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Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)

1:28:00
 
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Manage episode 435500518 series 2803422
Treść dostarczona przez Machine Learning Street Talk (MLST). Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Machine Learning Street Talk (MLST) lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.

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

  continue reading

190 odcinków

Artwork
iconUdostępnij
 
Manage episode 435500518 series 2803422
Treść dostarczona przez Machine Learning Street Talk (MLST). Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Machine Learning Street Talk (MLST) lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.

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

  continue reading

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