Asia
Efficient Discrepancy Testing for Learning with Distribution Shift Gautam Chandrasekaran UT Austin Adam R. Klivans UT Austin Vasilis Kontonis UT Austin Konstantinos Stavropoulos
Our approach generalizes and improves all prior work on TDS learning: (1) we obtain universal learners that succeed simultaneously for large classes of test distributions, (2) achieve near-optimal error rates, and (3) give exponential improvements for constant depth circuits.
Towards Effective Planning Strategies for Dynamic Opinion Networks
Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.
Transfer Learning for Diffusion Models
However, their success heavily relies on the availability of a large number of training samples.
Unchosen Experts Can Contribute Too: Unleashing MoE Models ' Power by Self-Contrast
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency.
AED: Adaptable Error Detection for Few-shot Imitation Policy Jia-Fong Y eh 1 Kuo-Han Hung 1, Pang-Chi Lo1, Chi-Ming Chung 1
We introduce a new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based on visual observations in novel environments. The potential to cause serious damage to surrounding areas limits the application of FSI policies in real-world scenarios.
