ravikumar (Neuro-Symbolic Artificial Intelligence, 2021)

An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. - Elan Rosenfeld; Pradeep Ravikumar; Andrej Risteski (AISTATS, 2022)

Heavy-tailed Streaming Statistical Estimation. - Che-Ping Tsai; Adarsh Prasad; Sivaraman Balakrishnan; Pradeep Ravikumar (AISTATS, 2022)

Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. - Chih-Kuan Yeh; Kuan-Yun Lee; Frederick Liu; Pradeep Ravikumar (AISTATS, 2022)

Iterative Alignment Flows. - Zeyu Zhou; Ziyu Gong; Pradeep Ravikumar; David I. Inouye (AISTATS, 2022)

Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence. - Juyong Kim 002; Jeremy C. Weiss; Pradeep Ravikumar (CHIL, 2022)

AnEMIC: A Framework for Benchmarking ICD Coding Models. - Juyong Kim 002; Abheesht Sharma; Suhas Shanbhogue; Jeremy C. Weiss; Pradeep Ravikumar (EMNLP (Demos), 2022)

Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. - Bingbin Liu; Elan Rosenfeld; Pradeep Kumar Ravikumar; Andrej Risteski (ICLR, 2022)

FILM: Following Instructions in Language with Modular Methods. - So Yeon Min; Devendra Singh Chaplot; Pradeep Kumar Ravikumar; Yonatan Bisk; Ruslan Salakhutdinov (ICLR, 2022)

Building Robust Ensembles via Margin Boosting. - Dinghuai Zhang; Hongyang Zhang 001; Aaron C. Courville; Yoshua Bengio; Pradeep Ravikumar; Arun Sai Suggala (ICML, 2022)

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. - Kevin Bello; Bryon Aragam; Pradeep Ravikumar (NeurIPS, 2022)

Identifiability of deep generative models without auxiliary information. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2022)

Masked Prediction: A Parameter Identifiability View. - Bingbin Liu; Daniel J. Hsu; Pradeep Ravikumar; Andrej Risteski (NeurIPS, 2022)

First is Better Than Last for Language Data Influence. - Chih-Kuan Yeh; Ankur Taly; Mukund Sundararajan; Frederick Liu; Pradeep Ravikumar (NeurIPS, 2022)

Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization. - Elan Rosenfeld; Pradeep Ravikumar; Andrej Risteski (CoRR, 2022)

Masked prediction tasks: a parameter identifiability view. - Bingbin Liu; Daniel Hsu 001; Pradeep Ravikumar; Andrej Risteski (CoRR, 2022)

First is Better Than Last for Training Data Influence. - Chih-Kuan Yeh; Ankur Taly; Mukund Sundararajan; Frederick Liu; Pradeep Ravikumar (CoRR, 2022)

Human-Centered Concept Explanations for Neural Networks. - Chih-Kuan Yeh; Been Kim; Pradeep Ravikumar (CoRR, 2022)

Identifiability of deep generative models under mixture priors without auxiliary information. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (CoRR, 2022)

Fundamental Limits and Tradeoffs in Invariant Representation Learning. - Han Zhao 002; Chen Dan 001; Bryon Aragam; Tommi S. Jaakkola; Geoffrey J. Gordon; Pradeep Ravikumar (J. Mach. Learn. Res., 2022)

Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. - Maria-Florina Balcan; Rattana Pukdee; Pradeep Ravikumar; Hongyang Zhang 001 (AISTATS, 2023)

Individual Fairness Under Uncertainty. - Wenbin Zhang 002; Zichong Wang; Juyong Kim 002; Cheng Cheng 001; Thomas Oommen; Pradeep Ravikumar; Jeremy C. Weiss (ECAI, 2023)

Concept Gradient: Concept-based Interpretation Without Linear Assumption. - Andrew Bai; Chih-Kuan Yeh; Neil Y. C. Lin; Pradeep Kumar Ravikumar; Cho-Jui Hsieh (ICLR, 2023)

Label Propagation with Weak Supervision. - Rattana Pukdee; Dylan Sam; Pradeep Kumar Ravikumar; Nina Balcan (ICLR, 2023)

Understanding Why Generalized Reweighting Does Not Improve Over ERM. - Runtian Zhai; Chen Dan 001; J. Zico Kolter; Pradeep Kumar Ravikumar (ICLR, 2023)

Optimizing NOTEARS Objectives via Topological Swaps. - Chang Deng; Kevin Bello; Bryon Aragam; Pradeep Kumar Ravikumar (ICML, 2023)

Representer Point Selection for Explaining Regularized High-dimensional Models. - Che-Ping Tsai; Jiong Zhang; Hsiang-Fu Yu; Eli Chien; Cho-Jui Hsieh; Pradeep Kumar Ravikumar (ICML, 2023)

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. - Simon Buchholz; Goutham Rajendran; Elan Rosenfeld; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (NeurIPS, 2023)

iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. - Tianyu Chen; Kevin Bello; Bryon Aragam; Pradeep Ravikumar (NeurIPS, 2023)

Global Optimality in Bivariate Gradient-based DAG Learning. - Chang Deng; Kevin Bello; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2023)

Responsible AI (RAI) Games and Ensembles. - Yash Gupta; Runtian Zhai; Arun Sai Suggala; Pradeep Ravikumar (NeurIPS, 2023)

Learning with Explanation Constraints. - Rattana Pukdee; Dylan Sam; J. Zico Kolter; Maria-Florina Balcan; Pradeep Ravikumar (NeurIPS, 2023)

Sample based Explanations via Generalized Representers. - Che-Ping Tsai; Chih-Kuan Yeh; Pradeep Ravikumar (NeurIPS, 2023)

Individual Fairness Guarantee in Learning with Censorship. - Wenbin Zhang 002; Juyong Kim 002; Zichong Wang; Pradeep Ravikumar; Jeremy C. Weiss (CoRR, 2023)

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation. - Runtian Zhai; Bingbin Liu; Andrej Risteski; Zico Kolter; Pradeep Ravikumar (CoRR, 2023)

Faith-Shap: The Faithful Shapley Interaction Index. - Che-Ping Tsai; Chih-Kuan Yeh; Pradeep Ravikumar (J. Mach. Learn. Res., 2023)

Neuro-Causal Models. - Bryon Aragam; Pradeep Ravikumar (Compendium of Neurosymbolic Artificial Intelligence, 2023)

An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. - Goutham Rajendran; Patrik Reizinger; Wieland Brendel; Pradeep Kumar Ravikumar (CLeaR, 2024)

Identifying Representations for Intervention Extrapolation. - Sorawit Saengkyongam; Elan Rosenfeld; Pradeep Kumar Ravikumar; Niklas Pfister; Jonas Peters (ICLR, 2024)

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression. - Runtian Zhai; Bingbin Liu; Andrej Risteski; J. Zico Kolter; Pradeep Kumar Ravikumar (ICLR, 2024)

Spectrally Transformed Kernel Regression. - Runtian Zhai; Rattana Pukdee; Roger Jin; Maria-Florina Balcan; Pradeep Kumar Ravikumar (ICLR, 2024)

On the Origins of Linear Representations in Large Language Models. - Yibo Jiang; Goutham Rajendran; Pradeep Kumar Ravikumar; Bryon Aragam; Victor Veitch (ICML, 2024)

Identifying General Mechanism Shifts in Linear Causal Representations. - Tianyu Chen; Kevin Bello; Francesco Locatello; Bryon Aragam; Pradeep Ravikumar (NeurIPS, 2024)

Markov Equivalence and Consistency in Differentiable Structure Learning. - Chang Deng; Kevin Bello; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2024)

Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. - Yibo Jiang; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2024)

LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation. - Bowen Li 007; Zhaoyu Li; Qiwei Du; Jinqi Luo; Wenshan Wang; Yaqi Xie 001; Simon Stepputtis; Chen Wang 033; Katia P. Sycara; Pradeep Ravikumar; Alexander G. Gray; Xujie Si; et al. (NeurIPS, 2024)

From Causal to Concept-Based Representation Learning. - Goutham Rajendran; Simon Buchholz; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (NeurIPS, 2024)

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. - Goutham Rajendran; Simon Buchholz; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (CoRR, 2024)

Likelihood-based Differentiable Structure Learning. - Chang Deng; Kevin Bello; Pradeep Ravikumar; Bryon Aragam (CoRR, 2024)

On the Consistent Recovery of Joint Distributions from Conditionals. - Mahbod Majid; Rattana Pukdee; Vishwajeet Agrawal; Burak Varici; Pradeep Kumar Ravikumar (AISTATS, 2025)

Learning from weak labelers as constraints. - Vishwajeet Agrawal; Rattana Pukdee; Maria-Florina Balcan; Pradeep Kumar Ravikumar (ICLR, 2025)

Knowledge-Enriched Machine Learning for Tabular Data. - Juyong Kim 002; Chandler Squires; Pradeep Ravikumar (NeuS, 2025)

LLM-Select: Feature Selection with Large Language Models. - Daniel P. Jeong; Zachary Chase Lipton; Pradeep Kumar Ravikumar (Trans. Mach. Learn. Res., 2025)

Contextures: Representations from Contexts. - Runtian Zhai; Kai Yang; Che-Ping Tsai; Burak Varici; Zico Kolter; Pradeep Ravikumar (CoRR, 2025)