PhD Student, Northeastern University

Graduation Date
May 1, 2019
Areas of interest
Ordinal embedding, metric learning, information retrieval, recommender systems, multi-armed bandits, statistical modeling and optimization techniques, computational geometry.
Javed Aslam


Thesis Proposal

My proposed thesis topic is described here.

Current Research

Practitioners of Machine Learning and related fields commonly seek out embeddings of object collections into some Euclidean space. These embeddings are useful for dimensionality reduction, for data visualization, as concrete representations of abstract notions of similarity for similarity search, or as features for some downstream learning task such as web search or sentiment analysis. A wide array of such techniques exist, ranging from traditional (PCA, MDS) to trendy (word2vec, deep learning).

Most existing techniques depend on exact numerical input of some form: object features, distance estimates, etc. For datasets derived from user behavior or opinion, however, such quantitative data is either too sparse, too noisy, or too difficult to interpret for us to rely on such exact numerical values. Instead of exactly trusting the numerical values of similarity or distance estimates, one can rely on the ordering of those values, e.g. by sorting all items by their estimated similarity to a given query item and discarding the (unreliable) exact numerical similarities. The relationship between objects is reduced to a set of ordinal triplets, such as "object a is closer to object b than to object c." One can then seek an embedding which preserves this ordinal information, and seek to prove guarantees for the degree to which such an embedding must recover the true distances or similarities between items.

My research has explored several key questions for producing large-scale ordinal embeddings, including:

  • What is the minimal subset of ordinal data sufficient to preserve ordering perfectly?
  • How and when can distance information be inferred from ordinal information? For instance, when can we say that if some set of triplets holds between objects a, b, c, ... in ℝd, then (e.g.) the ratio of distances ||a - b|| / ||a - c|| must lie within some small interval?
  • How can we obtain ordinal embeddings of n objects into ℝd in just O(n d log n) or better time, with good accuracy guarantees?
I have solved these and other questions, and am now working to publish these results and apply them to large-scale user behavior datasets.



(Summaries available upon request)

In Preparation

(Summaries available upon request)

  • Fast Geometric Ordinal Embedding
    Jesse Anderton, Michaël Perrot, Damien Garreau, and Ulrike von Luxburg
  • Density-Sensitive Coverings and Packings
    Jesse Anderton and Javed A. Aslam
  • Adaptively Pruning Features for Boosted Decision Trees
    Maryam Aziz, Jesse Anderton, and Javed A. Aslam
  • Offline evaluation of music recommendations
    Zahra Nazari, Jesse Anderton, and Benjamin Carterette
  • Improved ranking for music recommendations
    Jesse Anderton, Zahra Nazari, Joe Cauteruccio, Jason Uh, Benjamin Carterette, and Fernando Diaz