Balaji Lakshminarayanan

Publication Activity (10 Years)

Years Active: 2009-2023
Publications (10 Years): 90

Top Topics

Probabilistic Model

Expectation Propagation

Top Venues

Publications

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 (2023)
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)
Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan
Self-Evaluation Improves Selective Generation in Large Language Models. CoRR (2023)
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 (2023)
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 (2023)
Benoit Dherin, Huiyi Hu, Jie Ren, Michael W. Dusenberry, Balaji Lakshminarayanan
Morse Neural Networks for Uncertainty Quantification. CoRR (2023)
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)
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)
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 (2023)
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 (2023)
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) (2023)
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)
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)
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 (2022)
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. Trans. Mach. Learn. Res. 2022 (2022)
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)
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 (2022)
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. Medical Image Anal. 75 (2022)
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 (2022)
Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi
Test Sample Accuracy Scales with Training Sample Density in Neural Networks. CoLLAs (2022)
Dustin Tran, Jeremiah 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 (2022)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022)
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 (2022)
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs
Soft Calibration Objectives for Neural Networks. CoRR (2021)
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 (2021)
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alex Alemi, Joshua V. Dillon
Density of States Estimation for Out of Distribution Detection. AISTATS (2021)
Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio
Predicting Unreliable Predictions by Shattering a Neural Network. CoRR (2021)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR (2021)
Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin
A Realistic Simulation Framework for Learning with Label Noise. CoRR (2021)
Kehang Han, Balaji Lakshminarayanan, Jeremiah T. Liu
Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. CoRR (2021)
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)
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 (2021)
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 (2021)
Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
Exploring the Limits of Out-of-Distribution Detection. CoRR (2021)
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR (2021)
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
Normalizing Flows for Probabilistic Modeling and Inference. J. Mach. Learn. Res. 22 (2021)
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs
Soft Calibration Objectives for Neural Networks. NeurIPS (2021)
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)
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah 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
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR (2021)
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 (2021)
Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
Exploring the Limits of Out-of-Distribution Detection. NeurIPS (2021)
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 (2021)
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 (2020)
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)
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 (2020)
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 (2020)
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 (2020)
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR (2020)
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)
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
Bayesian Deep Ensembles via the Neural Tangent Kernel. CoRR (2020)
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)
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 (2020)
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
Bayesian Deep Ensembles via the Neural Tangent Kernel. NeurIPS (2020)
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 (2020)
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 (2019)
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)
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)
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)
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 (2019)
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 (2019)
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
Normalizing Flows for Probabilistic Modeling and Inference. CoRR (2019)
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 (2019)
Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
Deep Ensembles: A Loss Landscape Perspective. CoRR (2019)
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
Hybrid Models with Deep and Invertible Features. CoRR (2019)
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)
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
Hybrid Models with Deep and Invertible Features. ICML (2019)
Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, András György
Learning from Delayed Outcomes with Intermediate Observations. CoRR (2018)
Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Razvan Pascanu, Balaji Lakshminarayanan
Adapting Auxiliary Losses Using Gradient Similarity. CoRR (2018)
Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed
Distribution Matching in Variational Inference. CoRR (2018)
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
Do Deep Generative Models Know What They Don't Know? CoRR (2018)
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)
Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed
Variational Approaches for Auto-Encoding Generative Adversarial Networks. CoRR (2017)
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 (2017)
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, 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 (2017)
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. NIPS (2017)
Daniel Zoran, Balaji Lakshminarayanan, Charles Blundell
Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees. CoRR (2017)
Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan
Comparison of Maximum Likelihood and GAN-based training of Real NVPs. CoRR (2017)
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 (2017)
Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh
The Mondrian Kernel. UAI (2016)
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Mondrian Forests for Large-Scale Regression when Uncertainty Matters. AISTATS (2016)
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. CoRR (2016)
Shakir Mohamed, Balaji Lakshminarayanan
Learning in Implicit Generative Models. CoRR (2016)
Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard
Latent IBP Compound Dirichlet Allocation. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Particle Gibbs for Bayesian Additive Regression Trees. CoRR (2015)
Guillaume Bouchard, Balaji Lakshminarayanan
Approximate Inference with the Variational Holder Bound. CoRR (2015)
Yee Whye Teh, Leonard Hasenclever, Thibaut Lienart, Sebastian J. Vollmer, Stefan Webb, Balaji Lakshminarayanan, Charles Blundell
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. CoRR (2015)
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)
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 (2015)
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Particle Gibbs for Bayesian Additive Regression Trees. AISTATS (2015)
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Mondrian Forests for Large-Scale Regression when Uncertainty Matters. CoRR (2015)
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Mondrian Forests: Efficient Online Random Forests. CoRR (2014)
Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
Distributed Bayesian Posterior Sampling via Moment Sharing. NIPS (2014)
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Mondrian Forests: Efficient Online Random Forests. NIPS (2014)
Balaji Lakshminarayanan, Yee Whye Teh
Inferring ground truth from multi-annotator ordinal data: a probabilistic approach CoRR (2013)
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Top-down particle filtering for Bayesian decision trees CoRR (2013)
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Top-down particle filtering for Bayesian decision trees. ICML (3) (2013)
Balaji Lakshminarayanan, Raviv Raich
Inference in Supervised latent Dirichlet allocation. MLSP (2011)
Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau
Robust Bayesian Matrix Factorisation. AISTATS (2011)
Balaji Lakshminarayanan, Raviv Raich, Xiaoli Z. Fern
A Syllable-Level Probabilistic Framework for Bird Species Identification. ICMLA (2009)