Source author record

Shengbo Chen

Shengbo Chen appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record

Information Theory Machine Learning math.IT Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Performance Systems and Control

Catalog footprint

What is connected

4works

8topics

4close collaborators

Actions

Connect this record

Open graph Browse works

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

preprint2026arXiv

Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle

Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essential structural and semantic properties of the original graph. However, most existing methods rely on pair-wise similarity matching, where each node independently searches for its best partner based on global information. This selfishness matching paradigm incurs substantial computational and memory overhead. To address this problem, we shift to a non-selfishness principle that prioritizes the collective interference of neighborhood in coarsening, and propose an efficient method named NOPE, which achieves linear memory consumption and near-linear computational complexity in the number of nodes. Furthermore, we derive a faster variant NOPE*, which reduces O(δ\dot d) interference evaluation to O(d) based on the local isotropy assumption, and consequently alleviates the computational bottleneck for high-degree nodes. Experimental results show that NOPE* achieves 1.8-10\times speedup over NOPE and surpass almost all baselines with 1-3 orders of magnitude acceleration. Meanwhile, learning on coarsened graphs yields comparable performance to original graphs, and can even show superior performance over LLM-based graph reasoning owing to compact graph information. The code can be available at https://github.com/dazonglian/NOPE-main.

preprint2022arXiv

Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning

Model-Agnostic Meta-Learning (MAML) is one of the most successful meta-learning techniques for few-shot learning. It uses gradient descent to learn commonalities between various tasks, enabling the model to learn the meta-initialization of its own parameters to quickly adapt to new tasks using a small amount of labeled training data. A key challenge to few-shot learning is task uncertainty. Although a strong prior can be obtained from meta-learning with a large number of tasks, a precision model of the new task cannot be guaranteed because the volume of the training dataset is normally too small. In this study, first,in the process of choosing initialization parameters, the new method is proposed for task-specific learner adaptively learn to select initialization parameters that minimize the loss of new tasks. Then, we propose two improved methods for the meta-loss part: Method 1 generates weights by comparing meta-loss differences to improve the accuracy when there are few classes, and Method 2 introduces the homoscedastic uncertainty of each task to weigh multiple losses based on the original gradient descent,as a way to enhance the generalization ability to novel classes while ensuring accuracy improvement. Compared with previous gradient-based meta-learning methods, our model achieves better performance in regression tasks and few-shot classification and improves the robustness of the model to the learning rate and query sets in the meta-test set.

preprint2014arXiv

When Queueing Meets Coding: Optimal-Latency Data Retrieving Scheme in Storage Clouds

In this paper, we study the problem of reducing the delay of downloading data from cloud storage systems by leveraging multiple parallel threads, assuming that the data has been encoded and stored in the clouds using fixed rate forward error correction (FEC) codes with parameters (n, k). That is, each file is divided into k equal-sized chunks, which are then expanded into n chunks such that any k chunks out of the n are sufficient to successfully restore the original file. The model can be depicted as a multiple-server queue with arrivals of data retrieving requests and a server corresponding to a thread. However, this is not a typical queueing model because a server can terminate its operation, depending on when other servers complete their service (due to the redundancy that is spread across the threads). Hence, to the best of our knowledge, the analysis of this queueing model remains quite uncharted. Recent traces from Amazon S3 show that the time to retrieve a fixed size chunk is random and can be approximated as a constant delay plus an i.i.d. exponentially distributed random variable. For the tractability of the theoretical analysis, we assume that the chunk downloading time is i.i.d. exponentially distributed. Under this assumption, we show that any work-conserving scheme is delay-optimal among all on-line scheduling schemes when k = 1. When k > 1, we find that a simple greedy scheme, which allocates all available threads to the head of line request, is delay optimal among all on-line scheduling schemes. We also provide some numerical results that point to the limitations of the exponential assumption, and suggest further research directions.

preprint2013arXiv

Life-Add: Lifetime Adjustable Design for WiFi Networks with Heterogeneous Energy Supplies

WiFi usage significantly reduces the battery lifetime of handheld devices such as smartphones and tablets, due to its high energy consumption. In this paper, we propose "Life-Add": a Lifetime Adjustable design for WiFi networks, where the devices are powered by battery, electric power, and/or renewable energy. In Life-Add, a device turns off its radio to save energy when the channel is sensed to be busy, and sleeps for a random time period before sensing the channel again. Life-Add carefully controls the devices' average sleep periods to improve their throughput while satisfying their operation time requirement. It is proven that Life-Add achieves near-optimal proportional-fair utility performance for single access point (AP) scenarios. Moreover, Life-Add alleviates the near-far effect and hidden terminal problem in general multiple AP scenarios. Our ns-3 simulations show that Life-Add simultaneously improves the lifetime, throughput, and fairness performance of WiFi networks, and coexists harmoniously with IEEE 802.11.