Khurram Javed

kjaved (at) ualberta (dot) ca

I am a PhD student at the RLAI lab advised by Prof. Rich Sutton. I'm interested in building computational intelligence that learns online. Currently, I'm working on developing algorithms for scalable online agent-state construction. In the past, I did my M.Sc with Prof. Martha White on Meta-learning representations for Continual learning, worked at MILA with Prof. Yoshua Bengio on Causality and at TUKL-SEECS with Prof. Faisal Shafait on document analysis and continual learning. I also represented my home country at 55th International Mathematical Olympiad , and XXVI Asian Pacific Mathematical Olympiad, receiving an honorable-mention and a bronze medal respectively.

CV / Google Scholar / Github / Twitter


Scalable Online Recurrent Learning Using Columnar Neural Networks
K. Javed, M. White, R. Sutton

We propose an algorithm to approximates the gradient in for recurrent state learning, and meta-learning in O(n) operations and memory.


Learning Causal Models Online
K. Javed, M. White, Y.Bengio

We propose a method for learning models that do not rely on spurious correlations. Our work builds on IRM (M Arjovsky, 2019) except unlike IRM, it can be implemented online to (1) detect spurious features for a set of given features and (2) learn non-spurious features from sensory data.

Paper / Code

Meta-Learning Representations for Continual Learning
K. Javed and M. White

We propose OML, an objective for learning representations by using catastrophic interference as a training signal. Resultant representations are naturally sparse, accelerate future learning and are robust to forgetting under online updates in continual learning.

Paper / Code / Talk / Poster

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Simultaneous Prediction Intervals for Patient-Specific Survival Curves
S. Sokota, R. D'Orazio, K. Javed, H. Haider and R. Greiner.

We propose a simple drop-in procedure for approximating the Bayesian credible regions of patient-specific survival functions that can be applied to many ISD models.

Paper / Code

Revisiting Distillation and Incremental Classifier Learning
K. Javed and F. Shafait

We isolate the truly effective existing ideas for incremental classifier learning from those that only work under certain conditions. Moreover, we propose a dynamic threshold moving algorithm that can successfully remove bias from an incrementally learned classifier when learning by knowledge distillation.

Paper / Poster / Code

Real-Time Document Localization in Natural Images by Recursive Application of a CNN (Oral)
K. Javed and F. Shafait

We propose a computationally efficient document segmentation algorithm that recursively uses convolutional neural networks to precisely localize a document in a natural image in real-time.

Paper / Slides / Code

Recent Talk

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