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(I have stopped updating this page as it felt a waste of time; for the most recent drafts, please check my Google Scholar profile)

Conference papers

We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix over a set of local machines. QST is the canonical procedure to characterize the state of a quantum system, which we formulate as a stochastic nonconvex smooth optimization problem. Physically, the estimation of a low-rank density matrix helps characterizing the amount of noise introduced by quantum computation. Theoretically, we prove the local convergence of Local SFGD for a general class of restricted strongly convex/smooth loss functions, i.e., Local SFGD converges locally to a small neighborhood of the global optimum at a linear rate with a constant step size, while it locally converges exactly at a sub-linear rate with diminishing step sizes. With a proper initialization, local convergence results imply global convergence. We validate our theoretical findings with numerical simulations of QST on the Greenberger-Horne-Zeilinger (GHZ) state. 


  		@article{kim2022local,
  title={Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography},
  author={Kim, Junhyung Lyle and Toghani, Mohammad Taha and Uribe, C{\'e}sar A and Kyrillidis, Anastasios},
  journal={arXiv preprint arXiv:2203.11579},
  year={2022}
}

Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple accelerators such that each accelerator is able to hold a partitioned subgraph. However, distributed GCN training incurs prohibitive overhead of communicating node features and feature gradients among partitions for every GCN layer in each training iteration, limiting the achievable training efficiency and model scalability. To this end, we propose PipeGCN, a simple-yet-effective scheme that hides the communication overhead by pipelining inter-partition communication with intra-partition computation. It is non-trivial to pipeline for efficient GCN training, as communicated node features/gradients will become stale and thus can harm the convergence, negating the pipeline benefit. Notably, little is known regarding the convergence rate of GCN training with both stale features and stale feature gradients. This work not only provides a theoretical convergence guarantee but also finds the convergence rate of PipeGCN to be close to that of the vanilla distributed GCN training without staleness. Furthermore, we develop a smoothing method to further improve PipeGCN’s convergence. Extensive experiments show that PipeGCN can largely boost training throughput (up to 2.2x) while achieving the same accuracy as its vanilla counterpart and outperforming existing full-graph training methods. All code will be released publicly upon acceptance. 


  		@inproceedings{wan2021pipegcn,
  title={PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication},
  author={Wan, Cheng and Li, Youjie and Wolfe, Cameron R and Kyrillidis, Anastasios and Kim, Nam Sung and Lin, Yingyan},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

Deep learning practitioners often operate on a computational and monetary budget. Thus, it is critical to design optimization algorithms that perform well under any budget. The linear learning rate schedule is considered the best budget-aware schedule, as it outperforms most other schedules in the low budget regime. On the other hand, learning rate schedules – such as the 30-60-90 step schedule – are known to achieve high performance when the model can be trained for many epochs. Yet, it is often not known a priori whether one’s budget will be large or small; thus, the optimal choice of learning rate schedule is made on a case-by-case basis. In this paper, we frame the learning rate schedule selection problem as a combination of selecting a profile (i.e., the continuous function that models the learning rate schedule), and choosing a sampling rate (i.e., how frequently the learning rate is updated/sampled from this profile). We propose a novel profile and sampling rate combination called the Reflected Exponential (REX) schedule, which we evaluate across seven different experimental settings with both SGD and Adam optimizers. REX outperforms the linear schedule in the low budget regime, while matching or exceeding the performance of several state-of-the-art learning rate schedules (linear, step, exponential, cosine, step decay on plateau, and OneCycle) in both high and low budget regimes. Furthermore, REX requires no added computation, storage, or hyperparameters. 


  		@article{chen2022rex,
  title={REX: Revisiting Budgeted Training with an Improved Schedule},
  author={Chen, John and Wolfe, Cameron and Kyrillidis, Tasos},
  journal={Proceedings of Machine Learning and Systems},
  volume={4},
  pages={64--76},
  year={2022}
}

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