We study zeroth-order optimisation under context distributional uncertainty, a setting commonly tackled using Bayesian optimisation (BO). A prevailing strategy to make BO more robust to the complex and noisy nature of data is to employ an ensemble as the surrogate model, thereby mitigating the weaknesses of any single model. In this study, we propose a novel algorithm for Ensemble Distributionally Robust Bayesian Optimisation that remains computationally tractable while managing continuous context. We obtain theoretical sublinear regret bounds, improving current state-of-the-art results. We show that our method's empirical behaviour aligns with its theoretical guarantees.

Deep learning-based image stitching pipelines are typically divided into three cascading stages: registration, fusion, and rectangling. Each stage requires its own network training and is tightly coupled to the others, leading to error propagation and posing significant challenges to parameter tuning and system stability. This paper proposes the Simple and Robust Stitcher (SRStitcher), which revolutionizes the image stitching pipeline by simplifying the fusion and rectangling stages into a unified inpainting model, requiring no model training or fine-tuning. We reformulate the problem definitions of the fusion and rectangling stages and demonstrate that they can be effectively integrated into an inpainting task. Furthermore, we design the weighted masks to guide the reverse process in a pre-trained largescale diffusion model, implementing this integrated inpainting task in a single inference. Through extensive experimentation, we verify the interpretability and generalization capabilities of this unified model, demonstrating that SRStitcher outperforms state-of-the-art methods in both performance and stability.

Recall that for the n-way multiple choice setting, n 1 choices are negative pairs and only one pair is positive. Accordingly, for n = 4, 3 distractors are sampled, each with an incorrect pose embedding, while the 4th choice contains the matching pose embedding for the given vision and audio embeddings. In other words, the fusion embedding consisting of the vision and audio embeddings is kept as the anchor while negatives are sampled from the pose embeddings only. Of the 3 negative pose embeddings, 2 are considered "easy" negatives, sampled randomly from the entire training set, while the last one is a "hard" negative, sampled randomly from a pool of 25 embeddings corresponding to the 25 nearest neighbours of the anchor vision embedding. In the n = 3case, 2 hard negatives and no easy negatives are used, with the same nearest neighbour sampling method based on the anchorshared weights embedding.

Providing a very low critical probability pc means that certification occurs when the simulation ends after a large number of iterations m. We introduce `c the threshold associated to pc s.t. Table 5 shows that this approximation is excellent even for large pc. This shows that mis a little larger than mc = log(pc)/log(1 1/N). This section assumes that X = xo + σ X with X N(0n; In) and that h(x) = x>g τ with g Rn and kgk= 1 (w.l.o.g.).

In this supplementary document, we provide additional materials to supplement our main submission. In Section A, we talk about the societal impacts of our work In Section B, we provide detailed experimental settings as well as further evaluation results on CIFAR-10 and ImageNet. We also provide the comparison with input transformation-based defense methods. In Section D, we give the proofs w.r.t. In Section E, we give the proofs w.r.t. The proofs of Theorem 3 are given in Section F. In Section C, we provide the analysis and evaluation of decision-based attacks. Deep neural networks (DNNs) have been successfully applied in many safety-critical tasks, such as autonomous driving, face recognition and verification, etc. And adversarial samples have posed a serious threat to machine learning systems.