Research

My PhD research focused on statistical learning and information theory. Specifically, I was interested in applying tools from information theory and coding theory to high-dimensional statistical estimation problems, such as changepoints detection and random access in large user networks. I study the tradeoff between sample complexity and estimation error from both a fundamental theoretical perspective and an algorithmic perspective.

I’ve been particularly interested in studying message passing algorithms for the estimation problems mentioned above, which is a class of iterative algorithms that are fast to implement and achieve statistical optimality in various problems. I studied both belief propagation and approximate message passing (AMP) as two types of message passing algorithms.

I review papers for the Conference on Neural Information Processing Systems (NeurIPS) (top reviewer 2024), International Conference on Machine Learning (ICML), International Symposium on Information Theory (ISIT), and International Symposium on Topics in Coding (ISTC).

Interests:

• General first-order methods e.g. gradient descent, approximate message passing
• High-dimensional statistical learning
• Information theory and communication systems
• Finite-sample analysis and asymptotic analysis

Preprints:

X. Liu, K. Hsieh and R. Venkataramanan, “Coded many-user multiple access via Approximate Messsage Passing”, accepted to Information Theory, Probability and Statistical Learning: A Festschrift in Honor of Andrew Barron, 2025.

X. Liu, P. Pascual Cobo and R. Venkataramanan, “Many-user multiple access with random user activity: achievability bounds and efficient schemes”, in submission, 2024.

G. Arpino, X. Liu, J. Gontarek and R. Venkataramanan, “Inferring Change Points in High-Dimensional Regression via Approximate Message Passing”, in submission, 2024.

Publications:

X. Liu, P. Pascual Cobo and R. Venkataramanan, “Many-user multiple access with random user activity”, IEEE International Symposium on Information Theory, Athens, Greece, 2024. (poster, talk)

X. Liu, K. Hsieh and R. Venkataramanan, “Coded many-user multiple access via Approximate Messsage Passing”, IEEE International Symposium on Information Theory, Athens, Greece, 2024. (talk)

G. Arpino, X. Liu and R. Venkataramanan, “Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing”, Proceedings of the 41st International Conference on Machine Learning, PMLR 235:1841-1864, 2024. (poster, code)

X. Liu and R. Venkataramanan, “Sketching Sparse Low-Rank Matrices With Near-Optimal Sample- and Time-Complexity Using Message Passing”, in IEEE Transactions on Information Theory, vol. 69, no. 9, pp. 6071-6097, Sept. 2023, doi: 10.1109/TIT.2023.3273181.

X. Liu and R. Venkataramanan, “Sketching sparse low-rank matrices with near-optimal sample- and time-complexity”, IEEE International Symposium on Information Theory, Espoo, Finland, 2022, pp. 3138-3143, doi: 10.1109/ISIT50566.2022.9834693. (talk)

PhD thesis:

X. Liu, “Message Passing Algorithms for Statistical Estimation and Communication”, Apollo-University of Cambridge Repository.