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Balaji Lakshminarayanan
Person information
- affiliation: Google Brain
- affiliation (PhD 2016): University College London, UK
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2020 – today
- 2024
- [i59]Stanislav Fort, Balaji Lakshminarayanan:
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness. CoRR abs/2408.05446 (2024) - 2023
- [j8]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. 24: 42:1-42:63 (2023) - [j7]Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin
:
An instance-dependent simulation framework for learning with label noise. Mach. Learn. 112(6): 1871-1896 (2023) - [c35]Yunhao Ge, Jie Ren, Andrew Gallagher, Yuxiao Wang, Ming-Hsuan Yang, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, Jiaping Zhao:
Improving Zero-shot Generalization and Robustness of Multi-Modal Models. CVPR 2023: 11093-11101 - [c34]Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu:
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. EMNLP (Findings) 2023: 1324-1336 - [c33]Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan:
Self-Evaluation Improves Selective Generation in Large Language Models. ICBINB 2023: 49-64 - [c32]Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu:
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. ICLR 2023 - [c31]Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Balaji Lakshminarayanan, Deepak Ramachandran:
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. ICLR 2023 - [c30]James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. ICML 2023: 547-568 - [c29]Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel:
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. SaTML 2023: 365-376 - [i58]Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Martin Strobel, Balaji Lakshminarayanan, Deepak Ramachandran:
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. CoRR abs/2302.05807 (2023) - [i57]James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Jeremiah Zhe Liu, Xiuye Gu, Yin Cui, Dustin Tran, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. CoRR abs/2302.06235 (2023) - [i56]Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel:
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. CoRR abs/2302.11188 (2023) - [i55]Yunhao Ge, Jie Ren, Jiaping Zhao, Kaifeng Chen, Andrew Gallagher, Laurent Itti, Balaji Lakshminarayanan:
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models. CoRR abs/2305.17207 (2023) - [i54]Benoit Dherin, Huiyi Hu, Jie Ren, Michael W. Dusenberry, Balaji Lakshminarayanan:
Morse Neural Networks for Uncertainty Quantification. CoRR abs/2307.00667 (2023) - [i53]Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan:
Self-Evaluation Improves Selective Generation in Large Language Models. CoRR abs/2312.09300 (2023) - 2022
- [j6]Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi
Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions. Medical Image Anal. 75: 102274 (2022) - [j5]James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (2022) - [j4]Vincent Fortuin
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022) - [c28]Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi:
Test Sample Accuracy Scales with Training Sample Density in Neural Networks. CoLLAs 2022: 629-646 - [c27]Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang:
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. NeurIPS 2022 - [i52]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR abs/2205.00403 (2022) - [i51]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:
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR abs/2207.07411 (2022) - [i50]Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu:
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. CoRR abs/2209.15558 (2022) - [i49]Yunhao Ge, Jie Ren, Yuxiao Wang, Andrew Gallagher, Ming-Hsuan Yang, Laurent Itti, Hartwig Adam, Balaji Lakshminarayanan, Jiaping Zhao:
Improving Zero-shot Generalization and Robustness of Multi-modal Models. CoRR abs/2212.01758 (2022) - [i48]Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu:
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. CoRR abs/2212.09928 (2022) - 2021
- [j3]George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan:
Normalizing Flows for Probabilistic Modeling and Inference. J. Mach. Learn. Res. 22: 57:1-57:64 (2021) - [c26]Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon:
Density of States Estimation for Out of Distribution Detection. AISTATS 2021: 3232-3240 - [c25]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran:
Training independent subnetworks for robust prediction. ICLR 2021 - [c24]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation Can Harm Your Calibration. ICLR 2021 - [c23]Stanislav Fort, Jie Ren, Balaji Lakshminarayanan:
Exploring the Limits of Out-of-Distribution Detection. NeurIPS 2021: 7068-7081 - [c22]Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs:
Soft Calibration Objectives for Neural Networks. NeurIPS 2021: 29768-29779 - [i47]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:
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions. CoRR abs/2104.03829 (2021) - [i46]Stanislav Fort, Jie Ren, Balaji Lakshminarayanan:
Exploring the Limits of Out-of-Distribution Detection. CoRR abs/2106.03004 (2021) - [i45]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
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i44]Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio:
Predicting Unreliable Predictions by Shattering a Neural Network. CoRR abs/2106.08365 (2021) - [i43]Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan:
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR abs/2106.09022 (2021) - [i42]Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu:
Task-agnostic Continual Learning with Hybrid Probabilistic Models. CoRR abs/2106.12772 (2021) - [i41]Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai:
BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark. CoRR abs/2107.08189 (2021) - [i40]Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin:
A Realistic Simulation Framework for Learning with Label Noise. CoRR abs/2107.11413 (2021) - [i39]Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs:
Soft Calibration Objectives for Neural Networks. CoRR abs/2108.00106 (2021) - [i38]Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou:
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR abs/2110.02609 (2021) - [i37]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:
Sparse MoEs meet Efficient Ensembles. CoRR abs/2110.03360 (2021) - [i36]Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang:
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. CoRR abs/2110.07858 (2021) - [i35]Kehang Han, Balaji Lakshminarayanan, Jeremiah Z. Liu:
Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. CoRR abs/2111.12951 (2021) - 2020
- [c21]Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan:
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. ICLR 2020 - [c20]Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML 2020: 2782-2792 - [c19]Bobby He, Balaji Lakshminarayanan, Yee Whye Teh:
Bayesian Deep Ensembles via the Neural Tangent Kernel. NeurIPS 2020 - [c18]Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS 2020 - [i34]Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR abs/2005.07186 (2020) - [i33]Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon:
Density of States Estimation for Out-of-Distribution Detection. CoRR abs/2006.09273 (2020) - [i32]Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. CoRR abs/2006.10108 (2020) - [i31]Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek:
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR abs/2006.10963 (2020) - [i30]Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan:
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. CoRR abs/2007.05134 (2020) - [i29]Bobby He, Balaji Lakshminarayanan, Yee Whye Teh:
Bayesian Deep Ensembles via the Neural Tangent Kernel. CoRR abs/2007.05864 (2020) - [i28]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran:
Training independent subnetworks for robust prediction. CoRR abs/2010.06610 (2020) - [i27]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation can Harm your Calibration. CoRR abs/2010.09875 (2020)
2010 – 2019
- 2019
- [c17]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Do Deep Generative Models Know What They Don't Know? ICLR (Poster) 2019 - [c16]Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan:
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. ICML 2019: 4324-4332 - [c15]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Hybrid Models with Deep and Invertible Features. ICML 2019: 4723-4732 - [c14]Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado:
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. NeurIPS 2019: 13969-13980 - [c13]Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. NeurIPS 2019: 14680-14691 - [i26]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Hybrid Models with Deep and Invertible Features. CoRR abs/1902.02767 (2019) - [i25]Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek:
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift. CoRR abs/1906.02530 (2019) - [i24]Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. CoRR abs/1906.02845 (2019) - [i23]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan:
Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality. CoRR abs/1906.02994 (2019) - [i22]Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan:
Deep Ensembles: A Loss Landscape Perspective. CoRR abs/1912.02757 (2019) - [i21]George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan:
Normalizing Flows for Probabilistic Modeling and Inference. CoRR abs/1912.02762 (2019) - [i20]Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan:
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. CoRR abs/1912.02781 (2019) - 2018
- [c12]William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow:
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. ICLR (Poster) 2018 - [i19]Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed:
Distribution Matching in Variational Inference. CoRR abs/1802.06847 (2018) - [i18]Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, András György:
Learning from Delayed Outcomes with Intermediate Observations. CoRR abs/1807.09387 (2018) - [i17]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Do Deep Generative Models Know What They Don't Know? CoRR abs/1810.09136 (2018) - [i16]Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Razvan Pascanu, Balaji Lakshminarayanan:
Adapting Auxiliary Losses Using Gradient Similarity. CoRR abs/1812.02224 (2018) - 2017
- [j2]Leonard Hasenclever, Stefan Webb, Thibaut Liénart, Sebastian J. Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh:
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server. J. Mach. Learn. Res. 18: 106:1-106:37 (2017) - [c11]Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell:
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. NIPS 2017: 6402-6413 - [i15]Daniel Zoran, Balaji Lakshminarayanan, Charles Blundell:
Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees. CoRR abs/1702.08833 (2017) - [i14]Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan:
Comparison of Maximum Likelihood and GAN-based training of Real NVPs. CoRR abs/1705.05263 (2017) - [i13]Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos:
The Cramer Distance as a Solution to Biased Wasserstein Gradients. CoRR abs/1705.10743 (2017) - [i12]Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed:
Variational Approaches for Auto-Encoding Generative Adversarial Networks. CoRR abs/1706.04987 (2017) - [i11]William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow:
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. CoRR abs/1710.08446 (2017) - 2016
- [b1]Balaji Lakshminarayanan:
Decision trees and forests: a probabilistic perspective. University College London, UK, 2016 - [c10]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests for Large-Scale Regression when Uncertainty Matters. AISTATS 2016: 1478-1487 - [c9]Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh:
The Mondrian Kernel. UAI 2016 - [i10]Shakir Mohamed, Balaji Lakshminarayanan:
Learning in Implicit Generative Models. CoRR abs/1610.03483 (2016) - [i9]Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell:
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. CoRR abs/1612.01474 (2016) - 2015
- [j1]Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard:
Latent IBP Compound Dirichlet Allocation. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 321-333 (2015) - [c8]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Particle Gibbs for Bayesian Additive Regression Trees. AISTATS 2015 - [c7]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. UAI 2015: 405-414 - [i8]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Particle Gibbs for Bayesian Additive Regression Trees. CoRR abs/1502.04622 (2015) - [i7]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. CoRR abs/1503.02551 (2015) - [i6]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests for Large-Scale Regression when Uncertainty Matters. CoRR abs/1506.03805 (2015) - [i5]Guillaume Bouchard, Balaji Lakshminarayanan:
Approximate Inference with the Variational Holder Bound. CoRR abs/1506.06100 (2015) - [i4]Yee Whye Teh, Leonard Hasenclever, Thibaut Liénart, Sebastian J. Vollmer, Stefan Webb, Balaji Lakshminarayanan, Charles Blundell:
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. CoRR abs/1512.09327 (2015) - 2014
- [c6]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests: Efficient Online Random Forests. NIPS 2014: 3140-3148 - [c5]Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang:
Distributed Bayesian Posterior Sampling via Moment Sharing. NIPS 2014: 3356-3364 - [i3]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests: Efficient Online Random Forests. CoRR abs/1406.2673 (2014) - 2013
- [c4]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Top-down particle filtering for Bayesian decision trees. ICML (3) 2013: 280-288 - [i2]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Top-down particle filtering for Bayesian decision trees. CoRR abs/1303.0561 (2013) - [i1]Balaji Lakshminarayanan, Yee Whye Teh:
Inferring ground truth from multi-annotator ordinal data: a probabilistic approach. CoRR abs/1305.0015 (2013) - 2011
- [c3]Balaji Lakshminarayanan, Raviv Raich:
Inference in Supervised latent Dirichlet allocation. MLSP 2011: 1-6 - [c2]Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau:
Robust Bayesian Matrix Factorisation. AISTATS 2011: 425-433
2000 – 2009
- 2009
- [c1]Balaji Lakshminarayanan, Raviv Raich, Xiaoli Z. Fern:
A Syllable-Level Probabilistic Framework for Bird Species Identification. ICMLA 2009: 53-59
Coauthor Index
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