I am a final year PhD student at the Australian National University and CSIRO Data61, advised by Richard Nock. Before this, I received a BSc (Adv) in Pure Mathematics at the University of Sydney. 

I work on both theoretical and practical aspects of Machine Learning with particular research interests in generative models, privacy and robustness. I am interested in building theoretical foundations and explaining empirical phenomena in deep learning. Research questions I typically answer involve 

1. How does the information-theoretic divergence between probability measures interact with the data and structure of the learning problem?
   
2. How can we train machine learning models to be more robust against adversaries of varying degree?

News

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• [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 and resolves 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. Regularized Policies are Reward Robust.
   Hisham Husain, Kamil Ciosek and Ryota Tomioka.
   AISTATS2021
   
   
2. Distributional Robustness with IPMs and links to Regularization and GANs.
   Hisham Husain.
   NeurIPS2020
   
   
3. Optimal Continual Learning has Perfect Memory and is NP-Hard.
   Jeremias Knoblauch, Hisham Husain and Tom Diethe.
   ICML2020
   
   
4. Local Differential Privacy for Sampling.
   Hisham Husain, Borja Balle, Zac Cranko and Richard Nock.
   AISTATS2020
   
   
5. A Primal-Dual Link between GANs and Autoencoders.
   Hisham Husain, Richard Nock and Robert C. Williamson.
   NeurIPS2019

Preprints

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1. Data Preprocessing to Mitigate Bias with Fair Boosted Mollifiers
   Alexander Soen, Hisham Husain and Richard Nock.
   
   
2. Risk-Monotonicity in Statistical Learning
   Zakaria Mhammedi and Hisham Husain.

Contact

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hisham dot husain at anu dot edu dot au