Adversarial Machine Learning
Machine learning techniques were originally designed for environments in which the training and test data are assumed to be generated from the same (although possibly unknown) distribution and/or process. In the presence of intelligent and adaptive adversaries, however, this working hypothesis is likely to be violated.
Applying machine learning to use cases like fraud, anti-money laundering and infosec presents a unique set of challenges:
- Little or no labeled data
- Non-stationary data distributions
- Model decay
- Counterfactual conditions
This event is entirely devoted to understanding how modern machine learning methods can be applied to these adversarial environments. We will have hands-on workshops as well as talks by leading practitioners from industry and academia.
Read more about adversarial machine learning.
Sep 10, 2016, 9:30a - 5p
620 Folsom St #100
San Francisco, CA 94107
TensorFlow has taken the deep learning world by storm. This workshop will be led by one of TensorFlow’s main contributors, Illia Polosukhin. Illia’s 90 minute, hands-on workshop will cover:
- Dropout - both for preventing overfitting and as mechanics to get "what model doesn't know" (confidence of prediction).
- Augmenting data with adversarial examples - to prevent overfitting and speed up training
- How to limit technical exploits of your models - e.g. how to use different methods to prevent your model going haywire, using different methods (confidence, adversarial examples, discriminator, separate classifiers or just simple whitelists).
Ripple’s distributed financial technology allows for banks around the world to directly transact with each other without the need for a central counterparty or correspondent. Ripple offers plug-and-play products for financial institution as well as a blockchain solution and an innovative technology to connect all the ledgers of this world (from bitcoin to bank ledgers).
While working with financial institutions and regulators, Ripple has build significant trust on the compliance side. This talk will focus on the fraud detection and AML/KYC efforts developed for the Ripple Consensus Ledger (RCL). The RCL is our blockchain solution that make it possible to make transactions across different currencies. We will discuss some unique challenges related to applying machine learning to detect fraudulent activities on blockchain systems with a high velocity of multi-currency transactions.
Stripe processes billions of dollars a year in payments for businesses around the world. To protect our users from fraud, we use machine learning to score and block potentially fraudulent transactions. Many of the issues we faced when building this system are forms of the mult-armed bandit problem, in which an agent must choose been “exploring” multiple options and “exploiting” the option that it currently believes yields the highest payoff. In this talk, I’ll introduce multi-armed bandits and their variants (including contextual and adversarial bandits) and describe how counterfactual evaluation (evaluating the performance of models when you can’t always observe the outcomes of your actions) and deterrence (injecting misinformation to disrupt the bandit problem that fraudsters face) can be posed (and “solved”) in this framework.
Square Capital is Square's business financing services arm, providing capital to sellers in a fast, fair, and intelligent manner. While the data science team focuses on mitigating default and underwriting risk, another concern is fraud/bad actor risk within the Square ecosystem. In this talk, we'll look at modern approaches to assessing merchant fraud risk, as well as the effects of ensembling different models and external datasets to further improve accuracy.
Sift Science is the leading provider of real-time machine learning fraud prevention for online businesses across the globe. Sift Science protects thousand of different businesses from all kinds of fraud and abuse, from a stolen credit card used to buy an airline ticket or a digital game, from a fake apartment or job listing, from a fraudulent money transfer, or from abuse of a referral program.
In this talk, we'll discuss some challenges building a machine learning system to detect all of these diverse kinds of fraud and abuse, including extracting features and training models on custom data specific to each business, leveraging our network of data to help each individual business, learning in real-time, and explaining our system's recommendations to customers.
Coinbase is the largest bitcoin wallet company with over 2.4 million users who have opened over 3.1 million wallets to buy or sell bitcoins across 26 countries. Fraud detection is an important lynchpin of our service that allows users to purchase bitcoins instantaneously while limiting our fraud loss. In this talk, we'll look at how we are detecting fraud using a combination of human analysts and Machine learnt systems.
In particular, we will discuss some unique challenges in a Machine learnt fraud detection system such as training using skewed data sets, challenges around learning new fraud patterns in real-time, designing A/B test environments that allow us to collect training data as well as limit our losses, etc.