Pradeep

Pradeep Ravikumar

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Pradeep Ravikumar
Computer Science
#7965
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#8381
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Computational Linguistics
#1694
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#1712
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Machine Learning
#3129
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#3168
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Artificial Intelligence
#3428
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#3478
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Pradeep Ravikumar's Published Works

Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
198019902000201020200250500750100012501500175020002250

Published Works

  • A Comparison of String Distance Metrics for Name-Matching Tasks (2003) (1691)
  • A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers (2009) (1252)
  • Learning with Noisy Labels (2013) (923)
  • High-dimensional Ising model selection using ℓ1-regularized logistic regression (2010) (890)
  • High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence (2008) (784)
  • Adaptive Name Matching in Information Integration (2003) (559)
  • Sparse additive models (2007) (525)
  • Latent Variable Models (1999) (451)
  • DAGs with NO TEARS: Continuous Optimization for Structure Learning (2018) (413)
  • A Dirty Model for Multi-task Learning (2010) (376)
  • Sparse inverse covariance matrix estimation using quadratic approximation (2011) (348)
  • Collaborative Filtering with Graph Information: Consistency and Scalable Methods (2015) (246)
  • Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization (2010) (230)
  • On the (In)fidelity and Sensitivity of Explanations (2019) (230)
  • High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression (2006) (200)
  • SpAM: Sparse Additive Models (2007) (191)
  • BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables (2013) (191)
  • QUIC: quadratic approximation for sparse inverse covariance estimation (2014) (180)
  • Robust estimation via robust gradient estimation (2018) (178)
  • Information-theoretic lower bounds on the oracle complexity of convex optimization (2009) (171)
  • Representer Point Selection for Explaining Deep Neural Networks (2018) (167)
  • The Risks of Invariant Risk Minimization (2020) (165)
  • PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification (2016) (162)
  • Graphical models via univariate exponential family distributions (2013) (150)
  • Learning Sparse Nonparametric DAGs (2019) (145)
  • Consistent Binary Classification with Generalized Performance Metrics (2014) (139)
  • Graphical Models via Generalized Linear Models (2012) (137)
  • Quadratic programming relaxations for metric labeling and Markov random field MAP estimation (2006) (129)
  • A Voting-Based System for Ethical Decision Making (2017) (129)
  • On Completeness-aware Concept-Based Explanations in Deep Neural Networks (2019) (121)
  • PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification (2017) (120)
  • A Hierarchical Graphical Model for Record Linkage (2004) (118)
  • Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE (2008) (116)
  • Greedy Algorithms for Structurally Constrained High Dimensional Problems (2011) (113)
  • MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius (2020) (110)
  • On Learning Discrete Graphical Models using Greedy Methods (2011) (102)
  • Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of boldmathell_1-regularized MLE (2008) (101)
  • Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes (2010) (101)
  • Mixed Graphical Models via Exponential Families (2014) (95)
  • On Learning Discrete Graphical Models using Group-Sparse Regularization (2011) (93)
  • Word Mover’s Embedding: From Word2Vec to Document Embedding (2018) (88)
  • On NDCG Consistency of Listwise Ranking Methods (2011) (87)
  • Error-Correcting Tournaments (2009) (83)
  • A review of multivariate distributions for count data derived from the Poisson distribution (2016) (81)
  • Certified Robustness to Label-Flipping Attacks via Randomized Smoothing (2020) (80)
  • Consistent Multilabel Classification (2015) (73)
  • Nearest Neighbor based Greedy Coordinate Descent (2011) (66)
  • On Poisson Graphical Models (2013) (65)
  • FILM: Following Instructions in Language with Modular Methods (2021) (60)
  • High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods (2011) (58)
  • Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space (2014) (54)
  • Cost-Sensitive Learning with Noisy Labels (2017) (48)
  • A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation (2012) (48)
  • A Dirty Model for Multiple Sparse Regression (2011) (43)
  • ENCODING AND DECODING V1 FMRI RESPONSES TO NATURAL IMAGES WITH SPARSE NONPARAMETRIC MODELS. (2011) (42)
  • Connecting Optimization and Regularization Paths (2018) (40)
  • Dirty Statistical Models (2013) (38)
  • Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes (2008) (37)
  • Exponential Family Matrix Completion under Structural Constraints (2014) (37)
  • Large Scale Distributed Sparse Precision Estimation (2013) (37)
  • On the Information Theoretic Limits of Learning Ising Models (2014) (36)
  • A Dirty Model for Multitask Learning ( Appendices ) (2010) (36)
  • Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression (2019) (35)
  • Admixture of Poisson MRFs: A Topic Model with Word Dependencies (2014) (34)
  • Evaluations and Methods for Explanation through Robustness Analysis (2019) (31)
  • Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs (2015) (30)
  • Learning-based analytical cross-platform performance prediction (2015) (29)
  • Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization (2022) (29)
  • Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification (2020) (28)
  • Minimizing FLOPs to Learn Efficient Sparse Representations (2020) (28)
  • Elementary Estimators for Graphical Models (2014) (28)
  • D2KE: From Distance to Kernel and Embedding (2018) (27)
  • Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators (2014) (26)
  • Binary Classification with Karmic, Threshold-Quasi-Concave Metrics (2018) (25)
  • Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent (2015) (25)
  • Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings (2014) (24)
  • Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering (2018) (24)
  • DORO: Distributional and Outlier Robust Optimization (2021) (24)
  • Elementary Estimators for High-Dimensional Linear Regression (2014) (24)
  • A Unified Approach to Robust Mean Estimation (2019) (22)
  • On Concept-Based Explanations in Deep Neural Networks (2019) (22)
  • How Sensitive are Sensitivity-Based Explanations? (2019) (21)
  • Class-Weighted Classification: Trade-offs and Robust Approaches (2020) (21)
  • An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization (2021) (20)
  • Elementary Estimators for Sparse Covariance Matrices and other Structured Moments (2014) (20)
  • Vector-Space Markov Random Fields via Exponential Families (2015) (19)
  • A Robust Univariate Mean Estimator is All You Need (2020) (19)
  • XMRF: an R package to fit Markov Networks to high-throughput genetics data (2015) (19)
  • Predicting Growth Conditions from Internal Metabolic Fluxes in an In-Silico Model of E. coli (2014) (19)
  • Sparsistency of 1-Regularized M-Estimators (2015) (18)
  • A General Framework for Mixed Graphical Models (2014) (18)
  • Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances (2020) (17)
  • Loss Decomposition for Fast Learning in Large Output Spaces (2018) (17)
  • A Representation Theory for Ranking Functions (2014) (16)
  • Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies (2016) (16)
  • On the difficulty of learning power law graphical models (2013) (15)
  • Kernel Ridge Regression via Partitioning (2016) (15)
  • Deep Density Destructors (2018) (15)
  • The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities (2017) (15)
  • Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization (2017) (14)
  • Shape-based image reconstruction using linearized deformations (2017) (14)
  • Revisiting Adversarial Risk (2018) (14)
  • April 16 (1972) (14)
  • Uniform Convergence of Rank-weighted Learning (2020) (14)
  • Variational Chernoff Bounds for Graphical Models (2004) (14)
  • Learning latent causal graphs via mixture oracles (2021) (13)
  • A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery (2016) (13)
  • On Human-Aligned Risk Minimization (2019) (13)
  • A divide-and-conquer procedure for sparse inverse covariance estimation (2012) (12)
  • Fundamental Limits and Tradeoffs in Invariant Representation Learning (2020) (12)
  • On Learning Ising Models under Huber's Contamination Model (2020) (12)
  • Human-Centered Concept Explanations for Neural Networks (2021) (11)
  • On the Use of Variational Inference for Learning Discrete Graphical Model (2011) (11)
  • Distributional Rank Aggregation, and an Axiomatic Analysis (2015) (11)
  • Building Human-Machine Trust via Interpretability (2019) (11)
  • On Adversarial Risk and Training (2018) (10)
  • Latent Feature Lasso (2017) (10)
  • Towards Aggregating Weighted Feature Attributions (2019) (10)
  • Minimax Gaussian Classification & Clustering (2017) (10)
  • QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models (2014) (10)
  • Conditional Random Fields via Univariate Exponential Families (2013) (10)
  • Fast Classification Rates for High-dimensional Gaussian Generative Models (2015) (9)
  • Understanding Why Generalized Reweighting Does Not Improve Over ERM (2022) (9)
  • Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs (2014) (9)
  • A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models (2015) (9)
  • Faith-Shap: The Faithful Shapley Interaction Index (2022) (9)
  • When Is Generalizable Reinforcement Learning Tractable? (2021) (9)
  • Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain (2016) (8)
  • Ordinal Graphical Models: A Tale of Two Approaches (2017) (8)
  • Improving Compositional Generalization in Classification Tasks via Structure Annotations (2021) (8)
  • Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation (2019) (8)
  • Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images (2008) (8)
  • Learning Graphs with a Few Hubs (2014) (8)
  • Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial (2015) (7)
  • Approximate inference, structure learning and feature estimation in Markov random fields: thesis abstract (2008) (7)
  • Robust Nonparametric Regression under Huber's ε-contamination Model (2018) (7)
  • Optimal Classification with Multivariate Losses (2016) (7)
  • Boosted CVaR Classification (2021) (7)
  • Hyperparameter Selection under Localized Label Noise via Corrupt Validation (2017) (7)
  • Identifiability of deep generative models under mixture priors without auxiliary information (2022) (7)
  • Human Boosting (2013) (7)
  • Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain (2017) (6)
  • On Proximal Policy Optimization's Heavy-tailed Gradients (2021) (6)
  • Subseasonal climate prediction in the western US using Bayesian spatial models (2021) (6)
  • {Sparsistency of \ell_1-Regularized M-Estimators} (2014) (5)
  • Closed-form Estimators for High-dimensional Generalized Linear Models (2015) (5)
  • Building Robust Ensembles via Margin Boosting (2022) (4)
  • Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics (2015) (4)
  • Generalized Boosting (2020) (4)
  • On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models (2017) (4)
  • DAGs with NO TEARS: Smooth Optimization for Structure Learning (2018) (4)
  • Efficient Bandit Convex Optimization: Beyond Linear Losses (2021) (4)
  • The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models (2018) (4)
  • [The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers]: Comments (2010) (4)
  • Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation (2021) (4)
  • Heavy-tailed Streaming Statistical Estimation (2021) (4)
  • Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games (2022) (3)
  • Information-theoretic lower bounds on the oracle complexity of sparse convex optimization (2010) (3)
  • Objective Criteria For Explanations of Machine Learning Models (2021) (3)
  • DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization (2022) (3)
  • First is Better Than Last for Training Data Influence (2022) (3)
  • Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations (2022) (2)
  • DEEP-TRIM: REVISITING L1 REGULARIZATION FOR CONNECTION PRUNING OF DEEP NETWORK (2018) (2)
  • MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization (2018) (2)
  • Contrastive learning of strong-mixing continuous-time stochastic processes (2021) (2)
  • Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families (2016) (2)
  • Appendix : Proximal Quasi-Newton for Computationally Intensive ` 1-regularized M-estimators (2014) (2)
  • Identifiability of deep generative models without auxiliary information (2022) (2)
  • First is Better Than Last for Language Data Influence (2022) (2)
  • Individual Fairness Guarantee in Learning with Censorship (2023) (2)
  • Virus Dynamics on Starlike Graphs (2011) (2)
  • Regularized sparse inverse covariance matrix estimation (2012) (2)
  • Concept Gradient: Concept-based Interpretation Without Linear Assumption (2022) (1)
  • Diagnostic Curves for Black Box Models (2019) (1)
  • Robust Nonparametric Regression under Huber's $\epsilon$-contamination Model (2018) (1)
  • Online Classification with Complex Metrics (2016) (1)
  • Iterative Alignment Flows (2021) (1)
  • Tracking with ranked signals (2015) (1)
  • Learning Tensor Latent Features (2018) (1)
  • Preconditioner Approximations for Probabilistic Graphical Models (2005) (1)
  • Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence (2022) (1)
  • Label Propagation with Weak Supervision (2022) (1)
  • Predicting bacterial growth conditions from bacterial physiology (2014) (1)
  • Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing (2019) (1)
  • AnEMIC: A Framework for Benchmarking ICD Coding Models (2022) (1)
  • On Robust Estimation of High Dimensional Generalized Linear Models (2013) (1)
  • Iterative Barycenter Flows (2021) (1)
  • Learning Minimax Estimators via Online Learning (2020) (1)
  • Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches (2020) (1)
  • Perturbation based Large Margin Approach for Ranking (2012) (1)
  • Masked prediction tasks: a parameter identifiability view (2022) (1)
  • Optimal Statistical Guaratees for Adversarially Robust Gaussian Classification (2020) (1)
  • Masked Prediction: A Parameter Identifiability View (2022) (1)
  • A new class of ranking functions for DCG-like evaluation metrics using conditional probability models October 29 , 2010 (2014) (0)
  • Nonparametric Density Estimation 10716, Spring 2020 (2019) (0)
  • M L ] 2 3 M ar 2 01 9 Revisiting Adversarial Risk (2019) (0)
  • Exact Inference: Variable Elimination 10708, Fall 2020 (2020) (0)
  • Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition (2017) (0)
  • Chain Graphical Models 10708 , Fall 2020 (2020) (0)
  • 2 Exponential Family Via Maximum Entropy (2011) (0)
  • Sample Complexity of Nonparametric Semi-Supervised Learning (2018) (0)
  • Nonparametric Regression 10716 , Spring 2020 (2019) (0)
  • Overleaf Example (2021) (0)
  • M L ] 2 5 M ay 2 01 8 D 2 KE : From Distance to Kernel and Embedding (2018) (0)
  • ROBUSTNESS AND SYNTHESIS OF EARTH SYSTEM MODELS ( ESMS ) : A MULTITASK LEARNING PERSPECTIVE (2017) (0)
  • Comments (2005) (0)
  • Graphical Models : The Why 10708 , Fall 2020 (2020) (0)
  • DSS 2018 Foreword (2018) (0)
  • Approximate Inference, Variational Viewpoint: Upper Bounds, Mean Field 10708 (2020) (0)
  • Learning from Noisy Pairwise Similarity and Unlabeled Data (2022) (0)
  • Learning Graphs with a Few Hubs - Supplementary (2010) (0)
  • Efficient Tensor Decomposition with Boolean Factors. (2018) (0)
  • D2KE: From Distance to Kernel and Embedding via Random Features For Structured Inputs (2018) (0)
  • Automated Dependence Plots (2019) (0)
  • 3 Sampling from PGMs 3 . 1 Sampling from DGMs (2020) (0)
  • M L ] 1 5 S ep 2 01 5 Exponential Family Matrix Completion under Structural Con straints (2018) (0)
  • Exact Inference: Junction Trees (2020) (0)
  • Nonparametric Bayesian Methods 10716, Spring 2020 (2020) (0)
  • PGMs as Exponential Families 10708 , Fall 2020 (2020) (0)
  • Supplementary Material 6 Auxiliary Lemmas : Proof of Lemma 3 Proof (2011) (0)
  • Nonparametric Classification 10716 , Spring 2020 (2019) (0)
  • Causal Inference 10716 , Spring 2020 (2020) (0)
  • VIRUS DYNAMICS IN STAR GRAPHS – DRAFT – DO NOT DISTRIBUTE (2011) (0)
  • UGMs : Markov Properties 10708 , Fall 2020 (2020) (0)
  • M L ] 2 0 N ov 2 01 8 Revisiting Adversarial Risk (2018) (0)
  • Conditional Bayes Principle : Given samples X , choose a decision or action a ∈ (2020) (0)
  • A pr 2 00 8 High-Dimensional Graphical Model Selection Using l 1-Regularized Logistic Regression (2021) (0)
  • DORO: Distributional and Outlier Robust Optimization (Appendix) (2021) (0)
  • Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches (2020) (0)
  • Nonparametric Contextual Bandit Optimization via Random Approximation (2017) (0)
  • Probabilistic Graphical Models: Recap, Examples 10708, Fall 2020 (2020) (0)
  • Sparse additive models Series B Statistical methodology (2009) (0)
  • M L ] 1 J ul 2 01 9 A Unified Approach to Robust Mean Estimation (2019) (0)
  • Automated Dependency Plots (2020) (0)
  • Deep Density Estimation 10716, Spring 2020 (2020) (0)
  • Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data (2019) (0)
  • 2-2009 Sparse Additive Models (2015) (0)
  • Learning with Explanation Constraints (2023) (0)
  • Game Design for Eliciting Distinguishable Behavior (2019) (0)
  • Deep Neural Networks and Kernels 10716, Spring 2020 (2020) (0)
  • Decision Theory: Perils of the Likelihood Principle 10716, Spring 2020 (2019) (0)
  • Latent Variables, EM, Variational Inference 10708, Fall 2020 (2020) (0)
  • Variational Inference : Sum Product 10708 , Fall 2020 (2020) (0)
  • DGMs: Local Factors 10708, Fall 2020 Pradeep Ravikumar 1 DGMs: Conditional Probability based Local Factors (2020) (0)

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