Andrew Ilyas @andrew_ilyas
Machine Learning PhD student at MIT, advised by Aleksander Madry and Costis Daskalakis. andrewilyas.com Joined February 2013-
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Our second (and final) blog post on model components is now out: we show that component attributions enable model editing! See the blog post for more: gradientscience.org/modelcomponent… Paper: arxiv.org/abs/2404.11534 (this time with no typos :) Code: github.com/MadryLab/model…
Our second (and final) blog post on model components is now out: we show that component attributions enable model editing! See the blog post for more: gradientscience.org/modelcomponent… Paper: arxiv.org/abs/2404.11534 (this time with no typos :) Code: github.com/MadryLab/model…
New work with @andrew_ilyas and @aleks_madry on tracing predictions back to individual components (conv filters, attn heads) in the model! Paper: arxiv.org/abs/2404.11534 Thread: 👇
New work with @andrew_ilyas and @aleks_madry on tracing predictions back to individual components (conv filters, attn heads) in the model! Paper: arxiv.org/abs/2404.11534 Thread: 👇
New work led by @harshays_ on studying how model components affect predictions! Paper link: arxiv.org/abs/2404.11534 Blog: gradientscience.org/modelcomponents
New work led by @harshays_ on studying how model components affect predictions! Paper link: arxiv.org/abs/2404.11534 Blog: gradientscience.org/modelcomponents
Congratulations @ShibaniSan, this is extremely well deserved!
Congratulations @ShibaniSan, this is extremely well deserved!
We are pleased to announce the 2021 AAAI/ACM SIGAI Dissertation Award Winner. Congratulations to Shibani Santurkar, Massachusetts Institute of Technology for her work entitled Machine Learning Beyond Accuracy: A Features Perspective On Model Generalization. And congratulations…
We tend to choose LM training data via intuitive notions of text quality... but LMs are often *un*intuitive. Is there a better way? w/@logan_engstrom, @axel_s_feldmann: we select better data by modeling how models learn from data. Our method, DsDm, can greatly improve…
How do we attribute an image generated by a diffusion model back to the training data? w/ @kris_georgiev1 @josh_vendrow @hadisalmanX @smsampark we show that it’s useful to look at each step of the diffusion process:
I gave a keynote this week at the fantastic ATTRIB Workshop #NeurIPS2023 "What does scale give us: Why we are building a ladder 🪜 to the moon 🌕" Some of you asked for my slides, sharing below: docs.google.com/presentation/d… Thanks to the organizers for a fantastic workshop! 🔥
Next - @sarahookr on understanding the effects of scale and data on ML model performance! (ATTRIB workshop, Rm 271-273)
What types of attributions do modern LLM applications require? Check out our contributed talk [Friday, 10:30am] by @TeddiWorledge at the ATTRIB23 workshop [Rm 271-273] on "Unifying Corroborative and Contributive Attributions in Large Language Models" arxiv.org/abs/2311.12233
Any burning ML questions? The ATTRIB workshop is hosting a panel on "The Future of Attribution in ML" tomorrow at 11AM and is soliciting questions! Submit them by TODAY 11:59PM to hear answers at the panel tomorrow! forms.gle/Yd5N3Ti6kKfqij… More info: attrib-workshop.cc
Gautam Kamath @thegautamkamath
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Ekaterina Lobacheva @KateLobacheva
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MIT CSAIL @MIT_CSAIL
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Ziad Obermeyer @oziadias
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768 Followers 677 Following technical staff @openai, previously theory @berkeleyeecs | fan of graphs, crosswords, turtles, bad puns, running, and Survivor, among other things
Theo Diamandis @theo_diamandis
3K Followers 826 Following optimization phd @mit. head intern @baincapcryptoProud Advisor Moment: Giannis passes his Phd Proposal with flying colors. You can see this great thread about (some of) his work. The new in-painting attack for checking if an image is in the training set of a diffusion model is one of my favorites.
This week I successfully passed my Ph.D. proposal 🎉 The title of the talk: "Generative Models from Lossy Measurements". Here is a little 🧵 about it
HELM Lite v1.2.0 is out! Datasets: NarrativeQA, NaturalQA, OpenbookQA, MMLU, MATH, GSM8K, LegalBench, MedQA, WMT14 Results (we still need to add Claude 3, which requires more prompt finagling): crfm.stanford.edu/helm/lite/v1.2…
Our second (and final) blog post on model components is now out: we show that component attributions enable model editing! See the blog post for more: gradientscience.org/modelcomponent… Paper: arxiv.org/abs/2404.11534 (this time with no typos :) Code: github.com/MadryLab/model…
How do model components (conv filters, attn heads) collectively transform examples into predictions? Is it possible to somehow dissect how *every* model component contributes to a prediction? w/ @harshays_ @andrewilyas, we introduce a framework for tackling this question!…
New work with @andrew_ilyas and @aleks_madry on tracing predictions back to individual components (conv filters, attn heads) in the model! Paper: arxiv.org/abs/2404.11534 Thread: 👇
How do model components (conv filters, attn heads) collectively transform examples into predictions? Is it possible to somehow dissect how *every* model component contributes to a prediction? w/ @harshays_ @andrewilyas, we introduce a framework for tackling this question!…
How do model components (conv filters, attn heads) collectively transform examples into predictions? Is it possible to somehow dissect how *every* model component contributes to a prediction? w/ @harshays_ @andrewilyas, we introduce a framework for tackling this question!…
We can train a diffusion using only these noisy images and produce natural looking images. New results from our paper "Consistent Diffusion Meets Tweedie"
Consistent Diffusion Meets Tweedie. Our latest paper introduces an exact framework to train/finetune diffusion models like Stable Diffusion XL solely with noisy data. A year's worth of work breakthrough in reducing memorization and its implications on copyright 🧵
Can you train a generative model using only noisy data? If you can, this would alleviate the issue of training data memorization plaguing certain genAI models. In exciting work with @giannis_daras and @AlexGDimakis we show how to do this for diffusion-based generative models.
Consistent Diffusion Meets Tweedie. Our latest paper introduces an exact framework to train/finetune diffusion models like Stable Diffusion XL solely with noisy data. A year's worth of work breakthrough in reducing memorization and its implications on copyright 🧵
TacticAI is an AI system that can advise football coaches on tactics & plays deepmind.google/discover/blog/… This was a really fun project to work on in collaboration with my much loved Liverpool FC @LFC - fingers crossed we win the league this year to give Klopp a fitting send off!
When we looked for a final strategic partner to fulfill our dreams for series C, only one name came to mind: @nvidia. We are so thrilled to welcome our newest investors, collaboration partners, and long-time research buddies to the @AbridgeHQ family: abridge.com/press-release/…
Congratulations @ShibaniSan, this is extremely well deserved!
We are pleased to announce the 2021 AAAI/ACM SIGAI Dissertation Award Winner. Congratulations to Shibani Santurkar, Massachusetts Institute of Technology for her work entitled Machine Learning Beyond Accuracy: A Features Perspective On Model Generalization. And congratulations…
We are pleased to announce the 2021 AAAI/ACM SIGAI Dissertation Award Winner. Congratulations to Shibani Santurkar, Massachusetts Institute of Technology for her work entitled Machine Learning Beyond Accuracy: A Features Perspective On Model Generalization. And congratulations…
Excited to share our new applied causal machine learning book causalml-book.org is available online. Any feedback/corrections greatly appreciated!
It's such an honor to be considered in the company of the many brilliant people that have been promoted to the rank of associate professor. #humblebrag
What shall it be Twitter: platitude, hot take, or humble brag? #itakerequests
This subset is hard to find directly–we typically optimize loss with respect to parameters, not training data! Instead, DsDm approximates the solution using datamodels (arxiv.org/abs/2202.00622), a framework for modeling how ML models predict from data.
We tend to choose LM training data via intuitive notions of text quality... but LMs are often *un*intuitive. Is there a better way? w/@logan_engstrom, @axel_s_feldmann: we select better data by modeling how models learn from data. Our method, DsDm, can greatly improve…
I agree with Percy's call to arms for open models, I think it will be very important for our world. As investments for academic compute are growing, what is lagging the most is high quality datasets. The academic and open source community must work more on dataset curation and…
My TEDAI talk from Oct 2023 is now live: go.ted.com/percyliang It was a hard talk to give: 1. I memorized it - felt more like giving a piano recital than an academic talk. 2. I wanted it to be timeless despite AI changing fast…still ok after 3 months. Here’s what I said:
How do we attribute an image generated by a diffusion model back to the training data? w/ @kris_georgiev1 @josh_vendrow @hadisalmanX @smsampark we show that it’s useful to look at each step of the diffusion process:
I gave a keynote this week at the fantastic ATTRIB Workshop #NeurIPS2023 "What does scale give us: Why we are building a ladder 🪜 to the moon 🌕" Some of you asked for my slides, sharing below: docs.google.com/presentation/d… Thanks to the organizers for a fantastic workshop! 🔥
Join us at the panel where we discuss the big picture for attribution and explaining model behavior.
Up next at ATTRIB (in 5 minutes): A panel on the future of Attribution in ML, with @PangWeiKoh, @sarahookr, and @katherine1ee ! Room 271-273
🔥🎉 @maxdoesresearch presents “when less is more: investigating data pruning for pretraining LLMs at scale” Attrib Workshop 2023
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