Europe
Selecting the independent coordinates of manifolds with large aspect ratios
Neural Information Processing Systems http://nips.cc/
Exploiting LLM Quantization
While LLM quantization's impact on utility has been extensively explored, this work for the first time studies its adverse effects from a security perspective.
Confidence-based Reliable Learningunder Dual Noises
Filteringx-noiseusing W noise. Inspired weutilizex-noise.
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier
Top-level human players perform about 350 actions per minute [1].
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Jay Heo, Hae Beom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang
Neural Information Processing Systems http://nips.cc/
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan Ullman
In this work, westudy algorithms for learning Gaussian mixtures subject todifferential privacy[32], which has become thede facto standard for individual privacy in statistical analysis of sensitive data. Intuitively, differential privacy guarantees that the output of the algorithm does not depend significantly on any one individual's data, which in this case means any one sample.
ANear-OptimalBest-of-Both-WorldsAlgorithm forOnlineLearningwithFeedbackGraphs
We present a computationally efficient algorithm for learning in this framework that simultaneously achieves near-optimal regret bounds in both stochastic and adversarial environments. The bound against oblivious adversaries is O( αT), where T is the time horizon andα is the independence number of the feedback graph.