Balaji Lakshminarayanan

According to our database, Balaji Lakshminarayanan authored at least 69 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

An instance-dependent simulation framework for learning with label noise.

Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin

Mach. Learn., June, 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

J. Mach. Learn. Res., 2023

Self-Evaluation Improves Selective Generation in Large Language Models.

Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan

CoRR, 2023

Morse Neural Networks for Uncertainty Quantification.

Benoit Dherin, Huiyi Hu, Jie Ren, Michael W. Dusenberry, Balaji Lakshminarayanan

CoRR, 2023

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models.

Yunhao Ge, Jie Ren, Jiaping Zhao, Kaifeng Chen, Andrew Gallagher, Laurent Itti, Balaji Lakshminarayanan

CoRR, 2023

What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel.

Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel

CoRR, 2023

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play.

Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Martin Strobel, Balaji Lakshminarayanan, Deepak Ramachandran

CoRR, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Improving the Robustness of Summarization Models by Detecting and Removing Input Noise.

Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

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

Trans. Mach. Learn. Res., 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

Trans. Mach. Learn. Res., 2022

Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions.

Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zachary Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Gregory S. Corrado, Umesh Telang, Yun Liu, A. Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens

Medical Image Anal., 2022

Plex: Towards Reliability using Pretrained Large Model Extensions.

Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Test Sample Accuracy Scales with Training Sample Density in Neural Networks.

Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Normalizing Flows for Probabilistic Modeling and Inference.

George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

J. Mach. Learn. Res., 2021

Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift.

Kehang Han, Balaji Lakshminarayanan, Jeremiah Z. Liu

CoRR, 2021

A Realistic Simulation Framework for Learning with Label Noise.

Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin

CoRR, 2021

BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark.

Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai

CoRR, 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

CoRR, 2021

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.

Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan

CoRR, 2021

Predicting Unreliable Predictions by Shattering a Neural Network.

Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio

CoRR, 2021

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.

Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran

CoRR, 2021

Soft Calibration Objectives for Neural Networks.

Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Exploring the Limits of Out-of-Distribution Detection.

Stanislav Fort, Jie Ren, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 9th International Conference on Learning Representations, 2021

Density of States Estimation for Out of Distribution Detection.

Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks.

Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan

CoRR, 2020

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.

Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek

CoRR, 2020

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness.

Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Bayesian Deep Ensembles via the Neural Tangent Kernel.

Bobby He, Balaji Lakshminarayanan, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors.

Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran

Proceedings of the 37th International Conference on Machine Learning, 2020

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Deep Ensembles: A Loss Landscape Perspective.

Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan

CoRR, 2019

Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality.

Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan

CoRR, 2019

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.

Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Likelihood Ratios for Out-of-Distribution Detection.

Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Hybrid Models with Deep and Invertible Features.

Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems.

Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan

Proceedings of the 36th International Conference on Machine Learning, 2019

Do Deep Generative Models Know What They Don't Know?

Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Adapting Auxiliary Losses Using Gradient Similarity.

Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Razvan Pascanu, Balaji Lakshminarayanan

CoRR, 2018

Learning from Delayed Outcomes with Intermediate Observations.

Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, András György

CoRR, 2018

Distribution Matching in Variational Inference.

Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed

CoRR, 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 J. Goodfellow

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server.

Leonard Hasenclever, Stefan Webb, Thibaut Liénart, Sebastian J. Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

J. Mach. Learn. Res., 2017

Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees.

Daniel Zoran, Balaji Lakshminarayanan, Charles Blundell

CoRR, 2017

Variational Approaches for Auto-Encoding Generative Adversarial Networks.

Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed

CoRR, 2017

Comparison of Maximum Likelihood and GAN-based training of Real NVPs.

Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan

CoRR, 2017

The Cramer Distance as a Solution to Biased Wasserstein Gradients.

Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos

CoRR, 2017

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Decision trees and forests: a probabilistic perspective.

Balaji Lakshminarayanan

PhD thesis, 2016

Learning in Implicit Generative Models.

Shakir Mohamed, Balaji Lakshminarayanan

CoRR, 2016

The Mondrian Kernel.

Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Mondrian Forests for Large-Scale Regression when Uncertainty Matters.

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Latent IBP Compound Dirichlet Allocation.

Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Approximate Inference with the Variational Holder Bound.

Guillaume Bouchard, Balaji Lakshminarayanan

CoRR, 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

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Particle Gibbs for Bayesian Additive Regression Trees.

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Distributed Bayesian Posterior Sampling via Moment Sharing.

Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Mondrian Forests: Efficient Online Random Forests.

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Inferring ground truth from multi-annotator ordinal data: a probabilistic approach

Balaji Lakshminarayanan, Yee Whye Teh

CoRR, 2013

Top-down particle filtering for Bayesian decision trees.

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Proceedings of the 30th International Conference on Machine Learning, 2013

2011

Robust Bayesian Matrix Factorisation.

Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Inference in Supervised latent Dirichlet allocation.

Balaji Lakshminarayanan, Raviv Raich

Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

2009

A Syllable-Level Probabilistic Framework for Bird Species Identification.

Balaji Lakshminarayanan, Raviv Raich, Xiaoli Z. Fern

Proceedings of the International Conference on Machine Learning and Applications, 2009