Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems.

Kernel methods augmented with random features give scalable algorithms for learning from big data.

Forthenotations of directions, we use the convention that both the incident and outgoing rays point away from a scattering location. Spherical Harmonics (SH) are orthonormal basis defined on complex numbersovertheunitsphere. Since they were designed for scenes with solid objects, we adapt them to cope with participating media. Our implementation of the Neural Reflectance Field [2] baseline uses the same neural network architecture and positional encoding asinthe original paper. In addition, we employ a visibility MLP [3]tocompute a1-Dvisibility anda1-Dexpected termination depth.

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Active perception,collecting observations to reduce uncertainty about ahidden variable, isone of the fundamental capabilities of an intelligent agent [2]. In multi-agent active perceptiona team of autonomous agents cooperatively gathers observations to infer the value of a hidden variable.

We study the limits and capability of public-data assisted differentially private (P A-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data.