IJCAI 2019

Pushing the boundaries of convex optimization

Convex optimization problems, which involve the minimization of a convex function over a convex set, can be approximated in theory to any fixed precision in polynomial time. However, practical algorithms are known only for special cases. An important question is whether it is possible to develop algorithms for a broader subset of convex optimization problems that are efficient in both theory and practice.

Continue reading

Making Neural Networks Robust with New Perspectives

IBM researchers have partnered with scientists from MIT, Northeastern University, Boston University and University of Minnesota to publish two papers on novel attacks and defenses for graph neural networks and on a new robust training algorithm called hierarchical random switching at IJCAI 2019.

Continue reading

Improving the Scalability of AI Planning when Memory is Limited

We report new research results relevant to AI planning in our paper, "Depth-First Memory-Limited AND/OR Search and Unsolvability in Cyclic Search Spaces," presented at the International Joint Conference on Artificial Intelligence, IJCAI-19.

Continue reading

Causal Knowledge Extraction: An Evaluation using Automated Binary Causal Question Answering

At IJCAI'19, IBM researchers present new results on causal knowledge extraction from large amounts of text for applications in enterprise risk management.

Continue reading

IBM Research AI @ IJCAI 2019

At IJCAI-19, IBM Research AI will present technical papers exploring a variety of topics in AI including neurosymbolic reasoning, explainability, adversarial robustness, computational linguistics, graph analysis, optimization, and reinforcement learning.

Continue reading