Baharan Mirzasoleiman
@baharanm
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Assistant professor @UCLAComSci. Better ML via better data, Machine learning, Optimization
Los Angeles, CA
Joined July 2018
We’re thrilled by the amazing response to our #ICML2024 tutorial on “Foundations of data-efficient learning”! Over 1000 attendees joined us. Thank you all! 🙌🌱🌱🌱 ➡️ Slides: https://t.co/j5kUFHOVcJ ➡️ Recording: will be available on Aug 22 🎊🎊
I'll be giving a 2-hour tutorial on data-efficient learning with my PhD student @sjoshi804 on Monday July 22 at #ICML2024. Join us to learn more about this cool topic! ➡️ We can learn better from better data! ⬅️🙌🌱
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Can weak LLMs supervise strong LLMs to obtain superior performance? 🤔 Yes!! 🤩 Which weak models are better supervisors? 🤔 Check out @xue_yihao65785’s awesome #icml2025 paper to know how to identify best weak supervisors without having to collect labels! 🎉🌱
🎉 Our paper “Representations Shape Weak-to-Strong Generalization” is accepted at #ICML2025! We study weak-to-strong generalization (W2SG)—a core problem in superalignment—and offer new insights into the role of models' internal representations in W2SG. 1/
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🚨 Join us at the Workshop on Spurious Correlation & Shortcut Learning (SCSL) at #ICLR2025! @iclr_conf 🗓️ April 28, 2025 📍 Garnet 214-215, Singapore EXPO 🌐 More info: https://t.co/2JQao75s4D
#ICLR2025
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Can we pretrain deep models with small synthetic data? Dataset Distillation via Knowledge Distillation is the way to go! Check out @sjoshi804’s #ICLR2025 paper this Saturday April 26 at 9am, Poster #307 🎉🌱
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Want to train LLMs with better performance and lower GPU memory requirements on data mixtures? Check out this cool #ICLR2025 paper of @dangnth97 this Friday April 25 at 10am, Poster #265 🎉🌱
🎉 Achievement unlocked: having papers with all of my labmates and somehow all ended up at ICLR! I’ll be presenting our work “Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures” at #ICLR2025 🇸🇬 Come by and chat! 👋 on Fri, Apr 25 | 10 AM GMT+8
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Big congrats @YuYang_i on your graduation!! 🎉🎉 🎉 very nice PhD thesis with great contributions 🌱 I’m proud of all you’ve done, and I wish you the best! 💝
Sharing a little late update (before it’s no longer news): I wrapped up my PhD at the end of last year and recently joined @OpenAI’s reasoning team 🍓✨!
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(2/2) Not at UCLA but interested in this work? Check https://t.co/fQuDLpsEo4. Thanks to our fantastic intern @unregularized (soon to return full-time!) for leading this project, along with collaborators @ynd and Atish. Thanks to my UCLA host @baharanm for the seminar invitation!
arxiv.org
Curvature regularization techniques like Sharpness Aware Minimization (SAM) have shown great promise in improving generalization on vision tasks. However, we find that SAM performs poorly in...
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We are delighted that our proposal for the Workshop on “Spurious Correlation and Shortcut Learning: Foundations and Solutions” has been accepted at @iclr_conf 2025, hosting many brilliant keynote speakers and panelists. Stay tuned: https://t.co/g2VAhn0x1R
@SCSLWorkshop 1/
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At NeurIPS? Check out the 2nd workshop on Attributing Model Behavior at Scale (ATTRIB)! Meeting Rm 205-207, starting @ 9am - amazing talks from @SurbhiGoel_ @sanmikoyejo @baharanm, Robert Geirhos, and @coallaoh + exciting contributed talks! More info: https://t.co/Z4j46ddQXO
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I’ll help presenting our #NeurIPS2024 posters tomorrow (Friday):🌱 1- Changing the training data distribution to improve in-distribution performance (11@west #7106) w. @dangnth97 2- Data selection for fine-tuning LLMs with superior performance (16:30@west #5401) w. @YUYANG_UCLA
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Attending NeurIPS'24? Please mark your calendar for our special event "SFU@NeurIPS 2024" https://t.co/cb7s4mkmSA 9 speakers from both academia & industry! Only a 10-min walk from the convention center! Let’s enjoy exciting talks and open discussions!
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Same training and test distribution yields optimal in-distribution performance? @dangnth97 showed in his #NeurIPS2024 paper that this is not true when training with gradient methods!!😮🙃 Changing the training data distribution yields SOTA!🎊 Check it out Fri Dec 13, 11am, PS#5
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Smaller high-quality subsets of language data not only improve LLMs’ training efficiency, but also yield considerably better performance! 🙌🎉🌱 @YUYANG_UCLA has a theoretically-rigorous method for this in her #NeurIPS2024 paper! Check it out on Fri, Dec 13, 16:30, #PS 6
1/ I'll be at #NeurIPS2024 presenting our work SmallToLarge (S2L): Data-efficient Fine-tuning of LLMs! 🚀 What’s S2L? It’s a scalable data selection method that trains a small proxy model to guide fine-tuning for larger models, reducing costs while preserving performance. 👇
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Assist. Prof. Baharan Mirzasoleiman @baharanm of @UCLAComSci & her large-scale machine learning research group @UCLA is part of the new @NSF-@SimonsFdn Institute for Cosmic Origins at @UTAustin that aims to use AI to research the mysteries of the cosmos.
cns.utexas.edu
Stella Offner of University of Texas at Austin Astronomy along with Arya Farahi of the Department of Statistics and Data Sciences lead the new CosmicAI.
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📢 @UCLAComSci is hiring! Open to all CS areas! - Multiple Tenure-track Assistant Professor Positions: https://t.co/9I7DjDHNr7 - Open Rank Teaching Professor Position: https://t.co/8U99f7jX90 (We hired 11 Assistant Professors in the past two years ...)
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The Adversarial Machine Learning Rising Star Awards deadline is in two weeks! Submit your application and help us promote your work and research vision! @trustworthy_ml @LLMSecurity @ml_safety @safe_paper
🚩(1/2) Please help forward the Call for the 2024 Adversarial Machine Learning (AdvML) Rising Star Awards! We promote junior researchers in AI safety, robustness, and security. Award events are hosted at AdvML'Frontiers workshop @NeurIPSConf 2024 Info: https://t.co/cNWnMTZ6HG
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I’ll also present “SafeClip” on behalf of @WenhanYang0315 tomorrow at 1:30pm (poster session 6) #814. See you there! 🙌
CLIP is highly sensitive to data poisoning and backdoor attacks. In this #ICML2024 paper, @WenhanYang0315 proposed an interesting way to pretrain CLIP robust to such attacks without compromising the performance! 🌱🌱 🔗 https://t.co/8cgHn6dsuh Thu, July 25, Poster session 6, #814
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I’ll present “MixPro” on behalf of @xue_yihao65785 tomorrow at 11:30 (poster session 5) poster #800. Come check it out 🙌
ML models are sensitive to distribution shift. Can we adapt a model with only a few examples from the target domain? In this #ICML2024 paper, @xue_yihao65785 proposes an effective way, with nice theoretical analysis🌱 🔗 https://t.co/1v11FF1YlT Thu, July 25, Poster session 5, #800
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📢 We're back with a new edition, this year at @NeurIPSConf in Vancouver! Paper deadline is August 30th, we are looking forward to your submissions!
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