262 citations · 652 across the 74 of their papers we have counts for
9 papers · 1 filter
Targeted materials discovery using Bayesian algorithm execution
Sathya Chitturi, Akash Ramdas, Yue Wu +8
Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which…
LLM360: Towards Fully Transparent Open-Source LLMs
Zhengzhong Liu, Aurick Qiao, Willie Neiswanger +25
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs…
Bayesian Optimization Algorithms for Accelerator Physics
Ryan Roussel, Auralee L. Edelen, Tobias Boltz +23
Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simul…
Sample Efficient Preference Alignment in LLMs via Active Exploration
Viraj Mehta, Syrine Belakaria, Vikramjeet Das +7
Preference-based feedback is important for many applications in machine learning where evaluation of a reward function is not feasible. Notable recent examples arise in preference…
Making Scalable Meta Learning Practical
Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn +4
Despite its flexibility to learn diverse inductive biases in machine learning programs, meta learning (i.e., learning to learn) has long been recognized to suffer from poor scalabi…
Importance-aware Co-teaching for Offline Model-based Optimization
Ye Yuan, Can Chen, Zixuan Liu +2
Offline model-based optimization aims to find a design that maximizes a property of interest using only an offline dataset, with applications in robot, protein, and molecule design…
SlimPajama-DC: Understanding Data Combinations for LLM Training
Zhiqiang Shen, Tianhua Tao, Liqun Ma +8
This paper aims to understand the impacts of various data combinations (e.g., web text, Wikipedia, GitHub, books) on the pretraining of large language models using SlimPajama. Slim…
Kernelized Offline Contextual Dueling Bandits
Viraj Mehta, Ojash Neopane, Vikramjeet Das +3
Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning…
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching
Lantao Yu, Tianhe Yu, Jiaming Song +2
Offline imitation learning (IL) promises the ability to learn performant policies from pre-collected demonstrations without interactions with the environment. However, imitating be…