Balaji Lakshminarayanan

103 papers A* 17A 6C 1Journal 67Unranked 11

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness. CoRR Stanislav Fort, Balaji Lakshminarayanan
2023	J	`jnl`	A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
2023	A*	`conf`	A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. ICML James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan
2023	J	`jnl`	A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. CoRR James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Jeremiah Zhe Liu, Xiuye Gu, Yin Cui, Dustin Tran, Balaji Lakshminarayanan
2023	J	`jnl`	An instance-dependent simulation framework for learning with label noise. Mach. Learn. Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin
2023	J	`jnl`	Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models. CoRR Yunhao Ge, Jie Ren, Jiaping Zhao, Kaifeng Chen, Andrew Gallagher, Laurent Itti, Balaji Lakshminarayanan
2023	A*	`conf`	Improving Zero-shot Generalization and Robustness of Multi-Modal Models. CVPR Yunhao Ge, Jie Ren, Andrew Gallagher, Yuxiao Wang, Ming-Hsuan Yang, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, Jiaping Zhao
2023	—	`conf`	Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. EMNLP (Findings) Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu
2023	J	`jnl`	Morse Neural Networks for Uncertainty Quantification. CoRR Benoit Dherin, Huiyi Hu, Jie Ren, Michael W. Dusenberry, Balaji Lakshminarayanan
2023	A*	`conf`	Out-of-Distribution Detection and Selective Generation for Conditional Language Models. ICLR Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu
2023	A*	`conf`	Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. ICLR Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Balaji Lakshminarayanan, Deepak Ramachandran
2023	J	`jnl`	Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. CoRR Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Martin Strobel, Balaji Lakshminarayanan, Deepak Ramachandran
2023	—	`conf`	Self-Evaluation Improves Selective Generation in Large Language Models. ICBINB Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan
2023	J	`jnl`	Self-Evaluation Improves Selective Generation in Large Language Models. CoRR Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan
2023	—	`conf`	What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. SaTML Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel
2023	J	`jnl`	What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. CoRR Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel
2022	J	`jnl`	A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
2022	J	`jnl`	Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
2022	J	`jnl`	Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions. Medical Image Anal. 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
2022	J	`jnl`	Improving Zero-shot Generalization and Robustness of Multi-modal Models. CoRR Yunhao Ge, Jie Ren, Yuxiao Wang, Andrew Gallagher, Ming-Hsuan Yang, Laurent Itti, Hartwig Adam, Balaji Lakshminarayanan, Jiaping Zhao
2022	J	`jnl`	Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. CoRR Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu
2022	J	`jnl`	Out-of-Distribution Detection and Selective Generation for Conditional Language Models. CoRR Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu
2022	J	`jnl`	Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR 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
2022	J	`jnl`	Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 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
2022	—	`conf`	Test Sample Accuracy Scales with Training Sample Density in Neural Networks. CoLLAs Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi
2022	A*	`conf`	Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. NeurIPS Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang
2021	J	`jnl`	A Realistic Simulation Framework for Learning with Label Noise. CoRR Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin
2021	J	`jnl`	A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan
2021	J	`jnl`	BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark. CoRR Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai
2021	A*	`conf`	Combining Ensembles and Data Augmentation Can Harm Your Calibration. ICLR Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
2021	J	`jnl`	Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
2021	A	`conf`	Density of States Estimation for Out of Distribution Detection. AISTATS Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon
2021	J	`jnl`	Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions. CoRR 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
2021	A*	`conf`	Exploring the Limits of Out-of-Distribution Detection. NeurIPS Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
2021	J	`jnl`	Exploring the Limits of Out-of-Distribution Detection. CoRR Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
2021	J	`jnl`	Normalizing Flows for Probabilistic Modeling and Inference. J. Mach. Learn. Res. George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
2021	J	`jnl`	Predicting Unreliable Predictions by Shattering a Neural Network. CoRR Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio
2021	J	`jnl`	Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. CoRR Kehang Han, Balaji Lakshminarayanan, Jeremiah Z. Liu
2021	A*	`conf`	Soft Calibration Objectives for Neural Networks. NeurIPS Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs
2021	J	`jnl`	Soft Calibration Objectives for Neural Networks. CoRR Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs
2021	J	`jnl`	Sparse MoEs meet Efficient Ensembles. CoRR 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
2021	J	`jnl`	Task-agnostic Continual Learning with Hybrid Probabilistic Models. CoRR Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu
2021	A*	`conf`	Training independent subnetworks for robust prediction. ICLR Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran
2021	J	`jnl`	Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR 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
2021	J	`jnl`	Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. CoRR Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang
2020	A*	`conf`	AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. ICLR Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
2020	A*	`conf`	Bayesian Deep Ensembles via the Neural Tangent Kernel. NeurIPS Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
2020	J	`jnl`	Bayesian Deep Ensembles via the Neural Tangent Kernel. CoRR Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
2020	J	`jnl`	Combining Ensembles and Data Augmentation can Harm your Calibration. CoRR Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
2020	J	`jnl`	Density of States Estimation for Out-of-Distribution Detection. CoRR Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon
2020	A*	`conf`	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran
2020	J	`jnl`	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran
2020	J	`jnl`	Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek
2020	J	`jnl`	Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. CoRR Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan
2020	A*	`conf`	Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020	J	`jnl`	Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. CoRR Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020	J	`jnl`	Training independent subnetworks for robust prediction. CoRR Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran
2019	J	`jnl`	AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. CoRR Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
2019	J	`jnl`	Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift. CoRR Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek
2019	A*	`conf`	Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. NeurIPS Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado
2019	J	`jnl`	Deep Ensembles: A Loss Landscape Perspective. CoRR Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
2019	J	`jnl`	Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality. CoRR Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan
2019	—	`conf`	Do Deep Generative Models Know What They Don't Know? ICLR (Poster) Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019	A*	`conf`	Hybrid Models with Deep and Invertible Features. ICML Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019	J	`jnl`	Hybrid Models with Deep and Invertible Features. CoRR Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019	A*	`conf`	Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. ICML Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan
2019	A*	`conf`	Likelihood Ratios for Out-of-Distribution Detection. NeurIPS Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
2019	J	`jnl`	Likelihood Ratios for Out-of-Distribution Detection. CoRR Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
2019	J	`jnl`	Normalizing Flows for Probabilistic Modeling and Inference. CoRR George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
2018	J	`jnl`	Adapting Auxiliary Losses Using Gradient Similarity. CoRR Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Razvan Pascanu, Balaji Lakshminarayanan
2018	J	`jnl`	Distribution Matching in Variational Inference. CoRR Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed
2018	J	`jnl`	Do Deep Generative Models Know What They Don't Know? CoRR Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2018	J	`jnl`	Learning from Delayed Outcomes with Intermediate Observations. CoRR Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, András György
2018	—	`conf`	Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. ICLR (Poster) William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow
2017	J	`jnl`	Comparison of Maximum Likelihood and GAN-based training of Real NVPs. CoRR Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan
2017	J	`jnl`	Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server. J. Mach. Learn. Res. Leonard Hasenclever, Stefan Webb, Thibaut Liénart, Sebastian J. Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh
2017	J	`jnl`	Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees. CoRR Daniel Zoran, Balaji Lakshminarayanan, Charles Blundell
2017	J	`jnl`	Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. CoRR William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow
2017	—	`conf`	Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. NIPS Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017	J	`jnl`	The Cramer Distance as a Solution to Biased Wasserstein Gradients. CoRR Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos
2017	J	`jnl`	Variational Approaches for Auto-Encoding Generative Adversarial Networks. CoRR Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed
2016	—	`—`	Decision trees and forests: a probabilistic perspective. Balaji Lakshminarayanan
2016	J	`jnl`	Learning in Implicit Generative Models. CoRR Shakir Mohamed, Balaji Lakshminarayanan
2016	A	`conf`	Mondrian Forests for Large-Scale Regression when Uncertainty Matters. AISTATS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2016	J	`jnl`	Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. CoRR Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2016	A	`conf`	The Mondrian Kernel. UAI Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	Approximate Inference with the Variational Holder Bound. CoRR Guillaume Bouchard, Balaji Lakshminarayanan
2015	J	`jnl`	Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. CoRR Yee Whye Teh, Leonard Hasenclever, Thibaut Liénart, Sebastian J. Vollmer, Stefan Webb, Balaji Lakshminarayanan, Charles Blundell
2015	A	`conf`	Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. UAI Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó
2015	J	`jnl`	Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. CoRR Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó
2015	J	`jnl`	Latent IBP Compound Dirichlet Allocation. IEEE Trans. Pattern Anal. Mach. Intell. Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard
2015	J	`jnl`	Mondrian Forests for Large-Scale Regression when Uncertainty Matters. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2015	A	`conf`	Particle Gibbs for Bayesian Additive Regression Trees. AISTATS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	Particle Gibbs for Bayesian Additive Regression Trees. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2014	—	`conf`	Distributed Bayesian Posterior Sampling via Moment Sharing. NIPS Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
2014	—	`conf`	Mondrian Forests: Efficient Online Random Forests. NIPS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2014	J	`jnl`	Mondrian Forests: Efficient Online Random Forests. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2013	J	`jnl`	Inferring ground truth from multi-annotator ordinal data: a probabilistic approach CoRR Balaji Lakshminarayanan, Yee Whye Teh
2013	J	`jnl`	Top-down particle filtering for Bayesian decision trees CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2013	—	`conf`	Top-down particle filtering for Bayesian decision trees. ICML (3) Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2011	—	`conf`	Inference in Supervised latent Dirichlet allocation. MLSP Balaji Lakshminarayanan, Raviv Raich
2011	A	`conf`	Robust Bayesian Matrix Factorisation. AISTATS Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau
2009	C	`conf`	A Syllable-Level Probabilistic Framework for Bird Species Identification. ICMLA Balaji Lakshminarayanan, Raviv Raich, Xiaoli Z. Fern