Pradeep Ravikumar

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 151

Top Venues

CoRR

ICML

NeurIPS

NIPS

Publications

Runtian Zhai, Rattana Pukdee, Roger Jin, Maria-Florina Balcan, Pradeep Ravikumar
Spectrally Transformed Kernel Regression. CoRR (2024)
Daniel P. Jeong, Zachary C. Lipton, Pradeep Ravikumar
LLM-Select: Feature Selection with Large Language Models. CoRR (2024)
Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch
On the Origins of Linear Representations in Large Language Models. CoRR (2024)
Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. CoRR (2024)
Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR (2024)
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
Faith-Shap: The Faithful Shapley Interaction Index. J. Mach. Learn. Res. 24 (2023)
Yash Gupta, Runtian Zhai, Arun Sai Suggala, Pradeep Ravikumar
Responsible AI (RAI) Games and Ensembles. NeurIPS (2023)
Yash Gupta, Runtian Zhai, Arun Sai Suggala, Pradeep Ravikumar
Responsible AI (RAI) Games and Ensembles. CoRR (2023)
Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar
Learning with Explanation Constraints. NeurIPS (2023)
Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan, Pradeep Ravikumar
Learning with Explanation Constraints. CoRR (2023)
Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
Global Optimality in Bivariate Gradient-based DAG Learning. CoRR (2023)
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR (2023)
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
Sample based Explanations via Generalized Representers. CoRR (2023)
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS (2023)
Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
Optimizing NOTEARS Objectives via Topological Swaps. CoRR (2023)
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
Sample based Explanations via Generalized Representers. NeurIPS (2023)
Wenbin Zhang, Zichong Wang, Juyong Kim, Cheng Cheng, Thomas Oommen, Pradeep Ravikumar, Jeremy C. Weiss
Individual Fairness Under Uncertainty. ECAI (2023)
Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar
Representer Point Selection for Explaining Regularized High-dimensional Models. CoRR (2023)
Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. CoRR (2023)
Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. AISTATS (2023)
Runtian Zhai, Bingbin Liu, Andrej Risteski, Zico Kolter, Pradeep Ravikumar
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation. CoRR (2023)
Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Ravikumar
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CoRR (2023)
Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. NeurIPS (2023)
Wenbin Zhang, Juyong Kim, Zichong Wang, Pradeep Ravikumar, Jeremy C. Weiss
Individual Fairness Guarantee in Learning with Censorship. CoRR (2023)
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister, Jonas Peters
Identifying Representations for Intervention Extrapolation. CoRR (2023)
Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
Global Optimality in Bivariate Gradient-based DAG Learning. NeurIPS (2023)
Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. CoRR (2022)
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization. CoRR (2022)
Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
Building Robust Ensembles via Margin Boosting. CoRR (2022)
Juyong Kim, Jeremy C. Weiss, Pradeep Ravikumar
Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence. CHIL (2022)
Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar
First is Better Than Last for Language Data Influence. NeurIPS (2022)
Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar
Understanding Why Generalized Reweighting Does Not Improve Over ERM. CoRR (2022)
Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar
Human-Centered Concept Explanations for Neural Networks. CoRR (2022)
Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar, Neil Y. C. Lin, Cho-Jui Hsieh
Concept Gradient: Concept-based Interpretation Without Linear Assumption. CoRR (2022)
Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar
First is Better Than Last for Training Data Influence. CoRR (2022)
Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
Building Robust Ensembles via Margin Boosting. ICML (2022)
Rattana Pukdee, Dylan Sam, Maria-Florina Balcan, Pradeep Ravikumar
Label Propagation with Weak Supervision. CoRR (2022)
Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
Heavy-tailed Streaming Statistical Estimation. AISTATS (2022)
Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. 23 (2022)
Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar, David I. Inouye
Iterative Alignment Flows. AISTATS (2022)
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. AISTATS (2022)
Kevin Bello, Bryon Aragam, Pradeep Ravikumar
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. NeurIPS (2022)
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Identifiability of deep generative models without auxiliary information. NeurIPS (2022)
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
Faith-Shap: The Faithful Shapley Shapley Interaction Index. CoRR (2022)
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Identifiability of deep generative models under mixture priors without auxiliary information. CoRR (2022)
Bingbin Liu, Daniel Hsu, Pradeep Ravikumar, Andrej Risteski
Masked prediction tasks: a parameter identifiability view. CoRR (2022)
Kevin Bello, Bryon Aragam, Pradeep Ravikumar
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. CoRR (2022)
Bingbin Liu, Daniel J. Hsu, Pradeep Ravikumar, Andrej Risteski
Masked Prediction: A Parameter Identifiability View. NeurIPS (2022)
Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games. CoRR (2022)
Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. AISTATS (2022)
Juyong Kim, Abheesht Sharma, Suhas Shanbhogue, Jeremy C. Weiss, Pradeep Ravikumar
AnEMIC: A Framework for Benchmarking ICD Coding Models. EMNLP (Demos) (2022)
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Learning latent causal graphs via mixture oracles. CoRR (2021)
Juyong Kim, Pradeep Ravikumar, Joshua Ainslie, Santiago Ontañón
Improving Compositional Generalization in Classification Tasks via Structure Annotations. CoRR (2021)
Juyong Kim, Pradeep Ravikumar, Joshua Ainslie, Santiago Ontañón
Improving Compositional Generalization in Classification Tasks via Structure Annotations. ACL/IJCNLP (2) (2021)
Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar
When Is Generalizable Reinforcement Learning Tractable? NeurIPS (2021)
Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar
Subseasonal climate prediction in the western US using Bayesian spatial models. UAI (2021)
Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Sivaraman Balakrishnan, Zachary C. Lipton, Ruslan Salakhutdinov, Pradeep Ravikumar
On Proximal Policy Optimization's Heavy-tailed Gradients. CoRR (2021)
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. CoRR (2021)
David I. Inouye, Zeyu Zhou, Ziyu Gong, Pradeep Ravikumar
Iterative Barycenter Flows. CoRR (2021)
So Yeon Min, Devendra Singh Chaplot, Pradeep Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov
FILM: Following Instructions in Language with Modular Methods. CoRR (2021)
Runtian Zhai, Chen Dan, Arun Sai Suggala, J. Zico Kolter, Pradeep Ravikumar
Boosted CVaR Classification. NeurIPS (2021)
Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar
DORO: Distributional and Outlier Robust Optimization. ICML (2021)
Runtian Zhai, Chen Dan, Arun Sai Suggala, J. Zico Kolter, Pradeep Ravikumar
Boosted CVaR Classification. CoRR (2021)
Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, Arindam Banerjee
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. AAAI (2021)
Bingbin Liu, Pradeep Ravikumar, Andrej Risteski
Contrastive learning of strong-mixing continuous-time stochastic processes. CoRR (2021)
Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli
Efficient Bandit Convex Optimization: Beyond Linear Losses. COLT (2021)
Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar
On Proximal Policy Optimization's Heavy-tailed Gradients. ICML (2021)
Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar
When Is Generalizable Reinforcement Learning Tractable? CoRR (2021)
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Learning latent causal graphs via mixture oracles. NeurIPS (2021)
Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar
DORO: Distributional and Outlier Robust Optimization. CoRR (2021)
Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. CoRR (2021)
Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
Heavy-tailed Streaming Statistical Estimation. CoRR (2021)
Bingbin Liu, Pradeep Ravikumar, Andrej Risteski
Contrastive learning of strong-mixing continuous-time stochastic processes. AISTATS (2021)
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh
Evaluations and Methods for Explanation through Robustness Analysis. CoRR (2020)
Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
Class-Weighted Classification: Trade-offs and Robust Approaches. CoRR (2020)
Juyong Kim, Linyuan Gong, Justin Khim, Jeremy C. Weiss, Pradeep Ravikumar
Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches. ML4H@NeurIPS (2020)
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. ICLR (2020)
Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar
On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS (2020)
Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. ICML (2020)
Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
Class-Weighted Classification: Trade-offs and Robust Approaches. ICML (2020)
Chen Dan, Yuting Wei, Pradeep Ravikumar
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification. ICML (2020)
Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR (2020)
Arun Sai Suggala, Bingbin Liu, Pradeep Ravikumar
Generalized Boosting. NeurIPS (2020)
Chen Dan, Yuting Wei, Pradeep Ravikumar
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification. CoRR (2020)
Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar
On Learning Ising Models under Huber's Contamination Model. NeurIPS (2020)
Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. CoRR (2020)
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
Learning Sparse Nonparametric DAGs. AISTATS (2020)
Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR (2020)
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
The Risks of Invariant Risk Minimization. CoRR (2020)
Sijie He, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, Arindam Banerjee
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. CoRR (2020)
Justin Khim, Liu Leqi, Adarsh Prasad, Pradeep Ravikumar
Uniform Convergence of Rank-weighted Learning. ICML (2020)
Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar
Learning Minimax Estimators via Online Learning. CoRR (2020)
Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
A Robust Univariate Mean Estimator is All You Need. AISTATS (2020)
David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
Automated Dependence Plots. UAI (2020)
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. CoRR (2020)
Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR (2020)
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
Learning Sparse Nonparametric DAGs. CoRR (2019)
Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister
On Concept-Based Explanations in Deep Neural Networks. CoRR (2019)
Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
A Unified Approach to Robust Mean Estimation. CoRR (2019)
Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. COLT (2019)

Pradeep Ravikumar.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Pradeep Ravikumar