Jesson, Andrew

1. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
   2018/11/05 by Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras +421 · 40 citations
   #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
2. On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
   2021/02/22 by Joost van Amersfoort, van Amersfoort, Joost, Lewis Smith +7 · 11 citations
   Computer Science · #Gaussian Processes and Bayesian Inference #Advanced Multi-Objective Optimization Algorithms #Machine Learning and Data Classification
3. Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
   2021/06/22 by Kirsch, Andreas, Farquhar, Sebastian, Atighehchian, Parmida +3 · 8 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
4. Estimating the Hallucination Rate of Generative AI
   2024/06/11 by Andrew Jesson, Nicolas Beltran-Velez, Jesson, Andrew +13 · 1 voice · 5 citations
   #cs.LG #stat.ML
5. Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
   2022/04/21 by Jesson, Andrew, Douglas, Alyson, Manshausen, Peter +5 · 4 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
6. Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
   2020/07/01 by Jesson, Andrew, Mindermann, Sören, Shalit, Uri +1 · 3 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
7. Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
   2021/11/03 by Andrew Jesson, Jesson, Andrew, Panagiotis Tigas +9 · 3 citations
   Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Statistical Methods and Inference
8. Hypothesis Testing the Circuit Hypothesis in LLMs
   2024/10/16 by Claudia Shi, Nicolas Beltran-Velez, Shi, Claudia +13 · 1 voice · 4 citations
   Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.LG #stat.ML
9. Interventions, Where and How? Experimental Design for Causal Models at Scale
   2022/03/03 by Tigas, Panagiotis, Annadani, Yashas, Jesson, Andrew +3 · 2 citations
   #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
10. On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
    2018/06/03 by Xiang Jiang, Jiang, Xiang, Mohammad Havaei +13 · 1 citation
    Computer Science · #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #Topic Modeling
11. GeneDisco: A Benchmark for Experimental Design in Drug Discovery
    2021/10/22 by Mehrjou, Arash, Soleymani, Ashkan, Jesson, Andrew +4 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
12. Partial Identification of Dose Responses with Hidden Confounders
    2022/04/24 by Marmarelis, Myrl G., Haddad, Elizabeth, Jesson, Andrew +3 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)
13. ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
    2023/06/02 by Jesson, Andrew, Lu, Chris, Gupta, Gunshi +4 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
14. Differentiable Multi-Target Causal Bayesian Experimental Design
    2023/02/21 by Annadani, Yashas, Tigas, Panagiotis, Ivanova, Desi R. +4 · 1 citation
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Methodology (stat.ME)
15. BatchGFN: Generative Flow Networks for Batch Active Learning
    2023/06/26 by Shreshth A. Malik, Malik, Shreshth A., Salem Lahlou +13 · 1 citation
    Computer Science · Physics and Astronomy · #Machine Learning and Algorithms #Neural Networks and Applications #Model Reduction and Neural Networks
16. Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale
    2024/10/13 by Jesson, Andrew, Jiang, Yiding · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG)