Machine learning models are famously vulnerable to adversarial attacks: small ad-hoc perturbations of the data that can catastrophically alter the model predictions.

However,in manyapplications there are several different parameters one might wish to vary: for example, scale and density. In contrast to the one-parameter setting, techniques for applying statistics and machine learning in the setting of multiparameter persistence are not well understood due to the lack of a concise representationoftheresults.

Collaborative inference leverages diverse features provided by different agents (e.g.,sensors)formoreaccurateinference. Acommonsetupiswhereeachagent sends its embedded features instead of the raw data to the Fusion Center (FC) for joint prediction. In this setting, we consider inference phase attacks when asmall fraction of agents is compromised.

Our goal is to bridge the gap between deep reinforcement learning research and industrial manufacturing by creating simulation environments that model real-world factories.

Furthermore, letφ: X Z be an encoder s.t. Then, there is no functionφ s.t. Let there be a subset in the invariant spaceB Z, and suppose that we have marginal invariance inthelatent space:PS(φ(X) B) = PT(φ(X) B), B. Define thepre-image ofB as: A={a X:φ(a) B}. Let A X be a region s.t. We followed the procedure in [2], and used a mixture kernel function ofq RBF kernels: κ(z1,z2) = Pq i=1ηiexp{ ||z1 z2||2}/σ2i, where σ2i is the kernel width of the i-th kernel, and ηi is a mixing weight which we set to1/q.

This hasbeen shown to be insufficient for generating optimal representation for classification, and to find conditionally invariant representations, usually strong assumptions are needed.

Components of machine learning systems are not (yet) perceived as security hotspots. Secure coding practices, such as ensuring that no execution paths depend on confidential inputs, have not yet been adopted by ML developers. We initiate the study of code security of ML systems by investigating how nucleus sampling--a popular approach forgeneratingtext,used forapplications such as auto-completion--unwittingly leakstextstypedbyusers.