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Rajesh Ranganath

Showing results (1-10 of 44) with videos related to

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Advances in Neural Information Processing Systems|May 6, 2021
General Control Functions for Causal Effect Estimation from Instrumental VariablesAahlad Puli, Rajesh Ranganath
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence|March 11, 2024
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution DetectionLily H Zhang, Rajesh Ranganath
Proceedings of Machine Learning Research|April 28, 2025
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluationNeil Jethani, Adriel Saporta, Rajesh Ranganath
Advances in Neural Information Processing Systems|May 6, 2021
Deep direct likelihood knockoffsMukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Proceedings of Machine Learning Research|September 14, 2023
Survival Mixture Density NetworksXintian Han, Mark Goldstein, Rajesh Ranganath
Advances in Neural Information Processing Systems|May 6, 2021
Causal Estimation with Functional ConfoundersAahlad Puli, Adler J Perotte, Rajesh Ranganath
Proceedings of Machine Learning Research|July 21, 2022
Understanding Failures in Out-of-Distribution Detection with Deep Generative ModelsLily H Zhang, Mark Goldstein, Rajesh Ranganath
Proceedings of Machine Learning Research|September 8, 2023
DIET: Conditional independence testing with marginal dependence measures of residual informationMukund Sudarshan, Aahlad Puli, Wesley Tansey, et al.
Cancer Medicine|January 27, 2026
Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness-Aware Machine Learning ModelsHyungrok Do, Rajesh Ranganath, Katie Murray, et al.
Proceedings of Machine Learning Research|May 6, 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsNeil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, et al.
Pageof 5

Showing results (1-10 of 44) with videos related to

Sort By:
Pageof 5
Advances in Neural Information Processing Systems|May 6, 2021
General Control Functions for Causal Effect Estimation from Instrumental VariablesAahlad Puli, Rajesh Ranganath
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence|March 11, 2024
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution DetectionLily H Zhang, Rajesh Ranganath
Proceedings of Machine Learning Research|April 28, 2025
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluationNeil Jethani, Adriel Saporta, Rajesh Ranganath
Advances in Neural Information Processing Systems|May 6, 2021
Deep direct likelihood knockoffsMukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Proceedings of Machine Learning Research|September 14, 2023
Survival Mixture Density NetworksXintian Han, Mark Goldstein, Rajesh Ranganath
Advances in Neural Information Processing Systems|May 6, 2021
Causal Estimation with Functional ConfoundersAahlad Puli, Adler J Perotte, Rajesh Ranganath
Proceedings of Machine Learning Research|July 21, 2022
Understanding Failures in Out-of-Distribution Detection with Deep Generative ModelsLily H Zhang, Mark Goldstein, Rajesh Ranganath
Proceedings of Machine Learning Research|September 8, 2023
DIET: Conditional independence testing with marginal dependence measures of residual informationMukund Sudarshan, Aahlad Puli, Wesley Tansey, et al.
Cancer Medicine|January 27, 2026
Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness-Aware Machine Learning ModelsHyungrok Do, Rajesh Ranganath, Katie Murray, et al.
Proceedings of Machine Learning Research|May 6, 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsNeil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, et al.
Pageof 5