If you are a PhD student at Massachusetts Institute of Technology (MIT) (sorry about this constraint) looking for an #internship and are interested in any of the topics listed in this 🧵, please get in touch with me or Hao Wang, as my group is seeking talented students to join us at the MIT-IBM Watson AI Lab

Excited to announce the Workshop on Information-theoretic Methods for Trustworthy Machine Learning at the Simons Institute from May 22nd-25th! Stay tuned for more details: simons.berkeley.edu/workshops/asu-…

Paper: arxiv.org/abs/2306.00206 Code: github.com/azizanlab/repr…

Mario Diaz Torres, a brilliant researcher and mathematician, passed away suddenly on August 31st. Mario Diaz was a rising star in the LatAm math community and was doing exceptional work in information theory, differential privacy, and related areas. bit.ly/4d9XGPB

[1/x] can we scale small, open LMs to o1 level? Using classical probabilistic inference methods, YES! Joint MIT CSAIL / Red Hat AI Innovation Team work introduces a particle filtering approach to scaling inference w/o any training! check out …abilistic-inference-scaling.github.io

Join us this Friday for Random Samples, a weekly AI talk series from Red Hat AI Innovation Team. Topic: The State of LLM Compression — From Research to Production We’ll explore quantization, sparsity, academic vs. real-world benchmarks, and more. Join details in comments 👇

⚠️When using inference-time scaling, don't waste compute on reasoning steps likely to lead to dead ends. 💡In our latest work, we show that a calibrated PRM can estimate how likely each reasoning step is to reach the correct answer, enabling more efficient inference-time scaling.