Pradeep Ravikumar

Affiliations:
  • University of Texas in Austin, Department of Computer Science, USA
  • University of California, Berkeley, Department of Statistics, USA
  • Carnegie Mellon University, School of Computer Science, USA


According to our database1, Pradeep Ravikumar authored at least 193 papers between 2003 and 2024.

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Bibliography

2024
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation.
CoRR, 2024

Identifying General Mechanism Shifts in Linear Causal Representations.
CoRR, 2024

Likelihood-based Differentiable Structure Learning.
CoRR, 2024

LLM-Select: Feature Selection with Large Language Models.
CoRR, 2024

Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers.
CoRR, 2024

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models.
CoRR, 2024

On the Origins of Linear Representations in Large Language Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Spectrally Transformed Kernel Regression.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Identifying Representations for Intervention Extrapolation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis.
Proceedings of the Causal Learning and Reasoning, 2024

2023
Neuro-Causal Models.
Proceedings of the Compendium of Neurosymbolic Artificial Intelligence, 2023

Faith-Shap: The Faithful Shapley Interaction Index.
J. Mach. Learn. Res., 2023

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation.
CoRR, 2023

Individual Fairness Guarantee in Learning with Censorship.
CoRR, 2023

Sample based Explanations via Generalized Representers.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning with Explanation Constraints.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Responsible AI (RAI) Games and Ensembles.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Global Optimality in Bivariate Gradient-based DAG Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Representer Point Selection for Explaining Regularized High-dimensional Models.
Proceedings of the International Conference on Machine Learning, 2023

Optimizing NOTEARS Objectives via Topological Swaps.
Proceedings of the International Conference on Machine Learning, 2023

Understanding Why Generalized Reweighting Does Not Improve Over ERM.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Label Propagation with Weak Supervision.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Concept Gradient: Concept-based Interpretation Without Linear Assumption.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Individual Fairness Under Uncertainty.
Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Fundamental Limits and Tradeoffs in Invariant Representation Learning.
J. Mach. Learn. Res., 2022

Identifiability of deep generative models under mixture priors without auxiliary information.
CoRR, 2022

Human-Centered Concept Explanations for Neural Networks.
CoRR, 2022

First is Better Than Last for Training Data Influence.
CoRR, 2022

Masked prediction tasks: a parameter identifiability view.
CoRR, 2022

Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization.
CoRR, 2022

First is Better Than Last for Language Data Influence.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Masked Prediction: A Parameter Identifiability View.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Identifiability of deep generative models without auxiliary information.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Building Robust Ensembles via Margin Boosting.
Proceedings of the International Conference on Machine Learning, 2022

FILM: Following Instructions in Language with Modular Methods.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation.
Proceedings of the Tenth International Conference on Learning Representations, 2022

AnEMIC: A Framework for Benchmarking ICD Coding Models.
Proceedings of the The 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Iterative Alignment Flows.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Heavy-tailed Streaming Statistical Estimation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Human-Centered Concept Explanations for Neural Networks.
Proceedings of the Neuro-Symbolic Artificial Intelligence: The State of the Art, 2021

Iterative Barycenter Flows.
CoRR, 2021

Subseasonal climate prediction in the western US using Bayesian spatial models.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Boosted CVaR Classification.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

When Is Generalizable Reinforcement Learning Tractable?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning latent causal graphs via mixture oracles.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

DORO: Distributional and Outlier Robust Optimization.
Proceedings of the 38th International Conference on Machine Learning, 2021

On Proximal Policy Optimization's Heavy-tailed Gradients.
Proceedings of the 38th International Conference on Machine Learning, 2021

The Risks of Invariant Risk Minimization.
Proceedings of the 9th International Conference on Learning Representations, 2021

Evaluations and Methods for Explanation through Robustness Analysis.
Proceedings of the 9th International Conference on Learning Representations, 2021

Efficient Bandit Convex Optimization: Beyond Linear Losses.
Proceedings of the Conference on Learning Theory, 2021

Contrastive learning of strong-mixing continuous-time stochastic processes.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Improving Compositional Generalization in Classification Tasks via Structure Annotations.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification.
CoRR, 2020

Learning Minimax Estimators via Online Learning.
CoRR, 2020

Automated Dependence Plots.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

On Completeness-aware Concept-Based Explanations in Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generalized Boosting.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Learning Ising Models under Huber's Contamination Model.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches.
Proceedings of the Machine Learning for Health Workshop, 2020

Class-Weighted Classification: Trade-offs and Robust Approaches.
Proceedings of the 37th International Conference on Machine Learning, 2020

Certified Robustness to Label-Flipping Attacks via Randomized Smoothing.
Proceedings of the 37th International Conference on Machine Learning, 2020

Uniform Convergence of Rank-weighted Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification.
Proceedings of the 37th International Conference on Machine Learning, 2020

MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius.
Proceedings of the 8th International Conference on Learning Representations, 2020

Minimizing FLOPs to Learn Efficient Sparse Representations.
Proceedings of the 8th International Conference on Learning Representations, 2020

Learning Sparse Nonparametric DAGs.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A Robust Univariate Mean Estimator is All You Need.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Diagnostic Curves for Black Box Models.
CoRR, 2019

On Concept-Based Explanations in Deep Neural Networks.
CoRR, 2019

A Unified Approach to Robust Mean Estimation.
CoRR, 2019

Towards Aggregating Weighted Feature Attributions.
CoRR, 2019

How Sensitive are Sensitivity-Based Explanations?
CoRR, 2019

On the (In)fidelity and Sensitivity of Explanations.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Game Design for Eliciting Distinguishable Behavior.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On Human-Aligned Risk Minimization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression.
Proceedings of the Conference on Learning Theory, 2019

Revisiting Adversarial Risk.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Building Human-Machine Trust via Interpretability.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Learning Tensor Latent Features.
CoRR, 2018

Sample Complexity of Nonparametric Semi-Supervised Learning.
CoRR, 2018

On Adversarial Risk and Training.
CoRR, 2018

Robust Nonparametric Regression under Huber's ε-contamination Model.
CoRR, 2018

DAGs with NO TEARS: Smooth Optimization for Structure Learning.
CoRR, 2018

Robust Estimation via Robust Gradient Estimation.
CoRR, 2018

D2KE: From Distance to Kernel and Embedding.
CoRR, 2018

Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering.
CoRR, 2018

DAGs with NO TEARS: Continuous Optimization for Structure Learning.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Representer Point Selection for Explaining Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Connecting Optimization and Regularization Paths.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Loss Decomposition for Fast Learning in Large Output Spaces.
Proceedings of the 35th International Conference on Machine Learning, 2018

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics.
Proceedings of the 35th International Conference on Machine Learning, 2018

Deep Density Destructors.
Proceedings of the 35th International Conference on Machine Learning, 2018

Word Mover's Embedding: From Word2Vec to Document Embedding.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

A Voting-Based System for Ethical Decision Making.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Cost-Sensitive Learning with Noisy Labels.
J. Mach. Learn. Res., 2017

The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Latent Feature Lasso.
Proceedings of the 34th International Conference on Machine Learning, 2017

Ordinal Graphical Models: A Tale of Two Approaches.
Proceedings of the 34th International Conference on Machine Learning, 2017

Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization.
Proceedings of the 34th International Conference on Machine Learning, 2017

Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Minimax Gaussian Classification & Clustering.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Kernel Ridge Regression via Partitioning.
CoRR, 2016

XMRF: an R package to fit Markov Networks to high-throughput genetics data.
BMC Syst. Biol., 2016

Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery.
Proceedings of the 33nd International Conference on Machine Learning, 2016

PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Optimal Classification with Multivariate Losses.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Graphical models via univariate exponential family distributions.
J. Mach. Learn. Res., 2015

Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics.
CoRR, 2015

Tracking with ranked signals.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Learning-based analytical cross-platform performance prediction.
Proceedings of the 2015 International Conference on Embedded Computer Systems: Architectures, 2015

Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Closed-form Estimators for High-dimensional Generalized Linear Models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Collaborative Filtering with Graph Information: Consistency and Scalable Methods.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Classification Rates for High-dimensional Gaussian Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Consistent Multilabel Classification.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Vector-Space Markov Random Fields via Exponential Families.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Distributional Rank Aggregation, and an Axiomatic Analysis.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Sparsistency of 1-Regularized M-Estimators.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
QUIC: quadratic approximation for sparse inverse covariance estimation.
J. Mach. Learn. Res., 2014

Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Elementary Estimators for Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

On the Information Theoretic Limits of Learning Ising Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

A Representation Theory for Ranking Functions.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Consistent Binary Classification with Generalized Performance Metrics.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments.
Proceedings of the 31th International Conference on Machine Learning, 2014

Elementary Estimators for High-Dimensional Linear Regression.
Proceedings of the 31th International Conference on Machine Learning, 2014

Learning Graphs with a Few Hubs.
Proceedings of the 31th International Conference on Machine Learning, 2014

Admixture of Poisson MRFs: A Topic Model with Word Dependencies.
Proceedings of the 31th International Conference on Machine Learning, 2014

Exponential Family Matrix Completion under Structural Constraints.
Proceedings of the 31th International Conference on Machine Learning, 2014

Mixed Graphical Models via Exponential Families.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
A Dirty Model for Multiple Sparse Regression.
IEEE Trans. Inf. Theory, 2013

On Poisson Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Conditional Random Fields via Univariate Exponential Families.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Dirty Statistical Models.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Large Scale Distributed Sparse Precision Estimation.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Learning with Noisy Labels.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

On the difficulty of learning power law graphical models.
Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

On Robust Estimation of High Dimensional Generalized Linear Models.
Proceedings of the IJCAI 2013, 2013

Human Boosting.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization.
IEEE Trans. Inf. Theory, 2012

Perturbation based Large Margin Approach for Ranking.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Graphical Models via Generalized Linear Models.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
On NDCG Consistency of Listwise Ranking Methods.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

On Learning Discrete Graphical Models using Group-Sparse Regularization.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Greedy Algorithms for Structurally Constrained High Dimensional Problems.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

On Learning Discrete Graphical Models using Greedy Methods.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Nearest Neighbor based Greedy Coordinate Descent.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

On the Use of Variational Inference for Learning Discrete Graphical Model.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes.
J. Mach. Learn. Res., 2010

A Dirty Model for Multi-task Learning.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Information-theoretic lower bounds on the oracle complexity of convex optimization.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Error-Correcting Tournaments.
Proceedings of the Algorithmic Learning Theory, 20th International Conference, 2009

2008
Approximate inference, structure learning and feature estimation in Markov random fields: thesis abstract.
SIGKDD Explor., 2008

Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l<sub>1</sub>-regularized MLE.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes.
Proceedings of the Machine Learning, 2008

2007
SpAM: Sparse Additive Models.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
High-Dimensional Graphical Model Selection Using ℓ<sub>1</sub>-Regularized Logistic Regression.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Quadratic programming relaxations for metric labeling and Markov random field MAP estimation.
Proceedings of the Machine Learning, 2006

2005
Preconditioner Approximations for Probabilistic Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004
Variational Chernoff Bounds for Graphical Models.
Proceedings of the UAI '04, 2004

A Hierarchical Graphical Model for Record Linkage.
Proceedings of the UAI '04, 2004

2003
Adaptive Name Matching in Information Integration.
IEEE Intell. Syst., 2003

A Comparison of String Distance Metrics for Name-Matching Tasks.
Proceedings of IJCAI-03 Workshop on Information Integration on the Web (IIWeb-03), 2003


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