I'm a Machine Learning Scientist at Amazon. I completed my PhD at the Australian National University where I was based at CSIRO Data61, advised by Richard Nock. Before this, I received a BSc (Adv) (Hons Class I and University Medal) from The University of Sydney. I am interested in both theoretical and practical aspects of machine learning with particular research interests in

• Deep Generative Models 
• Distributional Robustness
• Differential Privacy

News

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• [09/23] Our work on Distributionally Robust Bayesian Optimization was accepted to NeurIPS2023!
• [04/23] New work titled "Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups" was accepted to ICCV2023!
• [04/23] Our work titled "Data Preprocessing to Mitigate Bias with Fair Boosted Mollifiers" was accepted to ICML2023!
• [04/22] New preprint on dealing with distributional shifts for Bayesian Optimization!
• [03/22] New work titled "Adversarial Interpretation of Bayesian Inference" was accepted to ALT2022.
• [01/21] New work titled "Regularized Policies are Reward Robust" was accepted to AISTATS2021. We build on the theory of regularizing policies beyond entropy with additional connections to regression losses in Q-learning. 
• [11/20] New preprint on Risk-Monotonicity, which helps us understand instability in training, which is related to an open problem posed at COLT2019.
• [09/20] "Distributional Robustness with IPMs and links to Regularization and GANs" was accepted to NeurIPS2020.
• [06/20] Our work "Optimal Continual Learning has Perfect Memory and is NP-Hard" was accepted to ICML2020.
• [01/20] Our work "Local Differential Privacy for Sampling" was accepted to AISTATS2020.
• [11/19] I gave a talk at the Max Planck Institute for Empirical Inference. (slides)
• [10/19] Our work "A Primal-Dual Link between GANs and Autoencoders" was accepted to NeurIPS2019.

Publications

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1. Distributionally Robust Bayesian Optimization with ϕ-divergences
   Hisham Husain, Vu Nguyen and Anton van den Hengel.
   NeurIPS2023
   
   
2. Data Preprocessing to Mitigate Bias with Fair Boosted Mollifiers
   Alexander Soen, Hisham Husain and Richard Nock.
   ICML2023
   
   
3. Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups.
   Peixia Li, Pulak Purkait, Ajanthan Thalaiyasingam, Majid Abdolshah, Ravi Garg, Hisham Husain, Chenchen Xu, Stephen Gould, Wanli Ouyang and Anton van den Hengel
   ICCV2023 
   
   
4. Adversarial Interpretation of Bayesian Inference.
   Hisham Husain and Jeremias Knoblauch
   ALT2022
   
   
5. Regularized Policies are Reward Robust.
   Hisham Husain, Kamil Ciosek and Ryota Tomioka.
   AISTATS2021
   
   
6. Distributional Robustness with IPMs and links to Regularization and GANs.
   Hisham Husain.
   NeurIPS2020
   
   
7. Optimal Continual Learning has Perfect Memory and is NP-Hard.
   Jeremias Knoblauch, Hisham Husain and Tom Diethe.
   ICML2020
   
   
8. Local Differential Privacy for Sampling.
   Hisham Husain, Borja Balle, Zac Cranko and Richard Nock.
   AISTATS2020
   
   
9. A Primal-Dual Link between GANs and Autoencoders.
   Hisham Husain, Richard Nock and Robert C. Williamson.
   NeurIPS2019

Preprints

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• Confident Sinkhorn Allocation for Psuedo-Labelling
  Vu Nguyen, Hisham Husain, Sachin Farfade and Anton van den Hengel

Contact

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hisham dot husain at protonmail dot com