publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2021

  1. UDL 2021
    On Pitfalls in OoD Detection: Entropy Considered Harmful
    Kirsch, Andreas, Mukhoti, Jishnu, Amersfoort, Joost, Torr, Philip H.S., and Gal, Yarin
    In Uncertainty & Robustness in Deep Learning at Int. Conf. on Machine Learning (ICML Workshop) 2021
  2. UDL 2021
    Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty
    Mukhoti, Jishnu, Kirsch, Andreas, Amersfoort, Joost, Torr, Philip H.S., and Gal, Yarin
    In Uncertainty & Robustness in Deep Learning at Int. Conf. on Machine Learning (ICML Workshop) 2021
  3. SubSetML 2021
    Active Learning under Pool Set Distribution Shift and Noisy Data
    Kirsch, Andreas, Rainforth, Tom, and Gal, Yarin
    In SubSetML: Subset Selection in Machine Learning: From Theory to Practice (ICML Workshop) 2021
  4. SubSetML 2021
    A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions
    Kirsch, Andreas, Farquhar, Sebastian, and Gal, Yarin
    In SubSetML: Subset Selection in Machine Learning: From Theory to Practice (ICML Workshop) 2021
  5. SubSetML 2021
    A Practical & Unified Notation for Information-Theoretic Quantities in ML
    Kirsch, Andreas, and Gal, Yarin
    In SubSetML: Subset Selection in Machine Learning: From Theory to Practice (ICML Workshop) 2021
  6. SubSetML 2021
    Prioritized training on points that are learnable, worth learning, and not yet learned
    Mindermann, Sören, Razzak, Muhammed, Xu, Winnie, Kirsch, Andreas, Sharma, Mrinank, Morisot, Adrien, Gomez, Aidan N., Farquhar, Sebastian, Brauner, Jan, and Gal, Yarin
    In SubSetML: Subset Selection in Machine Learning: From Theory to Practice (ICML Workshop) 2021
  7. NACI 2021
    Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
    Jesson, Andrew, Tigas, Panagiotis, Amersfoort, Joost, Kirsch, Andreas, Shalit, Uri, and Gal, Yarin
    In The Neglected Assumptions In Causal Inference (ICML Workshop) 2021
  8. Preprint
    Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty
    arXiv Preprint 2021

2020

  1. UDL 2020
    Scalable Training with Information Bottleneck Objectives
    Kirsch, Andreas, Lyle, Clare, and Gal, Yarin
    In Uncertainty & Robustness in Deep Learning at Int. Conf. on Machine Learning (ICML Workshop) 2020
  2. UDL 2020
    Learning CIFAR-10 with a Simple Entropy Estimator Using Information Bottleneck Objectives
    Kirsch, Andreas, Lyle, Clare, and Gal, Yarin
    In Uncertainty & Robustness in Deep Learning at Int. Conf. on Machine Learning (ICML Workshop) 2020
  3. Preprint
    Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning
    Kirsch, Andreas, Lyle, Clare, and Gal, Yarin
    arXiv Preprint 2020

2019

  1. NeurIPS 2019
    BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
    Kirsch*, Andreas, van Amersfoort*, Joost, and Gal, Yarin
    NeurIPS 2019