Junhong Shen

Ph.D. Student

Machine Learning Department
Carnegie Mellon University
Advisor: Ameet Talwalkar

Email: junhongs@andrew.cmu.edu
Office: Gates Hillman Centers 8123

Google Scholar   GitHub   Twitter

Bio

I am a second-year Ph.D. student in the Machine Learning Department at CMU, advised by Ameet Talwalkar. My current research focuses on automated machine learning (AutoML), in particular building practical and easily accessible model development tools for diverse tasks in real life.

I obtained my B.S. in Mathematics of Computation at UCLA, where I was fortunate to work with Lin Yang on sample-efficient reinforcement learning. I also studied multi-agent RL and Theory of Mind, advised by Song-Chun Zhu and Ying Nian Wu.

News

• May 2023: I’ll be interning at Microsoft Research New England this summer. Feel free to drop me an email for a coffee chat if you are at Boston!
• April 2023: our work on cross-modal fine-tuning is accepted by ICML 2023 as oral presentation!
• October 2022: we are organizing the 2022 AutoML Decathlon. Check out and participate!
• September 2022: our work on NAS for diverse tasks is accepted by NeurIPS 2022 and we’ve released the code.

Selected Publications

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).

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.

papercodetweetcite

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.

papercodeblogpostcite

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

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{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 = {arXiv},
 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{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}
 }