"What makes a good fisherman as opposed to other professions?"
This question can be formulated as a k-linear regression problem with self-selection bias.
Alkis, @anaymehrotra.bsky.social, and I design faster local convergence algorithms for this problem:
arxiv.org/abs/2504.07133
(1/7)
Really big thanks to the organizers for the invitation & for putting together such a fun workshop.
My talk: simons.berkeley.edu/talks/andrew...
The paper: arxiv.org/abs/2503.13751
Joint work with @logn.bsky.social, Benjamin Chen, Axel Feldmann, Billy Moses, and @aleksmadry.bsky.social
Had a great time @simonsinstitute.bsky.social last week talking about new & upcoming work on meta-optimization of ML training
tl;dr: we show how to compute gradients *through* the training process & use them to optimize training. Immediate big gains on data selection, poisoning, attribution & more!
We'd love to hear your feedback if you attended the ATTRIB workshop at @neuripsconf.bsky.social 2024!
Please consider taking 2-3 min to fill out this anonymous form: forms.gle/JzGebsx9haD5...
Thank you!🙏
After another very lively poster session, our final talk of the day from @coallaoh.bsky.social - who is talking about the interactions between ML, attribution, and humans!
Our second-last talk of the day - Robert Geirhos on “how do we make attribution easy?”
One great poster session (and lunch) later - Baharan Mirzasoleiman on data selection for large language models!
After some amazing contributed talks, we now have a panel moderated by @sadhika.bsky.social - with @coallaoh.bsky.social Baharan Mirzasoleiman and Robert Geirhos!
Next up, @sanmikoyejo.bsky.social on predicting downstream properties of language models!
Our first talk of the day @ ATTRIB 2024 (Rm 205-207): @surbhigoel.bsky.social on attributing model behavior using synthetic data!
Giving a talk tomorrow at #NeurIPS2024 on the exciting topic of explainability!
At NeurIPS? Come by the 2nd workshop on Attributing Model Behavior at Scale (ATTRIB)!
Meeting Rm 205-207 @ 9am - amazing talks by @surbhigoel.bsky.social @sanmikoyejo.bsky.social Baharan Mirzasoleiman, Robert Geirhos, @coallaoh.bsky.social + exciting contributed talks!
Details: attrib-workshop.cc
You might be looking for smoothed analysis (en.wikipedia.org/wiki/Smoothe...)? Kind of interpolates between worst and average-case: no distribution over problem instances you have to specify but ignores "brittle" worst-case instances. Explains, eg, simplex algorithm (paper: arxiv.org/abs/cs/0111050)
I am recruiting PhD students at Duke!
Please apply to Duke CS or CBB if you are interested in developing new methods and paradigms for NLP/LLMs in healthcare.
For details, see here: monicaagrawal.com/home/researc....
Primarily written for the Operations market, but folks may find this guide I wrote for the job market: gargnikhil.com/files/Nikhil...
