Lakshminarayanan, Balaji

50 publications

NeurIPSW 2024 Ensemble Everything Everywhere: Multi-Scale Aggregation for Adversarial Robustness Stanislav Fort, Balaji Lakshminarayanan

JMLR 2023 A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan

ICML 2023 A Simple Zero-Shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models James Urquhart Allingham, Jie Ren, Michael W Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan

MLJ 2023 An Instance-Dependent Simulation Framework for Learning with Label Noise Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin

CVPR 2023 Improving Zero-Shot Generalization and Robustness of Multi-Modal Models Yunhao Ge, Jie Ren, Andrew Gallagher, Yuxiao Wang, Ming-Hsuan Yang, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, Jiaping Zhao

ICMLW 2023 Morse Neural Networks for Uncertainty Quantification Benoit Dherin, Huiyi Hu, Jie Ren, Michael W Dusenberry, Balaji Lakshminarayanan

ICLR 2023 Out-of-Distribution Detection and Selective Generation for Conditional Language Models Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J Liu

ICLR 2023 Pushing the Accuracy-Group Robustness Frontier with Introspective Self-Play Jeremiah Zhe Liu, Krishnamurthy Dj Dvijotham, Jihyeon Lee, Quan Yuan, Balaji Lakshminarayanan, Deepak Ramachandran

NeurIPSW 2023 Self-Evaluation Improves Selective Generation in Large Language Models Jie Ren, Yao Zhao, Tu Vu, Peter J Liu, Balaji Lakshminarayanan

TMLR 2022 Deep Classifiers with Label Noise Modeling and Distance Awareness Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

NeurIPSW 2022 Improving Zero-Shot Generalization and Robustness of Multi-Modal Models Yunhao Ge, Jie Ren, Ming-Hsuan Yang, Yuxiao Wang, Andrew Gallagher, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, Jiaping Zhao

NeurIPSW 2022 Improving the Robustness of Conditional Language Models by Detecting and Removing Input Noise Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J Liu

NeurIPSW 2022 Out-of-Distribution Detection and Selective Generation for Conditional Language Models Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J Liu

ICMLW 2022 Plex: Towards Reliability Using Pretrained Large Model Extensions Dustin Tran, Jeremiah Zhe Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda E Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, E. Kelly Buchanan, Kevin Patrick Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

NeurIPSW 2022 Pushing the Accuracy-Fairness Tradeoff Frontier with Introspective Self-Play Jeremiah Zhe Liu, Krishnamurthy Dj Dvijotham, Jihyeon Lee, Quan Yuan, Martin Strobel, Balaji Lakshminarayanan, Deepak Ramachandran

NeurIPSW 2022 Reliability Benchmarks for Image Segmentation E. Kelly Buchanan, Michael W Dusenberry, Jie Ren, Kevin Patrick Murphy, Balaji Lakshminarayanan, Dustin Tran

TMLR 2022 Sparse MoEs Meet Efficient Ensembles James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton

CoLLAs 2022 Test Sample Accuracy Scales with Training Sample Density in Neural Networks Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi

NeurIPS 2022 Understanding and Improving Robustness of Vision Transformers Through Patch-Based Negative Augmentation Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang

AISTATS 2021 Density of States Estimation for Out of Distribution Detection Warren Morningstar, Cusuh Ham, Andrew Gallagher, Balaji Lakshminarayanan, Alex Alemi, Joshua Dillon

NeurIPSW 2021 BEDS-Bench: Behavior of EHR-Models Under Distributional Shift - A Benchmark Anand Avati, Martin Seneviratne, Yuan Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai

ICLR 2021 Combining Ensembles and Data Augmentation Can Harm Your Calibration Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran

NeurIPS 2021 Exploring the Limits of Out-of-Distribution Detection Stanislav Fort, Jie Ren, Balaji Lakshminarayanan

JMLR 2021 Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

NeurIPSW 2021 Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift Kehang Han, Balaji Lakshminarayanan, Jeremiah Zhe Liu

NeurIPS 2021 Soft Calibration Objectives for Neural Networks Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael Mozer, Becca Roelofs

ICMLW 2021 Task-Agnostic Continual Learning with Hybrid Probabilistic Models Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu

ICLR 2021 Training Independent Subnetworks for Robust Prediction Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran

NeurIPSW 2021 Understanding and Improving Robustness of VisionTransformers Through Patch-Based NegativeAugmentation Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang

ICLR 2020 AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

NeurIPS 2020 Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He, Balaji Lakshminarayanan, Yee Whye Teh

ICML 2020 Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

NeurIPS 2020 Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, Balaji Lakshminarayanan

NeurIPSW 2020 Why Are Bootstrapped Deep Ensembles Not Better? Jeremy Nixon, Balaji Lakshminarayanan, Dustin Tran

NeurIPS 2019 Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, Jasper Snoek

ICLR 2019 Do Deep Generative Models Know What They Don't Know? Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

ICML 2019 Hybrid Models with Deep and Invertible Features Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

ICML 2019 Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems Timothy Arthur Mann, Sven Gowal, Andras Gyorgy, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan

NeurIPS 2019 Likelihood Ratios for Out-of-Distribution Detection Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, Balaji Lakshminarayanan

ICLR 2018 Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence at Every Step William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

JMLR 2017 Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

NeurIPS 2017 Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell

AISTATS 2016 Mondrian Forests for Large-Scale Regression When Uncertainty Matters Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

UAI 2016 The Mondrian Kernel Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh

UAI 2015 Kernel-Based Just-in-Time Learning for Passing Expectation Propagation Messages Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó

AISTATS 2015 Particle Gibbs for Bayesian Additive Regression Trees Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

NeurIPS 2014 Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang

NeurIPS 2014 Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

ICML 2013 Top-Down Particle Filtering for Bayesian Decision Trees Balaji Lakshminarayanan, Daniel Roy, Yee Whye Teh

AISTATS 2011 Robust Bayesian Matrix Factorisation Balaji Lakshminarayanan, Guillaume Bouchard, Cedric Archambeau