I am broadly interested in scalable algorithms for ranking and retrieval. My current interests are in distance metric learning algorithms, structured prediction, and recommender systems.
Lim, D.K.H., McFee, B., and Lanckriet, G.R.G. Robust Structural Metric Learning. In Proc. International Conference of Machine Learning (ICML), 2013 (pdf)
Lim, D.K.H., and Lanckriet, G.R.G. Efficient Learning of Mahalanobis Metrics for Ranking. In Proc. International Conference of Machine Learning (ICML), 2014 (pdf)(Supplementary)
Daryl Lim, Julian McAuley, and Gert Lanckriet. Top-N Recommendation with Missing Implicit Feedback. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys), 2015.
Daryl K. H. Lim and Gert Lanckriet. Efficient Metric Learning for Ranking and Retrieval. Under review by The Journal of Machine Learning Research.
Deep Learning Course Project
Investigating whether edge-like filters would be observed in bases learnt by unsupervised feature learning algorithms (e.g. Sparse coding, sparse autoencoders) on non-natural images
Unsupervised Learning Course Project
Using Latent Dirichlet Allocation to learn topic distributions over song lyrics
Matrix Factorization Presentation
Tutorial presentation on PCA and SVD-based collaborative filtering given at Data Mining Boot Camp
To be updated