Jie Wang

Uhlmann’s inverse relative gain array conjecture holds strictly up to dimension six—settling a fundamental open problem in matrix positivity before definitively breaking down at dimension seven.

Scaling behavior of neural networks can vary dramatically based on task structure and architectural choices, challenging conventional scaling theories.

Sentiment in social-media conversations is significantly influenced by discourse moves, with specific interventions capable of mitigating negativity or amplifying hostility.

PrivateHub can reduce the accuracy of private application detection by up to 50% without sacrificing the performance of non-private applications.

Achieving competitive inventory decisions with significantly fewer features could revolutionize how businesses approach demand forecasting and inventory management.

Generative models can revolutionize Sinkhorn distributionally robust hypothesis testing by learning least-favorable distributions more efficiently than traditional methods.

Vocabulary-relative queries can nearly double the accuracy of state localization in object histories, transforming how we understand visual tracking.

Continual pretraining on AXIS boosts robot manipulation success rates by 5.8% over previous methods and outperforms existing datasets by over 37%.