Lizhong Ding

Research output

Research output per year 2010 2012 2014 2018 2019 2022

• 12 Conference contribution
• 11 Article
• 3 Conference article

Research output per year

Research output per year

Search results

• 2022

  SAIL: Self-Augmented Graph Contrastive Learning

  Yu, L., Pei, S., Ding, L., Zhou, J., Li, L., Zhang, C. & Zhang, X., 30 Jun 2022, AAAI-22 Technical Tracks 8. Association for the Advancement of Artificial Intelligence, p. 8927-8935 9 p. (Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022; vol. 36).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Graph Neural Network 100%
  • Contrastive Learning 100%
  • Nodes 100%
  • Learning Framework 25%
  • Regularization 25%
  29 Citations (Scopus)
• 2019

  Approximate kernel selection with strong approximate consistency

  Ding, L., Liu, Y., Liao, S., Li, Y., Yang, P., Pan, Y., Huang, C., Shao, L. & Gao, X., 2019, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. AAAI press, p. 3462-3469 8 p. (33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Approximation (Algorithm) 100%
  • Convergence Property 100%
  • Approximated Matrix 100%
  • Algorithm Selection 100%
  • Sampling Distribution 100%
  6 Citations (Scopus)

• Dynamically visual disambiguation of keyword-based image search

  Yao, Y., Sun, Z., Shen, F., Liu, L., Wang, L., Zhu, F., Ding, L., Wu, G. & Shao, L., 2019, Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. Kraus, S. (ed.). International Joint Conferences on Artificial Intelligence, p. 996-1002 7 p. (IJCAI International Joint Conference on Artificial Intelligence; vol. 2019-August).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Image Search 100%
  • Disambiguation 100%
  • Classification Models 33%
  • Annotation 33%
  • Learning Network 33%
  13 Citations (Scopus)

• Linear kernel tests via empirical likelihood for high-dimensional data

  Ding, L., Liu, Z., Li, Y., Liao, S., Liu, Y., Yang, P., Yu, G., Shao, L. & Gao, X., 2019, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. AAAI press, p. 3454-3461 8 p. (33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Empirical Likelihood 100%
  • Likelihood Ratio Statistic 85%
  • Limiting Distribution 28%
  • High Power 14%
  • Empirical Likelihood Method 14%
  8 Citations (Scopus)
• 2018

  Fast cross-validation

  Liu, Y., Lin, H., Ding, L., Wang, W. & Liao, S., 2018, Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. Lang, J. (ed.). International Joint Conferences on Artificial Intelligence, p. 2497-2503 7 p. (IJCAI International Joint Conference on Artificial Intelligence; vol. 2018-July).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Influence Function 100%
  • Kernel Method 33%
  • Experimental Result 16%
  • Taylor Expansion 16%
  • Sample Distribution 16%
  18 Citations (Scopus)

• Randomized kernel selection with spectra of multilevel circulant matrices

  Ding, L., Liao, S., Liu, Y., Yang, P. & Gao, X., 2018, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, p. 2910-2917 8 p. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Kernel Function 100%
  • Selection Criterion 66%
  • Experimental Result 33%
  • Statistical Property 33%
  • Space Complexity 33%
  14 Citations (Scopus)

• Robust asymmetric recommendation via min-max optimization

  Yang, P., Zhao, P., Zheng, V. W., Ding, L. & Gao, X., 27 Jun 2018, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018. Association for Computing Machinery, Inc, p. 1077-1080 4 p. (41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Objective Function 100%
  • Optimization Problem 50%
  • Computational Cost 50%
  • Recommender Systems 50%
  • Reconstruction Error 50%
  4 Citations (Scopus)
• 2014

  Approximate consistency: Towards foundations of approximate kernel selection

  Ding, L. & Liao, S., 2014, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 1 ed. Springer Verlag, p. 354-369 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 8724 LNAI, no. PART 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Approximated Matrix 100%
  • Approximation Algorithms 40%
  • Selection Criterion 40%
  • Kernel Method 20%
  • Approximation (Algorithm) 20%
  9 Citations (Scopus)

• Model selection with the covering number of the ball of RKHS

  Ding, L. & Liao, S., 3 Nov 2014, CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, p. 1159-1168 10 p. (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Covering Number 100%
  • Hypothesis Space 40%
  9 Citations (Scopus)
• 2012

  Nyström approximate model selection for LSSVM

  Ding, L. & Liao, S., 2012, Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 1 ed. p. 282-293 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 7301 LNAI, no. PART 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • least square support vector machine 100%
  • Approximation (Algorithm) 33%
  • Approximated Matrix 33%
  • Experimental Result 16%
  • Low-Rank Representation 16%
  10 Citations (Scopus)
• 2011

  Approximate parameter tuning of support vector machines

  Liao, S., Yang, C. & Ding, L., 2011, 2011 3rd International Workshop on Intelligent Systems and Applications, ISA 2011 - Proceedings. 5873377. (2011 3rd International Workshop on Intelligent Systems and Applications, ISA 2011 - Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Support Vector Machine 100%
  • Approximated Matrix 50%
  • Experimental Result 25%
  • Optimization Problem 25%
  • Quadratic Programming 25%
  2 Citations (Scopus)
• 2010

  Kernel matrix approximation for parameters tuning of support vector regression

  Ding, L. & Liao, S., 2010, ICCASM 2010 - 2010 International Conference on Computer Application and System Modeling, Proceedings. p. V11214-V11218 5623223. (ICCASM 2010 - 2010 International Conference on Computer Application and System Modeling, Proceedings; vol. 11).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Approximated Matrix 100%
  • Support Vector Regression 100%
  • Computational Complexity 40%
  • Approximation Algorithms 40%
  • Experimental Result 20%
  1 Citation (Scopus)