Our Experience at KDD2021
By: Jaime D. Acevedo-Viloria
Tags: Conference, Kdd, Data Science, Machine Learning, Graph

Our Experience at KDD2021

Our AI Team at RappiBank had the pleasure to both attend and present one of our papers at KDD 2021 (More on that to come!) Jaime D. Acevedo-Viloria Follow Sep 6 · 4 min read

KDD is one of the top AI conferences in the world, with the mission of providing a premier forum for advancement, education, and adoption of the “science” of knowledge discovery and data mining from all types of data stored in computers and networks of computers.

It was an all-around amazing opportunity for us to better learn our craft, learn from amazing speakers, and hear innovative ideas on the subject. We will go through a small summary of what we personally considered the highlights of this conference, doing a bit of an emphasis on the Workshop for Machine Learning in Finance where our paper was accepted.

How to learn more with less in Graphs!

As you may know from our previous post we are really into Graph Machine Learning in RappiBank. As such, there is no other way to start these highlights without Neil Shah’s amazing presentation about Graph Machine Learning with Scarce Labels.

In a field that we personally have been looking into semi-supervised and self-supervised solutions for this same problem of scarcity, Neil presented a brilliant way to learn more with graphs through modifications of the edges in the graph. With the idea, that through the removal of noisy edges and the adding of possible missing edges we can more closely approximate an ideal graph, where classes are perfectly separated into the same sections of the representations.

These generated augmented graphs achieve better performance in graphs with practically every GNN model! As Neil said himself:

You can learn better representations without labels by exploiting good priors through graph augmentation.

GCN performance on the original Zachary’s Karate CLub graph in (a), and three augmented graph variants in (b-d). Black, solid-blue, dashed-blue edges denote original graph edges, newly added, and removed edges respectively.

Fairness and performance, both equally important

Afterward came Pedro Bizarro to talk to us about the many amazing projects Feedzai is working on. From fairness in AI to models that actually can explain themselves, it was an exciting ride through many subjects that have room for innovation and are critical to our work.

Our personal favorite was Fairband, a bandit-based fairness-aware hyperparameter optimization algorithm. A simple to implement, model-agnostic methodology to consider the tradeoff between performance and fairness (if there is one!). The authors even found out that without extra training cost, Fairband consistently finds configurations that obtain substantially improved fairness at a comparatively small decrease in predictive accuracy.

Fairness-accuracy tradeoff of thousands of models on the Adult dataset. In orange, the linear regression relationship between accuracy and fairness; in the red rectangle, the top 10% models with the highest accuracy; in light gray, the fairness accuracy Pareto frontier; marked with an A, the model with the highest accuracy; marked with a B, a model with 0.8% lower accuracy and 44.8% higher fairness than A.

For more information on how the authors define fairness, or how do they solve this multi-objective optimization problem make sure to check out the paper!

How to use deep learning to create user-merchant recommendations

Finally, Mahashweta Das talked to us about an impressive Transfer Learning approach that embeds users and merchants in the same contextual vectorial space for a novel and impressive recommender system of merchants for users. With the key intuition to:

Guide neural collaborative filtering with domain invariant components shared across both dense and sparse domains, improving the user and item representation learned in the sparse domains.

They show the effectiveness and scalability of their proposed approach on two public datasets and a massive transaction dataset from Visa. Make sure to check the paper for further information about it!

General Overview

It was nice to see the emphasis on ethical issues like fairness evaluation and interpretability methodologies for models. It is really important for us as developers of this community to not forget about ethics and make a better place when developing state-of-the-art models and innovating.

It was also interesting to see some presentations centered around Graph Machine Learning, such as the one presented by Neil Shah, a couple of others by MIT and the National University of Singapore that are yet to be publicly shared, and ourself’s (Make sure to check our future post on this!).