Balaji Lakshminarayanan
31 papers · 2011–2023 · 7 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (10) π Interdisciplinary Bridge π Conference Polyglot (7)
πΊοΈ
Taxonomy Completionist
(10)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Keyword Trendsetter Combo
(3)
π±
Topic Pioneer
π
Triple Crown
π¬
Deep Specialist
(12)
β‘
Prolific Year
(5)
π
Trend Setter
ποΈ
Keyword Collector
(99)
π
Century Club
(31)
β
The Questioner
(2)
π
Conference Pioneer
π₯
Unstoppable
(11)
Conferences
NIPS (10)
ICLR (7)
ICML (5)
AISTATS (4)
JMLR (3)
CVPR (1)
EMNLP (1)
Top co-authors
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