242 citations · 331 across the 9 of their papers we have counts for
9 papers
Rethinking the Design of Reinforcement Learning-Based Deep Research Agents
Yi Wan, Jiuqi Wang, Liam Li +3
Large language models (LLMs) augmented with external tools are increasingly deployed as deep research agents that gather, reason over, and synthesize web information to answer comp…
Cross-Modal Fine-Tuning: Align then Refine
Junhong Shen, Liam Li, Lucio M. Dery +4
Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many ot…
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak, Renbo Tu, Tian Li +4
Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are…
Rethinking Neural Operations for Diverse Tasks
Nicholas Roberts, Mikhail Khodak, Tri Dao +3
An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains. Motivated by this goal, we study the problem of enabling users…
On Data Efficiency of Meta-learning
Maruan Al-Shedivat, Liam Li, Eric Xing +1
Meta-learning has enabled learning statistical models that can be quickly adapted to new prediction tasks. Motivated by use-cases in personalized federated learning, we study the o…
Geometry-Aware Gradient Algorithms for Neural Architecture Search
Liam Li, Mikhail Khodak, Maria-Florina Balcan +1
Recent state-of-the-art methods for neural architecture search (NAS) exploit gradient-based optimization by relaxing the problem into continuous optimization over architectures and…