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%
  30 Citations (Scopus)
• 2020

  Approximate Kernel Selection via Matrix Approximation

  Ding, L., Liao, S., Liu, Y., Liu, L., Zhu, F., Yao, Y., Shao, L. & Gao, X., Nov 2020, In: IEEE Transactions on Neural Networks and Learning Systems. 31, 11, p. 4881-4891 11 p., 8959405.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Selection Criterion 100%
  • Approximate Algorithm 100%
  • Approximability 60%
  • Circulant Matrix 60%
  • Kernel Method 30%
  17 Citations (Scopus)

• Fast Cross-Validation for Kernel-Based Algorithms

  Liu, Y., Liao, S., Jiang, S., Ding, L., Lin, H. & Wang, W., 1 May 2020, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 42, 5, p. 1083-1096 14 p., 8611136.

  Research output: Contribution to journal › Article › peer-review

  • Cross-Validation 100%
  • Influence Function 100%
  • Approximates 60%
  • Taylor Expansion 50%
  • Experimental Result 25%
  32 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)

• Deep learning in bioinformatics: Introduction, application, and perspective in the big data era

  Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y. & Gao, X., 15 Aug 2019, In: Methods. 166, p. 4-21 18 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Neural Network 100%
  • Variational Autoencoder 100%
  • Recurrent Neural Network 33%
  271 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)

• Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions

  Alazmi, M., Kuwahara, H., Soufan, O., Ding, L. & Gao, X., 1 Aug 2019, In: Bioinformatics. 35, 15, p. 2634-2643 10 p., bty1035.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Gibbs Free Energy 100%
  • Biochemical Reaction 100%
  • Learning System 25%
  11 Citations (Scopus)

• Two generator game: Learning to sample via linear goodness-of-fit test

  Ding, L., Yu, M., Liu, L., Zhu, F., Liu, Y., Li, Y. & Shao, L., 2019, In: Advances in Neural Information Processing Systems. 32

  Research output: Contribution to journal › Conference article › peer-review

  • Goodness of Fit Test 100%
  • Underlying Distribution 100%
  • Test Statistic 50%
  • Energy Model 25%
  • Probability Distribution 25%
  5 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)

• Multi-class learning: From theory to algorithm

  Li, J., Liu, Y., Yin, R., Zhang, H., Ding, L. & Wang, W., 2018, In: Advances in Neural Information Processing Systems. 2018-December, p. 1586-1595 10 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Multiclass Classification 100%
  • Multi Class Classification 100%
  • Experimental Result 50%
  • Classification Method 50%
  • Learning Algorithm 50%
  36 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)
• 2017

  An approximate approach to automatic kernel selection

  Ding, L. & Liao, S., Mar 2017, In: IEEE Transactions on Cybernetics. 47, 3, p. 554-565 12 p., 7396940.

  Research output: Contribution to journal › Article › peer-review

  • Approximates 100%
  • Approximation (Algorithm) 100%
  • Circulant Matrix 80%
  • Learning Algorithm 50%
  • Fundamental Problem 50%
  22 Citations (Scopus)

• Predictive Nyström method for kernel methods

  Wu, J., Ding, L. & Liao, S., 19 Apr 2017, In: Neurocomputing. 234, p. 116-125 10 p.

  Research output: Contribution to journal › Article › peer-review

  • Predictive Performance 100%
  • Approximate Solution 50%
  • Approximation Method 25%
  6 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)

• Simultaneous multiple parameters tuning in support vector learning

  Ding, L. Z., Jia, L. & Liao, S. Z., 1 Sept 2014, In: Ruan Jian Xue Bao/Journal of Software. 25, 9, p. 2149-2159 11 p.

  Research output: Contribution to journal › Article › peer-review

  • Support Vector 100%
  • Support Vector Machine 100%
  • Model Selection 45%
  • Support Vector Regression 37%
  • Optimization Problem 25%
  2 Citations (Scopus)
• 2012

  Approximate model selection on regularization path for support vector machines

  Ding, L. & Liao, S., Jun 2012, In: Jisuanji Yanjiu yu Fazhan/Computer Research and Development. 49, 6, p. 1248-1255 8 p.

  Research output: Contribution to journal › Article › peer-review

  • Support Vector Machine 100%
  • Regularization 100%
  • Approximated Matrix 42%
  • Experimental Result 28%
  • Approximation (Algorithm) 28%
  7 Citations (Scopus)

• KMA-α: A kernel matrix approximation algorithm for support vector machines

  Ding, L. & Liao, S., Apr 2012, In: Jisuanji Yanjiu yu Fazhan/Computer Research and Development. 49, 4, p. 746-753 8 p.

  Research output: Contribution to journal › Article › peer-review

  • Support Vector Machine 100%
  • Approximated Matrix 100%
  • Cholesky Factorization 40%
  • Sample Matrix 40%
  • Incomplete Cholesky 40%
  10 Citations (Scopus)

• 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)

• Parameter tuning via kernel matrix approximation for support vector machine

  Yang, C., Ding, L. & Liao, S., 2012, In: Journal of Computers (Finland). 7, 8, p. 2047-2054 8 p.

  Research output: Contribution to journal › Article › peer-review

  • Support Vector Machine 100%
  • Approximated Matrix 100%
  • Experimental Result 20%
  • Optimization Problem 20%
  • Quadratic Programming 20%
  2 Citations (Scopus)
• 2011

  Approximate model selection for large scale LSSVM

  Ding, L. & Liao, S., 2011, In: Journal of Machine Learning Research. 20, p. 165-180 16 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Approximates 100%
  • Model Selection 100%
  • least square support vector machine 100%
  • Support Vector Machine 100%
  • Least Square 100%
  26 Citations (Scopus)

• 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)

• Learning with uncertain kernel matrix set

  Jia, L., Liao, S. Z. & Ding, L. Z., Jul 2010, In: Journal of Computer Science and Technology. 25, 4, p. 709-727 19 p.

  Research output: Contribution to journal › Article › peer-review

  • Statistical Error 100%
  • Case Complexity 50%
  7 Citations (Scopus)