no code implementations • 29 May 2024 • Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen
Instead, we propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.
no code implementations • ICCV 2023 • Peixia Li, Pulak Purkait, Thalaiyasingam Ajanthan, Majid Abdolshah, Ravi Garg, Hisham Husain, Chenchen Xu, Stephen Gould, Wanli Ouyang, Anton Van Den Hengel
Each learning group consists of a teacher network, a student network and a novel filter module.
1 code implementation • 13 Jun 2022 • Vu Nguyen, Hisham Husain, Sachin Farfade, Anton Van Den Hengel
CSA outperforms the current state-of-the-art in this practically important area of semi-supervised learning.
no code implementations • 16 Feb 2021 • Hisham Husain, Borja Balle
Our result coincides with that conjectured in (Bubeck et al., 2020) for two-layer networks under the assumption of bounded weights.
no code implementations • 18 Jan 2021 • Hisham Husain, Kamil Ciosek, Ryota Tomioka
Entropic regularization of policies in Reinforcement Learning (RL) is a commonly used heuristic to ensure that the learned policy explores the state-space sufficiently before overfitting to a local optimal policy.
1 code implementation • 1 Dec 2020 • Alexander Soen, Hisham Husain, Richard Nock
Furthermore, when the weak learners are specified to be decision trees, the sufficient statistics of the learned distribution can be examined to provide clues on sources of (un)fairness.
no code implementations • ICML 2020 • Jeremias Knoblauch, Hisham Husain, Tom Diethe
Continual Learning (CL) algorithms incrementally learn a predictor or representation across multiple sequentially observed tasks.
no code implementations • NeurIPS 2020 • Hisham Husain
Our main result shows that DRO under \textit{any} choice of IPM corresponds to a family of regularization penalties, which recover and improve upon existing results in the setting of MMD and Wasserstein distances.
no code implementations • NeurIPS 2019 • Hisham Husain, Richard Nock, Robert C. Williamson
First, we find that the $f$-GAN and WAE objectives partake in a primal-dual relationship and are equivalent under some assumptions, which then allows us to explicate the success of WAE.
no code implementations • 3 Feb 2019 • Hisham Husain, Richard Nock, Robert C. Williamson
First, we find that the $f$-GAN and WAE objectives partake in a primal-dual relationship and are equivalent under some assumptions, which then allows us to explicate the success of WAE.
1 code implementation • 13 Jun 2018 • Hisham Husain, Zac Cranko, Richard Nock
Privacy enforces an information theoretic barrier on approximation, and we show how to reach this barrier with guarantees on the approximation of the target non private density.