More from the Same Authors

• 2022 : Teaching Algorithmic Reasoning via In-context Learning »
  Hattie Zhou · Azade Nova · aaron courville · Hugo Larochelle · Behnam Neyshabur · Hanie Sedghi
• 2022 : Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization »
  Elan Rosenfeld · Pradeep Ravikumar · Andrej Risteski
• 2022 Spotlight: Identifiability of deep generative models without auxiliary information »
  Bohdan Kivva · Goutham Rajendran · Pradeep Ravikumar · Bryon Aragam
• 2022 : MATH-AI: Toward Human-Level Mathematical Reasoning »
  Francois Charton · Noah Goodman · Behnam Neyshabur · Talia Ringer · Daniel Selsam
• 2022 : External Validity: Framework, Design, and Analysis »
  Erin Hartman
• 2022 : Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization »
  Elan Rosenfeld · Pradeep Ravikumar · Andrej Risteski
• 2022 : Teaching Algorithmic Reasoning via In-context Learning »
  Hattie Zhou · Azade Nova · aaron courville · Hugo Larochelle · Behnam Neyshabur · Hanie Sedghi
• 2022 : Length Generalization in Quantitative Reasoning »
  Behnam Neyshabur
• 2022 Workshop: Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022 »
  Shanghang Zhang · Hao Dong · Wei Pan · Pradeep Ravikumar · Vittorio Ferrari · Fisher Yu · Xin Wang · Zihan Ding
• 2022 Poster: DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization »
  Kevin Bello · Bryon Aragam · Pradeep Ravikumar
• 2022 Poster: Identifiability of deep generative models without auxiliary information »
  Bohdan Kivva · Goutham Rajendran · Pradeep Ravikumar · Bryon Aragam
• 2022 Poster: Masked Prediction: A Parameter Identifiability View »
  Bingbin Liu · Daniel Hsu · Pradeep Ravikumar · Andrej Risteski
• 2022 Poster: Exploring Length Generalization in Large Language Models »
  Cem Anil · Yuhuai Wu · Anders Andreassen · Aitor Lewkowycz · Vedant Misra · Vinay Ramasesh · Ambrose Slone · Guy Gur-Ari · Ethan Dyer · Behnam Neyshabur
• 2022 Poster: Revisiting Neural Scaling Laws in Language and Vision »
  Ibrahim Alabdulmohsin · Behnam Neyshabur · Xiaohua Zhai
• 2022 Poster: First is Better Than Last for Language Data Influence »
  Chih-Kuan Yeh · Ankur Taly · Mukund Sundararajan · Frederick Liu · Pradeep Ravikumar
• 2022 Poster: Solving Quantitative Reasoning Problems with Language Models »
  Aitor Lewkowycz · Anders Andreassen · David Dohan · Ethan Dyer · Henryk Michalewski · Vinay Ramasesh · Ambrose Slone · Cem Anil · Imanol Schlag · Theo Gutman-Solo · Yuhuai Wu · Behnam Neyshabur · Guy Gur-Ari · Vedant Misra
• 2022 Poster: Block-Recurrent Transformers »
  DeLesley Hutchins · Imanol Schlag · Yuhuai Wu · Ethan Dyer · Behnam Neyshabur
• 2021 Poster: Learning latent causal graphs via mixture oracles »
  Bohdan Kivva · Goutham Rajendran · Pradeep Ravikumar · Bryon Aragam
• 2021 Poster: Boosted CVaR Classification »
  Runtian Zhai · Chen Dan · Arun Suggala · J. Zico Kolter · Pradeep Ravikumar
• 2021 Poster: When Is Generalizable Reinforcement Learning Tractable? »
  Dhruv Malik · Yuanzhi Li · Pradeep Ravikumar
• 2020 Poster: On Learning Ising Models under Huber's Contamination Model »
  Adarsh Prasad · Vishwak Srinivasan · Sivaraman Balakrishnan · Pradeep Ravikumar
• 2020 Poster: On Completeness-aware Concept-Based Explanations in Deep Neural Networks »
  Chih-Kuan Yeh · Been Kim · Sercan Arik · Chun-Liang Li · Tomas Pfister · Pradeep Ravikumar
• 2020 Poster: Generalized Boosting »
  Arun Suggala · Bingbin Liu · Pradeep Ravikumar
• 2019 : Opening Remarks »
  Thorsten Joachims · Nathan Kallus · Michele Santacatterina · Adith Swaminathan · David Sontag · Angela Zhou
• 2019 Workshop: “Do the right thing”: machine learning and causal inference for improved decision making »
  Michele Santacatterina · Thorsten Joachims · Nathan Kallus · Adith Swaminathan · David Sontag · Angela Zhou
• 2019 Poster: On the (In)fidelity and Sensitivity of Explanations »
  Chih-Kuan Yeh · Cheng-Yu Hsieh · Arun Suggala · David Inouye · Pradeep Ravikumar
• 2019 Poster: On Human-Aligned Risk Minimization »
  Liu Leqi · Adarsh Prasad · Pradeep Ravikumar
• 2019 Poster: Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation »
  Chen Dan · Hong Wang · Hongyang Zhang · Yuchen Zhou · Pradeep Ravikumar
• 2019 Poster: Game Design for Eliciting Distinguishable Behavior »
  Fan Yang · Liu Leqi · Yifan Wu · Zachary Lipton · Pradeep Ravikumar · Tom M Mitchell · William Cohen
• 2018 Poster: The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models »
  Chen Dan · Liu Leqi · Bryon Aragam · Pradeep Ravikumar · Eric Xing
• 2018 Poster: Connecting Optimization and Regularization Paths »
  Arun Suggala · Adarsh Prasad · Pradeep Ravikumar
• 2018 Poster: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
  Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing
• 2018 Spotlight: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
  Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing
• 2018 Poster: 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
• 2018 Poster: Representer Point Selection for Explaining Deep Neural Networks »
  Chih-Kuan Yeh · Joon Kim · Ian En-Hsu Yen · Pradeep Ravikumar
• 2017 : Pradeep Ravikumar (CMU) on A Parallel Primal-Dual Sparse Method for Extreme Classification »
  Pradeep Ravikumar
• 2017 Poster: The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities »
  Arun Suggala · Mladen Kolar · Pradeep Ravikumar
• 2017 Poster: On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models »
  Adarsh Prasad · Alexandru Niculescu-Mizil · Pradeep Ravikumar
• 2017 Spotlight: On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models »
  Adarsh Prasad · Alexandru Niculescu-Mizil · Pradeep Ravikumar
• 2016 Poster: 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 Dhillon
• 2015 Poster: Fast Classification Rates for High-dimensional Gaussian Generative Models »
  Tianyang Li · Adarsh Prasad · Pradeep Ravikumar
• 2015 Poster: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
  Nikhil Rao · Hsiang-Fu Yu · Pradeep Ravikumar · Inderjit Dhillon
• 2015 Spotlight: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
  Nikhil Rao · Hsiang-Fu Yu · Pradeep Ravikumar · Inderjit Dhillon
• 2015 Poster: Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs »
  Vidyashankar Sivakumar · Arindam Banerjee · Pradeep Ravikumar
• 2015 Poster: Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent »
  Ian En-Hsu Yen · Kai Zhong · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon
• 2015 Poster: Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial »
  David I Inouye · Pradeep Ravikumar · Inderjit Dhillon
• 2015 Poster: Consistent Multilabel Classification »
  Oluwasanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon
• 2015 Poster: Closed-form Estimators for High-dimensional Generalized Linear Models »
  Eunho Yang · Aurelie Lozano · Pradeep Ravikumar
• 2015 Spotlight: Closed-form Estimators for High-dimensional Generalized Linear Models »
  Eunho Yang · Aurelie Lozano · Pradeep Ravikumar
• 2014 Poster: QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models »
  Cho-Jui Hsieh · Inderjit Dhillon · Pradeep Ravikumar · Stephen Becker · Peder A Olsen
• 2014 Poster: Consistent Binary Classification with Generalized Performance Metrics »
  Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon
• 2014 Poster: On the Information Theoretic Limits of Learning Ising Models »
  Rashish Tandon · Karthikeyan Shanmugam · Pradeep Ravikumar · Alex Dimakis
• 2014 Poster: Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space »
  Ian En-Hsu Yen · Ting-Wei Lin · Shou-De Lin · Pradeep Ravikumar · Inderjit Dhillon
• 2014 Spotlight: Consistent Binary Classification with Generalized Performance Metrics »
  Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon
• 2014 Poster: Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators »
  Kai Zhong · Ian En-Hsu Yen · Inderjit Dhillon · Pradeep Ravikumar
• 2014 Poster: A Representation Theory for Ranking Functions »
  Harsh H Pareek · Pradeep Ravikumar
• 2014 Poster: Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs »
  David I Inouye · Pradeep Ravikumar · Inderjit Dhillon
• 2014 Poster: Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings »
  Ian En-Hsu Yen · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon
• 2014 Poster: Elementary Estimators for Graphical Models »
  Eunho Yang · Aurelie Lozano · Pradeep Ravikumar
• 2013 Workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
  Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan
• 2013 Poster: Conditional Random Fields via Univariate Exponential Families »
  Eunho Yang · Pradeep Ravikumar · Genevera I Allen · Zhandong Liu
• 2013 Poster: On Poisson Graphical Models »
  Eunho Yang · Pradeep Ravikumar · Genevera I Allen · Zhandong Liu
• 2013 Poster: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables »
  Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar · Russell Poldrack
• 2013 Oral: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables »
  Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar · Russell Poldrack
• 2013 Poster: Dirty Statistical Models »
  Eunho Yang · Pradeep Ravikumar
• 2013 Poster: Large Scale Distributed Sparse Precision Estimation »
  Huahua Wang · Arindam Banerjee · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon
• 2013 Poster: Learning with Noisy Labels »
  Nagarajan Natarajan · Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari
• 2012 Workshop: Discrete Optimization in Machine Learning (DISCML): Structure and Scalability »
  Stefanie Jegelka · Andreas Krause · Jeffrey A Bilmes · Pradeep Ravikumar
• 2012 Poster: Graphical Models via Generalized Linear Models »
  Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu
• 2012 Oral: Graphical Models via Generalized Linear Models »
  Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu
• 2012 Poster: A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation »
  Cho-Jui Hsieh · Inderjit Dhillon · Pradeep Ravikumar · Arindam Banerjee
• 2011 Workshop: Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback »
  Andreas Krause · Pradeep Ravikumar · Stefanie S Jegelka · Jeffrey A Bilmes
• 2011 Poster: On Learning Discrete Graphical Models using Greedy Methods »
  Ali Jalali · Christopher C Johnson · Pradeep Ravikumar
• 2011 Spotlight: On Learning Discrete Graphical Models using Greedy Methods »
  Ali Jalali · Christopher C Johnson · Pradeep Ravikumar
• 2011 Poster: Greedy Algorithms for Structurally Constrained High Dimensional Problems »
  Ambuj Tewari · Pradeep Ravikumar · Inderjit Dhillon
• 2011 Poster: Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation »
  Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar
• 2011 Session: Oral Session 5 »
  Pradeep Ravikumar
• 2011 Poster: Nearest Neighbor based Greedy Coordinate Descent »
  Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari
• 2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
  Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes · Stefanie Jegelka
• 2010 Workshop: Robust Statistical Learning »
  Pradeep Ravikumar · Constantine Caramanis · Sujay Sanghavi
• 2010 Session: Oral Session 14 »
  Pradeep Ravikumar
• 2010 Oral: A Dirty Model for Multi-task Learning »
  Ali Jalali · Pradeep Ravikumar · Sujay Sanghavi · Chao Ruan
• 2010 Poster: A Dirty Model for Multi-task Learning »
  Ali Jalali · Pradeep Ravikumar · Sujay Sanghavi · Chao Ruan
• 2009 Workshop: Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity »
  Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes
• 2009 Poster: Information-theoretic lower bounds on the oracle complexity of convex optimization »
  Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright
• 2009 Spotlight: Information-theoretic lower bounds on the oracle complexity of convex optimization »
  Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright
• 2009 Poster: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
  Sahand N Negahban · Pradeep Ravikumar · Martin J Wainwright · Bin Yu
• 2009 Oral: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
  Sahand N Negahban · Pradeep Ravikumar · Martin J Wainwright · Bin Yu
• 2008 Poster: Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images »
  Pradeep Ravikumar · Vincent Vu · Bin Yu · Thomas Naselaris · Kendrick Kay · Jack Gallant
• 2008 Spotlight: Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images »
  Pradeep Ravikumar · Vincent Vu · Bin Yu · Thomas Naselaris · Kendrick Kay · Jack Gallant
• 2008 Poster: Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \ell_1-regularizedMLE »
  Pradeep Ravikumar · Garvesh Raskutti · Martin J Wainwright · Bin Yu
• 2007 Poster: SpAM: Sparse Additive Models »
  Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman
• 2007 Spotlight: SpAM: Sparse Additive Models »
  Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman
• 2006 Poster: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
  Martin J Wainwright · Pradeep Ravikumar · John Lafferty
• 2006 Spotlight: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
  Martin J Wainwright · Pradeep Ravikumar · John Lafferty