Machine Learning Street Talk (MLST)

Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)

22 Aug 2024

1h 28m

15986 words

3 speakers

22 Aug 2024

Audio

Description

Andrew Ilyas, a PhD student at MIT who is about to start as a professor at CMU. We discuss Data modeling and understanding how datasets influence model predictions, Adversarial examples in machine learning and why they occur, Robustness in machine learning models, Black box attacks on machine learning systems, Biases in data collection and dataset creation, particularly in ImageNet and Self-selection bias in data and methods to address it. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api Andrew's site: https://andrewilyas.com/ https://x.com/andrew_ilyas TOC: 00:00:00 - Introduction and Andrew's background 00:03:52 - Overview of the machine learning pipeline 00:06:31 - Data modeling paper discussion 00:26:28 - TRAK: Evolution of data modeling work 00:43:58 - Discussion on abstraction, reasoning, and neural networks 00:53:16 - "Adversarial Examples Are Not Bugs, They Are Features" paper 01:03:24 - Types of features learned by neural networks 01:10:51 - Black box attacks paper 01:15:39 - Work on data collection and bias 01:25:48 - Future research plans and closing thoughts References: Adversarial Examples Are Not Bugs, They Are Features https://arxiv.org/pdf/1905.02175 TRAK: Attributing Model Behavior at Scale https://arxiv.org/pdf/2303.14186 Datamodels: Predicting Predictions from Training Data https://arxiv.org/pdf/2202.00622 Adversarial Examples Are Not Bugs, They Are Features https://arxiv.org/pdf/1905.02175 IMAGENET-TRAINED CNNS https://arxiv.org/pdf/1811.12231 ZOO: Zeroth Order Optimization Based Black-box https://arxiv.org/pdf/1708.03999 A Spline Theory of Deep Networks https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf Scaling Monosemanticity https://transformer-circuits.pub/2024/scaling-monosemanticity/ Adversarial Examples Are Not Bugs, They Are Features https://gradientscience.org/adv/ Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies https://proceedings.mlr.press/v235/bartoldson24a.html Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors https://arxiv.org/abs/1807.07978 Estimation of Standard Auction Models https://arxiv.org/abs/2205.02060 From ImageNet to Image Classification: Contextualizing Progress on Benchmarks https://arxiv.org/abs/2005.11295 Estimation of Standard Auction Models https://arxiv.org/abs/2205.02060 What Makes A Good Fisherman? Linear Regression under Self-Selection Bias https://arxiv.org/abs/2205.03246 Towards Tracing Factual Knowledge in Language Models Back to the Training Data [Akyürek] https://arxiv.org/pdf/2205.11482

Featured

Daniel Jeffries (Unknown)

Unknown

Andrew Ilyas

Transcription

Full Episode

0.031 - 9.923 Andrew Ilyas

if we forget for a second about the data collection process and we just assume that you have a data set, clearly changing that data set is going to change model predictions in some way.

10.844 - 25.802 Andrew Ilyas

And so what we were asking is, can we, without actually thinking about the very mechanistic details of the learning algorithm itself, can we sort of black box that away and think of machine learning as just a map directly from training data set to prediction?

25.782 - 48.162 Daniel Jeffries (Unknown)

The Brave Search API brings affordable developer access to an independent index of over 20 billion web pages. While it's built from scratch without big tech biases, powered by real anonymized human page visits to filter out data and refreshed daily with tens of millions of new pages. Perfect for AI model training and retrieval augmented generation,

48.142 - 71.635 Daniel Jeffries (Unknown)

Whether you're working on language models or information retrieval systems, Brave offers representative data sets and up-to-date information affordably. We'll get started with 2,000 free queries monthly at brave.com forward slash API. Cool. Andrew, it's an honor to have you on MLST. I've been a fan for years now. What's your bio? What's your background?

72.222 - 81.695 Andrew Ilyas

It's an honor to be here. Yeah, so my name is Andrew. I'm a sixth year PhD student at MIT. I'm advised by Alexander Modry and Kostas Daskalakis, hopefully graduating soon.

82.896 - 99.799 Andrew Ilyas

I work a lot on robustness and reliability with a focus on sort of looking at the entire machine learning pipeline from how we collect data to how we make it into data sets to what learning algorithms we use, and really trying to take a step back and look at the entire pipeline to answer questions about robustness and reliability.

100.319 - 112.27 Daniel Jeffries (Unknown)

Yeah, and that's really interesting because I think a lot of people, they put machine learning models into production and then they see all sorts of problems coming up. So we need to have a holistic approach rather than just looking at the individual components.

112.587 - 130.507 Andrew Ilyas

Yeah, absolutely. I think a big goal of my work is what I'd call predictability in machine learning systems. So really whether we can understand the principles behind why they work well enough that when we put them into production, we understand both when they're going to work and when they're not going to work, and also ideally why.

130.547 - 134.832 Daniel Jeffries (Unknown)

And what are your broader interests? I mean, how did you get to where you are today?

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Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)

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