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Zhiqing (he/him) obtained his doctoral degree from Tsinghua University, and he currently works in a bulge bracket investment bank.
Disclaimer: Opinions expressed in this website are those of Zhiqing, and do not necessarily reflect the view of any organizations or institutions.
A comprehensive tutorial of reinforcement learning that covers the fundamental theory and algorithms with Python implementations. The first RL tutorial book with both TensorFlow 2 and PyTorch implementation.
The first hard-copy neural network tutorial book on PyTorch 1.
In distributed multilevel diversity coding, K correlated sources are encoded in a distributed manner such that, given the outputs from any α encoders, the decoder can reconstruct the first α components of each of the corresponding α sources. For this problem, the optimality of a multilayer Slepian-Wolf coding scheme based on binning and superposition is established when K ≤ 3. The same conclusion is shown to hold for general K under a certain symmetry condition, which generalizes a celebrated result by Yeung and Zhang. (Published in IEEE Transactions on Information Theory)
This paper studies the allocation of information flows in noiseless, memoryless communication networks in the presence of omniscient Byzantine adversary. In such networks, adversary may maliciously modify some edge-flows, and legitimate users should resort to network error correction strategies to transmit data reliably. Unlike prior papers, which focused on the capacities of the networks, we consider the expense of resources used by the flow. Hereby, this paper uses an optimization problem to define the concept of minimum cost network error correction flows. (Published in IEEE Transactions on Communications)
Leetcode solution with most views and upvotes. [Problem][Zhiqing's Solution]
Leetcode solution with most views and upvotes. [Problem][Zhiqing's Solution]
Interview with Chinese Software Developer Network (CSDN), one of the biggest networks of software developers in China (20191101). Full text in Chinese
Broadcast on reinforcement learning (20200315). Video and full text in Chinese
