← Authors

Balaji Lakshminarayanan

31 papers · 2011–2023 · 7 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (6) 🐝 Cross-Pollinator (15) πŸƒ Academic Marathon (12) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (3) 🌱 Topic Pioneer πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (12) ⚑ Prolific Year (5) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (99) πŸ’Ž Century Club (31) ❓ The Questioner (2) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (11)

Conferences

NIPS (10) ICLR (7) ICML (5) AISTATS (4) JMLR (3) CVPR (1) EMNLP (1)

Top co-authors

Yee Whye Teh (9) Jie Ren (8) Dustin Tran (7) Jasper Snoek (6) Jeremiah Zhe Liu (4) Yeming Wen (3) Joshua Dillon (3) Eric Nalisnick (3) Ghassen Jerfel (3) Emily Fertig (2)

Keywords

out-of-distribution detection (6) variational inference (5) uncertainty quantification (5) bayesian neural network (3) uncertainty estimation (3) markov chain monte carlo (3) ensemble learning (3) vision transformer (2) decision tree (2) distribution shift (2) posterior inference (2) generative model (2) online learning (2) image classification (2) bayesian inference (2) distributed learning (2) gaussian process (2) normalizing flow (2) expectation propagation (2) deep ensemble (2)

Papers

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness JMLR 2023
Improving Zero-Shot Generalization and Robustness of Multi-Modal Models CVPR 2023
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise EMNLP 2023
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play ICLR 2023
Out-of-Distribution Detection and Selective Generation for Conditional Language Models ICLR 2023
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models ICML 2023
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation NIPS 2022
Exploring the Limits of Out-of-Distribution Detection NIPS 2021
Soft Calibration Objectives for Neural Networks NIPS 2021
Density of States Estimation for Out of Distribution Detection AISTATS 2021
Normalizing Flows for Probabilistic Modeling and Inference JMLR 2021
Combining Ensembles and Data Augmentation Can Harm Your Calibration ICLR 2021
Training independent subnetworks for robust prediction ICLR 2021
Bayesian Deep Ensembles via the Neural Tangent Kernel NIPS 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors ICML 2020
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty ICLR 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness NIPS 2020
Do Deep Generative Models Know What They Don't Know? ICLR 2019
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift NIPS 2019
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems ICML 2019
Hybrid Models with Deep and Invertible Features ICML 2019
Likelihood Ratios for Out-of-Distribution Detection NIPS 2019
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step ICLR 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles NIPS 2017
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server JMLR 2017
Mondrian Forests for Large-Scale Regression when Uncertainty Matters AISTATS 2016
Particle Gibbs for Bayesian Additive Regression Trees AISTATS 2015
Distributed Bayesian Posterior Sampling via Moment Sharing NIPS 2014
Mondrian Forests: Efficient Online Random Forests NIPS 2014
Top-down particle filtering for Bayesian decision trees ICML 2013
Robust Bayesian Matrix Factorisation AISTATS 2011