Search papers, labs, and topics across Lattice.
4
0
4
6
Breaking the privacy-utility bottleneck, DP-NGD achieves state-of-the-art accuracy and a 10x speedup in convergence for differentially private training.
SIEVE reveals that leveraging reusable structures in demonstration data can lead to more efficient and effective imitation learning, outperforming full-data training with significantly less input.
M-agents harbor 158 unique implementation bugs that could lead to critical failures in real-world applications, and a new tool can identify over 60% of these issues automatically.
Achieve >97.5% of full-data VIT performance with only 16% of the data using ScalSelect, a surprisingly effective and scalable training-free data selection method.
