Song Jiang

Orcid: 0000-0002-1076-1626

Affiliations:
  • University of California, Los Angeles, CA, USA


According to our database1, Song Jiang authored at least 11 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models.
CoRR, 2023

A Single Vector Is Not Enough: Taxonomy Expansion via Box Embeddings.
Proceedings of the ACM Web Conference 2023, 2023

CARE: Modeling Interacting Dynamics Under Temporal Environmental Variation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022
Learning Probabilities of Causation from Finite Population Data.
CoRR, 2022

Unit Selection: Learning Benefit Function from Finite Population Data.
CoRR, 2022

Bridging Self-Attention and Time Series Decomposition for Periodic Forecasting.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Estimating Causal Effects on Networked Observational Data via Representation Learning.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021
Deep Learning of Potential Outcomes.
CoRR, 2021

HINTS: Citation Time Series Prediction for New Publications via Dynamic Heterogeneous Information Network Embedding.
Proceedings of the WWW '21: The Web Conference 2021, 2021

2019
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019


  Loading...