A Syllable-Level Probabilistic Framework for Bird Species Identification. - Balaji Lakshminarayanan; Raviv Raich; Xiaoli Z. Fern (ICMLA, 2009)

Inference in Supervised latent Dirichlet allocation. - Balaji Lakshminarayanan; Raviv Raich (MLSP, 2011)

Robust Bayesian Matrix Factorisation. - Balaji Lakshminarayanan; Guillaume Bouchard; Cédric Archambeau (AISTATS, 2011)

Top-down particle filtering for Bayesian decision trees. - Balaji Lakshminarayanan; Daniel M. Roy 001; Yee Whye Teh (ICML (3), 2013)

Inferring ground truth from multi-annotator ordinal data: a probabilistic approach - Balaji Lakshminarayanan; Yee Whye Teh (CoRR, 2013)

Mondrian Forests: Efficient Online Random Forests. - Balaji Lakshminarayanan; Daniel M. Roy 001; Yee Whye Teh (NIPS, 2014)

Distributed Bayesian Posterior Sampling via Moment Sharing. - Minjie Xu; Balaji Lakshminarayanan; Yee Whye Teh; Jun Zhu 001; Bo Zhang 010 (NIPS, 2014)

Particle Gibbs for Bayesian Additive Regression Trees. - Balaji Lakshminarayanan; Daniel M. Roy 001; Yee Whye Teh (AISTATS, 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 001 (UAI, 2015)

Approximate Inference with the Variational Holder Bound. - Guillaume Bouchard; Balaji Lakshminarayanan (CoRR, 2015)

Latent IBP Compound Dirichlet Allocation. - Cédric Archambeau; Balaji Lakshminarayanan; Guillaume Bouchard (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Mondrian Forests for Large-Scale Regression when Uncertainty Matters. - Balaji Lakshminarayanan; Daniel M. Roy 001; Yee Whye Teh (AISTATS, 2016)

The Mondrian Kernel. - Matej Balog; Balaji Lakshminarayanan; Zoubin Ghahramani; Daniel M. Roy 001; Yee Whye Teh (UAI, 2016)

Learning in Implicit Generative Models. - Shakir Mohamed; Balaji Lakshminarayanan (CoRR, 2016)

Decision trees and forests: a probabilistic perspective. - Balaji Lakshminarayanan (2016)

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. - Balaji Lakshminarayanan; Alexander Pritzel; Charles Blundell (NIPS, 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)

Comparison of Maximum Likelihood and GAN-based training of Real NVPs. - Ivo Danihelka; Balaji Lakshminarayanan; Benigno Uria; Daan Wierstra; Peter Dayan (CoRR, 2017)

Variational Approaches for Auto-Encoding Generative Adversarial Networks. - Mihaela Rosca; Balaji Lakshminarayanan; David Warde-Farley; Shakir Mohamed (CoRR, 2017)

Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees. - Daniel Zoran; Balaji Lakshminarayanan; Charles Blundell (CoRR, 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)

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 (ICLR (Poster), 2018)

Distribution Matching in Variational Inference. - Mihaela Rosca; Balaji Lakshminarayanan; Shakir Mohamed (CoRR, 2018)

Learning from Delayed Outcomes with Intermediate Observations. - Timothy A. Mann; Sven Gowal; Ray Jiang; Huiyi Hu; Balaji Lakshminarayanan; András György 001 (CoRR, 2018)

Adapting Auxiliary Losses Using Gradient Similarity. - Yunshu Du; Wojciech M. Czarnecki; Siddhant M. Jayakumar; Razvan Pascanu; Balaji Lakshminarayanan (CoRR, 2018)

Do Deep Generative Models Know What They Don't Know? - Eric T. Nalisnick; Akihiro Matsukawa; Yee Whye Teh; Dilan Görür; Balaji Lakshminarayanan (ICLR (Poster), 2019)

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. - Timothy A. Mann; Sven Gowal; András György 001; Huiyi Hu; Ray Jiang; Balaji Lakshminarayanan; Prav Srinivasan (ICML, 2019)

Hybrid Models with Deep and Invertible Features. - Eric T. Nalisnick; Akihiro Matsukawa; Yee Whye Teh; Dilan Görür; Balaji Lakshminarayanan (ICML, 2019)

Likelihood Ratios for Out-of-Distribution Detection. - Jie Ren 006; Peter J. Liu; Emily Fertig; Jasper Snoek; Ryan Poplin; Mark A. DePristo; Joshua V. Dillon; Balaji Lakshminarayanan (NeurIPS, 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 006; Zachary Nado (NeurIPS, 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)

Deep Ensembles: A Loss Landscape Perspective. - Stanislav Fort; Huiyi Hu; Balaji Lakshminarayanan (CoRR, 2019)

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

Bayesian Deep Ensembles via the Neural Tangent Kernel. - Bobby He; Balaji Lakshminarayanan; Yee Whye Teh (NeurIPS, 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 (NeurIPS, 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)

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. - Shreyas Padhy; Zachary Nado; Jie Ren 006; Jeremiah Z. Liu; Jasper Snoek; Balaji Lakshminarayanan (CoRR, 2020)

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 (AISTATS, 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 (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 (ICLR, 2021)

Exploring the Limits of Out-of-Distribution Detection. - Stanislav Fort; Jie Ren 006; Balaji Lakshminarayanan (NeurIPS, 2021)

Soft Calibration Objectives for Neural Networks. - Archit Karandikar; Nicholas Cain; Dustin Tran; Balaji Lakshminarayanan; Jonathon Shlens; Michael C. Mozer; Becca Roelofs (NeurIPS, 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; et al. (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)

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. - Jie Ren 006; Stanislav Fort; Jeremiah Z. Liu; Abhijit Guha Roy; Shreyas Padhy; Balaji Lakshminarayanan (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)

BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark. - Anand Avati; Martin Seneviratne; Emily Xue; Zhen Xu 006; Balaji Lakshminarayanan; Andrew M. Dai (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)

Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. - Kehang Han; Balaji Lakshminarayanan; Jeremiah Z. Liu (CoRR, 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)

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

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. - Yao Qin 001; Chiyuan Zhang; Ting Chen; Balaji Lakshminarayanan; Alex Beutel; Xuezhi Wang 002 (NeurIPS, 2022)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 2022)

Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions. - Abhijit Guha Roy; Jie Ren 006; Shekoofeh Azizi; Aaron Loh; Vivek Natarajan; Basil Mustafa; Nick Pawlowski; Jan Freyberg; Yuan Liu; Zachary Beaver; Nam Vo; Peggy Bui; et al. (Medical Image Anal., 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; et al. (Trans. Mach. Learn. Res., 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)

Improving Zero-shot Generalization and Robustness of Multi-Modal Models. - Yunhao Ge; Jie Ren 006; Andrew Gallagher; Yuxiao Wang 001; Ming-Hsuan Yang 001; Hartwig Adam; Laurent Itti; Balaji Lakshminarayanan; Jiaping Zhao (CVPR, 2023)

Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. - Kundan Krishna; Yao Zhao; Jie Ren 006; Balaji Lakshminarayanan; Jiaming Luo; Mohammad Saleh; Peter J. Liu (EMNLP (Findings), 2023)

Self-Evaluation Improves Selective Generation in Large Language Models. - Jie Ren 006; Yao Zhao; Tu Vu; Peter J. Liu; Balaji Lakshminarayanan (ICBINB, 2023)

Out-of-Distribution Detection and Selective Generation for Conditional Language Models. - Jie Ren 006; 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 001; Balaji Lakshminarayanan; Deepak Ramachandran (ICLR, 2023)

A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. - James Urquhart Allingham; Jie Ren 006; Michael W. Dusenberry; Xiuye Gu; Yin Cui; Dustin Tran; Jeremiah Zhe Liu; Balaji Lakshminarayanan (ICML, 2023)

What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. - Yao Qin 001; Xuezhi Wang 002; Balaji Lakshminarayanan; Ed H. Chi; Alex Beutel (SaTML, 2023)

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. - Jeremiah Zhe Liu; Krishnamurthy (Dj) Dvijotham; Jihyeon Lee; Quan Yuan 001; Martin Strobel 001; Balaji Lakshminarayanan; Deepak Ramachandran (CoRR, 2023)

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models. - Yunhao Ge; Jie Ren 006; Jiaping Zhao; Kaifeng Chen; Andrew Gallagher; Laurent Itti; Balaji Lakshminarayanan (CoRR, 2023)

Morse Neural Networks for Uncertainty Quantification. - Benoit Dherin; Huiyi Hu; Jie Ren 006; Michael W. Dusenberry; Balaji Lakshminarayanan (CoRR, 2023)

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. - Jeremiah Zhe Liu; Shreyas Padhy; Jie Ren 006; Zi Lin; Yeming Wen; Ghassen Jerfel; Zachary Nado; Jasper Snoek; Dustin Tran; Balaji Lakshminarayanan (J. Mach. Learn. Res., 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., 2023)

Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness. - Stanislav Fort; Balaji Lakshminarayanan (CoRR, 2024)