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2020 – today
- 2024
[j13]Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath:
From algorithms to action: improving patient care requires causality. BMC Medical Informatics Decis. Mak. 24(3): 111 (2024)- [j12]Abhijith Gandrakota, Lily H. Zhang
, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran:
Robust anomaly detection for particle physics using multi-background representation learning. Mach. Learn. Sci. Technol. 5(3): 35082 (2024) - [j11]Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi M. Schambra, Carlos Fernandez-Granda:
Quantifying impairment and disease severity using AI models trained on healthy subjects. npj Digit. Medicine 7(1) (2024) - [j10]Aahlad Manas Puli, Nitish Joshi, Yoav Wald, He He, Rajesh Ranganath:
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. Trans. Mach. Learn. Res. 2024 (2024) - [j9]Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi:
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. Trans. Mach. Learn. Res. 2024 (2024) - [c57]Michael S. Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden:
Stochastic Interpolants with Data-Dependent Couplings. ICML 2024 - [c56]Raghav Singhal, Mark Goldstein, Rajesh Ranganath:
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions. ICML 2024 - [c55]Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit Chopra, Lerrel Pinto:
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction. ICML 2024 - [i64]Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran:
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning. CoRR abs/2401.08777 (2024) - [i63]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i62]Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, Kyunghyun Cho:
Preference Learning Algorithms Do Not Learn Preference Rankings. CoRR abs/2405.19534 (2024) - [i61]Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit Chopra, Lerrel Pinto:
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction. CoRR abs/2406.04318 (2024) - [i60]Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi:
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. CoRR abs/2406.13660 (2024) - [i59]Raghav Singhal, Mark Goldstein, Rajesh Ranganath:
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions. CoRR abs/2407.07998 (2024) - [i58]Adriel Saporta, Aahlad Manas Puli, Mark Goldstein, Rajesh Ranganath:
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities. CoRR abs/2411.01053 (2024) - 2023
- [j8]Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich:
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics. Trans. Mach. Learn. Res. 2023 (2023) - [c54]Lily H. Zhang, Rajesh Ranganath:
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection. AAAI 2023: 15305-15312 - [c53]Neil Jethani, Adriel Saporta, Rajesh Ranganath:
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. AISTATS 2023: 8925-8953 - [c52]Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath:
DIET: Conditional independence testing with marginal dependence measures of residual information. AISTATS 2023: 10343-10367 - [c51]Raghav Singhal, Mark Goldstein, Rajesh Ranganath:
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. ICLR 2023 - [c50]Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner:
An Effective Meaningful Way to Evaluate Survival Models. ICML 2023: 28244-28276 - [c49]Rhys Compton, Lily H. Zhang, Aahlad Manas Puli, Rajesh Ranganath:
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. MLHC 2023: 110-127 - [c48]Aahlad Manas Puli, Lily H. Zhang, Yoav Wald, Rajesh Ranganath:
Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy. NeurIPS 2023 - [e7]Kaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary C. Lipton, Rajesh Ranganath, Iñigo Urteaga, Serene Yeung:
Machine Learning for Healthcare Conference, MLHC 2023, 11-12 August 2023, New York, USA. Proceedings of Machine Learning Research 219, PMLR 2023 [contents] - [i57]Raghav Singhal, Mukund Sudarshan, Anish Mahishi, Sri Kaushik, Luke Ginocchio, Angela Tong, Hersh Chandarana, Daniel K. Sodickson, Rajesh Ranganath, Sumit Chopra:
On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease. CoRR abs/2301.11962 (2023) - [i56]Lily H. Zhang, Rajesh Ranganath:
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection. CoRR abs/2302.04132 (2023) - [i55]Raghav Singhal, Mark Goldstein, Rajesh Ranganath:
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. CoRR abs/2302.07261 (2023) - [i54]Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich:
Shortcut Learning Through the Lens of Early Training Dynamics. CoRR abs/2302.09344 (2023) - [i53]Neil Jethani, Adriel Saporta, Rajesh Ranganath:
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. CoRR abs/2302.12893 (2023) - [i52]Yuxuan Hu, Albert Lui, Mark Goldstein, Mukund Sudarshan, Andrea Tinsay, Cindy Tsui, Samuel Maidman, John Medamana, Neil Jethani, Aahlad Manas Puli, Vuthy Nguy, Yindalon Aphinyanaphongs, Nicholas Kiefer, Nathaniel Smilowitz, James Horowitz, Tania Ahuja, Glenn I Fishman, Judith Hochman, Stuart Katz, Samuel Bernard, Rajesh Ranganath:
A dynamic risk score for early prediction of cardiogenic shock using machine learning. CoRR abs/2303.12888 (2023) - [i51]Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner:
An Effective Meaningful Way to Evaluate Survival Models. CoRR abs/2306.01196 (2023) - [i50]Rhys Compton, Lily H. Zhang, Aahlad Manas Puli, Rajesh Ranganath:
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. CoRR abs/2308.04431 (2023) - [i49]Aahlad Manas Puli, Lily H. Zhang, Yoav Wald, Rajesh Ranganath:
Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy. CoRR abs/2308.12553 (2023) - [i48]Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden:
Stochastic interpolants with data-dependent couplings. CoRR abs/2310.03725 (2023) - [i47]Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi M. Schambra, Carlos Fernandez-Granda:
Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects. CoRR abs/2311.12781 (2023) - [i46]Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, Giovanni Ciná:
When accurate prediction models yield harmful self-fulfilling prophecies. CoRR abs/2312.01210 (2023) - 2022
- [c47]Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller:
Learning Invariant Representations with Missing Data. CLeaR 2022: 290-301 - [c46]Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath:
FastSHAP: Real-Time Shapley Value Estimation. ICLR 2022 - [c45]Aahlad Manas Puli, Lily H. Zhang, Eric Karl Oermann, Rajesh Ranganath:
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations. ICLR 2022 - [c44]Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath:
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. ICML 2022: 26559-26574 - [c43]Xintian Han, Mark Goldstein, Rajesh Ranganath:
Survival Mixture Density Networks. MLHC 2022: 224-248 - [e6]Zachary C. Lipton, Rajesh Ranganath, Mark P. Sendak, Michael W. Sjoding, Serena Yeung:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2022, 5-6 August 2022, Durham, NC, USA. Proceedings of Machine Learning Research 182, PMLR 2022 [contents] - [i45]Neil Jethani, Aahlad Manas Puli, Hao Zhang, Leonid Garber, Lior Jankelson, Yindalon Aphinyanaphongs, Rajesh Ranganath:
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography. CoRR abs/2205.02900 (2022) - [i44]Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath:
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. CoRR abs/2206.11925 (2022) - [i43]Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath:
DIET: Conditional independence testing with marginal dependence measures of residual information. CoRR abs/2208.08579 (2022) - [i42]Xintian Han, Mark Goldstein, Rajesh Ranganath:
Survival Mixture Density Networks. CoRR abs/2208.10759 (2022) - [i41]Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath:
Decision making in cancer: Causal questions require causal answers. CoRR abs/2209.07397 (2022) - [i40]Aahlad Manas Puli, Nitish Joshi, He He, Rajesh Ranganath:
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. CoRR abs/2210.01302 (2022) - 2021
- [c42]Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath:
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. AISTATS 2021: 1459-1467 - [c41]Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath:
CONTRA: Contrarian statistics for controlled variable selection. AISTATS 2021: 1900-1908 - [c40]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Offline Contextual Bandits with Overparameterized Models. ICML 2021: 1049-1058 - [c39]Lily H. Zhang, Mark Goldstein, Rajesh Ranganath:
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. ICML 2021: 12427-12436 - [c38]Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath:
Inverse-Weighted Survival Games. NeurIPS 2021: 2160-2172 - [c37]David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna:
Offline RL Without Off-Policy Evaluation. NeurIPS 2021: 4933-4946 - [e5]Ken Jung, Serena Yeung, Mark P. Sendak, Michael W. Sjoding, Rajesh Ranganath:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2021, 6-7 August 2021, Virtual Event. Proceedings of Machine Learning Research 149, PMLR 2021 [contents] - [i39]Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath:
X-CAL: Explicit Calibration for Survival Analysis. CoRR abs/2101.05346 (2021) - [i38]Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath:
Causal Estimation with Functional Confounders. CoRR abs/2102.08533 (2021) - [i37]Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath:
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. CoRR abs/2103.01890 (2021) - [i36]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Offline RL Without Off-Policy Evaluation. CoRR abs/2106.08909 (2021) - [i35]Aahlad Manas Puli, Lily H. Zhang, Eric K. Oermann, Rajesh Ranganath:
Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations. CoRR abs/2107.00520 (2021) - [i34]Lily H. Zhang, Mark Goldstein, Rajesh Ranganath:
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. CoRR abs/2107.06908 (2021) - [i33]Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath:
FastSHAP: Real-Time Shapley Value Estimation. CoRR abs/2107.07436 (2021) - [i32]Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath:
Inverse-Weighted Survival Games. CoRR abs/2111.08175 (2021) - [i31]Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller:
Learning Invariant Representations with Missing Data. CoRR abs/2112.00881 (2021) - [i30]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Quantile Filtered Imitation Learning. CoRR abs/2112.00950 (2021) - 2020
- [j7]Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte:
The Counterfactual χ-GAN: Finding comparable cohorts in observational health data. J. Biomed. Informatics 109: 103515 (2020) - [j6]Narges Razavian, Vincent J. Major, Mukund Sudarshan, Jesse Burk-Rafel, Peter Stella, Hardev Randhawa, Seda Bilaloglu, Ji Chen, Vuthy Nguy, Walter Wang, Hao Zhang, Ilan Reinstein, David Kudlowitz, Cameron Zenger, Meng Cao, Ruina Zhang, Siddhant Dogra, Keerthi B. Harish, Brian Bosworth, Fritz Francois, Leora I. Horwitz, Rajesh Ranganath, Jonathan S. Austrian, Yindalon Aphinyanaphongs:
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients. npj Digit. Medicine 3 (2020) - [c36]Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte:
Adversarially-Learned Balancing Weights for Causal Inference. AMIA 2020 - [c35]Shreyas Bhave, Xintian Han, Rajesh Ranganath, Adler J. Perotte:
Deep Survival Analysis: The Impact of Feature Missingness. AMIA 2020 - [c34]Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath:
X-CAL: Explicit Calibration for Survival Analysis. NeurIPS 2020 - [c33]Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath:
Causal Estimation with Functional Confounders. NeurIPS 2020 - [c32]Aahlad Manas Puli, Rajesh Ranganath:
General Control Functions for Causal Effect Estimation from IVs. NeurIPS 2020 - [c31]Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath:
Deep Direct Likelihood Knockoffs. NeurIPS 2020 - [e4]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2020, 7-8 August 2020, Virtual Event, Durham, NC, USA. Proceedings of Machine Learning Research 126, PMLR 2020 [contents] - [i29]Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte:
The Counterfactual χ-GAN. CoRR abs/2001.03115 (2020) - [i28]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Overfitting and Optimization in Offline Policy Learning. CoRR abs/2006.15368 (2020) - [i27]Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath:
Deep Direct Likelihood Knockoffs. CoRR abs/2007.15835 (2020) - [i26]Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath:
Probabilistic Machine Learning for Healthcare. CoRR abs/2009.11087 (2020)
2010 – 2019
- 2019
- [c30]Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath:
Support and Invertibility in Domain-Invariant Representations. AISTATS 2019: 527-536 - [c29]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR 2019 - [c28]Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini:
Reproducibility in Machine Learning for Health. RML@ICLR 2019 - [c27]Da Tang, Rajesh Ranganath:
The Variational Predictive Natural Gradient. ICML 2019: 6145-6154 - [c26]Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath:
Predicate Exchange: Inference with Declarative Knowledge. ICML 2019: 6186-6195 - [c25]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS 2019: 8499-8511 - [e3]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA. Proceedings of Machine Learning Research 106, PMLR 2019 [contents] - [i25]Zenna Tavares, Javier Burroni, Edgar Minaysan, Armando Solar-Lezama, Rajesh Ranganath:
Soft Constraints for Inference with Declarative Knowledge. CoRR abs/1901.05437 (2019) - [i24]Da Tang, Rajesh Ranganath:
The Variational Predictive Natural Gradient. CoRR abs/1903.02984 (2019) - [i23]Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath:
Support and Invertibility in Domain-Invariant Representations. CoRR abs/1903.03448 (2019) - [i22]Zenna Tavares, Xin Zhang, Edgar Minaysan, Javier Burroni, Rajesh Ranganath, Armando Solar-Lezama:
The Random Conditional Distribution for Higher-Order Probabilistic Inference. CoRR abs/1903.10556 (2019) - [i21]Raghav Singhal, Saad Lahlou, Rajesh Ranganath:
Kernelized Complete Conditional Stein Discrepancy. CoRR abs/1904.04478 (2019) - [i20]Kexin Huang, Jaan Altosaar, Rajesh Ranganath:
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission. CoRR abs/1904.05342 (2019) - [i19]Xintian Han, Yuxuan Hu, Luca Foschini, Larry Chinitz, Lior Jankelson, Rajesh Ranganath:
Adversarial Examples for Electrocardiograms. CoRR abs/1905.05163 (2019) - [i18]Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini:
Reproducibility in Machine Learning for Health. CoRR abs/1907.01463 (2019) - [i17]Aahlad Manas Puli, Rajesh Ranganath:
Generalized Control Functions via Variational Decoupling. CoRR abs/1907.03451 (2019) - [i16]Rajesh Ranganath, David M. Blei:
Population Predictive Checks. CoRR abs/1908.00882 (2019) - [i15]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR abs/1910.14265 (2019) - 2018
- [j5]Jeremy R. Manning, Xia Zhu, Theodore L. Willke, Rajesh Ranganath, Kimberly L. Stachenfeld, Uri Hasson, David M. Blei, Kenneth A. Norman:
A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage 180(Part): 243-252 (2018) - [c24]Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei:
Variational Sequential Monte Carlo. AISTATS 2018: 968-977 - [c23]Jaan Altosaar, Rajesh Ranganath, David M. Blei:
Proximity Variational Inference. AISTATS 2018: 1961-1969 - [c22]Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei:
Noisin: Unbiased Regularization for Recurrent Neural Networks. ICML 2018: 1251-1260 - [c21]Xenia Miscouridou, Adler J. Perotte, Noemie Elhadad, Rajesh Ranganath:
Deep Survival Analysis: Nonparametrics and Missingness. MLHC 2018: 244-256 - [c20]Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag:
Max-margin learning with the Bayes factor. UAI 2018: 896-905 - [e2]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2018, 17-18 August 2018, Palo Alto, California. Proceedings of Machine Learning Research 85, PMLR 2018 [contents] - [i14]Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei:
Noisin: Unbiased Regularization for Recurrent Neural Networks. CoRR abs/1805.01500 (2018) - [i13]Rajesh Ranganath, Adler J. Perotte:
Multiple Causal Inference with Latent Confounding. CoRR abs/1805.08273 (2018) - [i12]Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath:
Opportunities in Machine Learning for Healthcare. CoRR abs/1806.00388 (2018) - 2017
- [b1]Rajesh Ranganath:
Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications. Princeton University, USA, 2017 - [j4]Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Differentiation Variational Inference. J. Mach. Learn. Res. 18: 14:1-14:45 (2017) - [c19]Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei:
Variational Inference via \chi Upper Bound Minimization. NIPS 2017: 2732-2741 - [c18]Dustin Tran, Rajesh Ranganath, David M. Blei:
Hierarchical Implicit Models and Likelihood-Free Variational Inference. NIPS 2017: 5523-5533 - [e1]Finale Doshi-Velez, Jim Fackler, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Health Care Conference, MLHC 2017, Boston, Massachusetts, USA, 18-19 August 2017. Proceedings of Machine Learning Research 68, PMLR 2017 [contents] - [i11]Dustin Tran, Rajesh Ranganath, David M. Blei:
Deep and Hierarchical Implicit Models. CoRR abs/1702.08896 (2017) - [i10]Jaan Altosaar, Rajesh Ranganath, David M. Blei:
Proximity Variational Inference. CoRR abs/1705.08931 (2017) - 2016
- [c17]Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei:
Variational Tempering. AISTATS 2016: 704-712 - [c16]Rajesh Ranganath, Dustin Tran, David M. Blei:
Hierarchical Variational Models. ICML 2016: 324-333 - [c15]Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei:
Deep Survival Analysis. MLHC 2016: 101-114 - [c14]Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei:
Operator Variational Inference. NIPS 2016: 496-504 - [c13]Dustin Tran, Rajesh Ranganath, David M. Blei:
Variational Gaussian Process. ICLR 2016 - [i9]Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Differentiation Variational Inference. CoRR abs/1603.00788 (2016) - [i8]Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei:
Deep Survival Analysis. CoRR abs/1608.02158 (2016) - [i7]Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei:
Operator Variational Inference. CoRR abs/1610.09033 (2016) - [i6]Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei:
The $χ$-Divergence for Approximate Inference. CoRR abs/1611.00328 (2016) - 2015
- [j3]Adler J. Perotte, Rajesh Ranganath, Jamie S. Hirsch, David M. Blei, Noémie Elhadad:
Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J. Am. Medical Informatics Assoc. 22(4): 872-880 (2015) - [c12]Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei:
Deep Exponential Families. AISTATS 2015 - [c11]Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Variational Inference in Stan. NIPS 2015: 568-576 - [c10]James McInerney, Rajesh Ranganath, David M. Blei:
The Population Posterior and Bayesian Modeling on Streams. NIPS 2015: 1153-1161 - [c9]Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei:
Dynamic Poisson Factorization. RecSys 2015: 155-162 - [c8]Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei:
The Survival Filter: Joint Survival Analysis with a Latent Time Series. UAI 2015: 742-751 - [i5]Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei:
Dynamic Poisson Factorization. CoRR abs/1509.04640 (2015) - [i4]Rajesh Ranganath, Dustin Tran, David M. Blei:
Hierarchical Variational Models. CoRR abs/1511.02386 (2015) - 2014
- [c7]Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei:
Bayesian Nonparametric Poisson Factorization for Recommendation Systems. AISTATS 2014: 275-283 - [c6]Rajesh Ranganath, Sean Gerrish, David M. Blei:
Black Box Variational Inference. AISTATS 2014: 814-822 - [c5]Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, David M. Blei:
Hierarchical topographic factor analysis. PRNI 2014: 1-4 - [i3]Rajesh Ranganath, Sean Gerrish, David M. Blei:
Black Box Variational Inference. CoRR abs/1401.0118 (2014) - [i2]Farhan Abrol, Stephan Mandt, Rajesh Ranganath, David M. Blei:
Deterministic Annealing for Stochastic Variational Inference. CoRR abs/1411.1810 (2014) - [i1]Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei:
Deep Exponential Families. CoRR abs/1411.2581 (2014) - 2013
- [j2]Rajesh Ranganath, Dan Jurafsky, Daniel A. McFarland:
Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates. Comput. Speech Lang. 27(1): 89-115 (2013) - [c4]Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing:
An Adaptive Learning Rate for Stochastic Variational Inference. ICML (2) 2013: 298-306 - 2011
- [j1]Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng:
Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM 54(10): 95-103 (2011)
2000 – 2009
- 2009
- [c3]Rajesh Ranganath, Daniel Jurafsky, Daniel A. McFarland:
It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates. EMNLP 2009: 334-342 - [c2]Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng:
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. ICML 2009: 609-616 - [c1]Daniel Jurafsky, Rajesh Ranganath, Daniel A. McFarland:
Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation. HLT-NAACL 2009: 638-646
Coauthor Index
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