Resarch

I’m interested in machine learning and deep learning in general. My current research focuses mainly on the practical side of ML, i.e., developing effective ML tools and pipelines for diverse applications in real life. I approach this problem from the following aspects:

• Automated machine learning (AutoML): I study neural architecture search (NAS) to generate effective and task-specific neural network architectures for different downstream problems.

• Tranfer learning with pretrained models: how can we leverage existing large-scale pretrained models effectvely for solving problems that are not within the model’s pretraining domain and modality?

Talks

• Cross-Modal Fine-Tuning, AI4Science Talks, March 20, 2023.
• DASH: How to Search Over Convolutions, The AutoML Podcast, December 19, 2022.
• Tackling Diverse Tasks with Neural Architecture Search, Deep Learning Machine Learning Journal Club, Mayo Clinic, October 17, 2022.

Publications

Junhong Shen, Atishay Jain, Zedian Xiao, Ishan Amlekar, Mouad Hadji, Aaron Podolny, Ameet Talwalkar
ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data

In Preprint, .

LLM agents are rapidly improving to handle increasingly complex web-based tasks. Most of these agents rely on prompting general-purpose, proprietary models like GPT-4. We explore an alternative approach that fine-tunes open-source LLMs using production-scale workflow data collected from over 250 domains corresponding to 6 billion tokens. This simple yet effective approach shows substantial gains over prompting-based agents on existing benchmarks---ScribeAgent achieves SOTA direct generation performance on Mind2Web and improves the task success rate by 14.1% over the previous best text-only web agents on WebArena.

papercodecite

Junhong Shen, Tanya Marwah, Ameet Talwalkar
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation

In TMLR 2024 & ICML AI4Science Workshop, 2024 (Spotlight).

UPS is developed for solving diverse spatiotemporal PDEs defined over various domains, dimensions, and resolutions. It unifies different PDEs into a consistent representation space and processes diverse collections of PDE data using a unified network architecture that combines LLMs with domain-specific neural operators.

papercodecite

Junhong Shen, Neil Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolo Fusi
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains

In ICML, 2024.

LLMs have demonstrated remarkable proficiency in understanding and generating natural language. However, their capabilities wane in highly specialized domains underrepresented in the pretraining corpus, such as physical and biomedical sciences. This work explores how to repurpose general LLMs into specialized task solvers through a novel, model-agnostic framework for learning custom input tags to condition the LLM.

papercodecite

Junhong Shen, Liam Li, Lucio Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
Cross-Modal Fine-Tuning: Align then Refine

In ICML, 2023 ([Oral](https://icml.cc/virtual/2023/oral/25514)).

ORCA is a general cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. It adapts to a target task via an align-then-refine workflow. Given the target input, ORCA first learns an embedding network that aligns the embedded feature distribution with the pretraining modality. The pretrained model is then fine-tuned on the embedded data to exploit the knowledge shared across modalities.

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Junhong Shen*, Mikhail Khodak*, Ameet Talwalkar
Efficient Architecture Search for Diverse Tasks

In NeurIPS, 2022.

DASH is developed for efficiently solving diverse ML problems outside of the well-researched domains such as vision and natural language processing. Being fast, simple, and broadly applicable, DASH fixes a standard CNN topology and searches for the right kernel sizes and dilation rates that its operations should take on. It expands the network capacity to extract features at multiple resolutions for different types of data while only requiring searching over the operation space.

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Renbo Tu*, Nicholas Roberts*, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks

In NeurIPS Datasets and Benchmarks Track, 2022.

Neural architecture search (NAS) benchmarks and methods prioritize performance on well-studied tasks, e.g., image classification on CIFAR and ImageNet. To mitigate this bias, NAS-Bench-360 is a benchmark suite for evaluating state-of-the-art NAS methods on a diverse set of tasks. The selection spans different application domains, dataset sizes, problem dimensionalities, and learning objectives.

papercodewebsiteblogpostcite

Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
Iterative Teacher-Aware Learning

In NeurIPS, 2021.

In this paper, we propose a gradient optimization based teacher-aware learner who can incorporate teacher’s cooperative intention into the likelihood function and learn provably faster compared with the naive learning algorithms used in previous machine teaching works.

papercodecite

Junhong Shen, Lin F. Yang
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation

In AAAI, 2021.

We propose a theoretically principled nearest neighbor (NN) function approximator that can replace the value networks in deep RL methods. Inspired by human similarity judgments, the NN approximator estimates the action values using rollouts on past observations and can provably obtain a small regret bound that depends only on the intrinsic complexity of the environment.

papercodeslidescite

Junhong Shen, Abdul Hannan Faruqi, Yifan Jiang, Nima Maftoon
Mathematical Reconstruction of Patient-Specific Vascular Networks Based on Clinical Images and Global Optimization

In IEEE Access, 2021.

We developed a computational framework that takes 3D medical images as input and reconstructs complete, patient-specific vascular network models using a mathematical optimization procedure. Our framework extracts major vessels from the images and uses the organ geometry to select vessel termination points. Then, it generates the remainder network based on physiological optimality principles.

papercodeslidescite

Luyao Yuan, Zipeng Fu, Jingyue Shen, Lu Xu, Junhong Shen, Song-Chun Zhu
Emergence of Pragmatics from Referential Game between Theory of Mind Agents

In Emergent Communication Workshop, NeurIPS, 2019.

We integrate the theory of mind (ToM) in a cooperative multi-agent pedagogical situation and propose an adaptive reinforcement learning (RL) algorithm to develop a communication protocol.

paperwebsitecite

@misc{shen2024scribeagent,
 title={ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data},
 author={Junhong Shen and Atishay Jain and Zedian Xiao and Ishan Amlekar and Mouad Hadji and Aaron Podolny and Ameet Talwalkar},
 year={2024},
 eprint={2411.15004},
 archivePrefix={arXiv},
 primaryClass={cs.CL},
 }

@misc{shen2024ups, title={UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation},
 author={Junhong Shen and Tanya Marwah and Ameet Talwalkar},
 year={2024},
 eprint={2403.07187},
 archivePrefix={arXiv},
 primaryClass={cs.LG}
 }

@misc{shen2024tagllm,
 title={Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains}, 
 author={Junhong Shen and Neil Tenenholtz and James Brian Hall and David Alvarez-Melis and Nicolo Fusi},
 year={2024},
 eprint={2402.05140},
 archivePrefix={arXiv},
 primaryClass={cs.LG}
 }

@misc{shen2023orca,
 author = {Shen, Junhong and Li, Liam and Dery, Lucio M. and Staten, Corey and Khodak, Mikhail and Neubig, Graham and Talwalkar, Ameet},
 title = {Cross-Modal Fine-Tuning: Align then Refine},
 publisher = {ICML},
 year = {2023},
 url = {https://arxiv.org/abs/2302.05738}
 }

@inproceedings{shen2022efficient,
 title={Efficient Architecture Search for Diverse Tasks},
 author={Shen, Junhong and Khodak, Mikhail and Talwalkar, Ameet},
 booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
 year={2022}
 }

@inproceedings{nasbench360,
 title={NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks},
 author={Renbo Tu and Nicholas Roberts and Mikhail Khodak and Junhong Shen and Frederic Sala and Ameet Talwalkar},
 booktitle={Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track},
 year={2022}
 }

@inproceedings{yuan2021iterative,
 title={Iterative Teacher-Aware Learning},
 author={Luyao Yuan and Dongruo Zhou and Junhong Shen and Jingdong Gao and Jeffrey L. Chen and Quanquan Gu and Ying Nian Wu and Song-Chun Zhu},
 booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
 year={2021}
 }

@inproceedings{Shen2021TheoreticallyPD,
 title={Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation},
 author={Junhong Shen and Lin F. Yang},
 booktitle={AAAI},
 year={2021}
 }

@article{shen2021reconstruction,
 author={Shen, Junhong and Faruqi, Abdul Hannan and Jiang, Yifan and Maftoon, Nima},
 journal={IEEE Access}, 
 title={Mathematical Reconstruction of Patient-Specific Vascular Networks Based on Clinical Images and Global Optimization}, 
 year={2021},
 volume={9},
 pages={20648-20661}
 }

@article{Yuan2020EmergenceOP,
 title={Emergence of Pragmatics from Referential Game between Theory of Mind Agents},
 author={Luyao Yuan and Zipeng Fu and Jingyue Shen and Lu Xu and Junhong Shen and Song-Chun Zhu},
 journal={NeurIPS 2019 Workshop on Emergent Communication},
 year={2019}
 }