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Automatically understanding the contents of an image is a highly relevant problem in practice. In e-commerce and social media settings, for example, a common problem is to automatically categorize user-provided pictures. Nowadays, a standard approach is to fine-tune pre-trained image models with application-specific data. Besides images, organizations however often also collect collaborative signals in the context of their application, in particular how users interacted with the provided online content, e.g., in forms of viewing, rating, or tagging. Such signals are commonly used for item recommendation, typically by deriving latent user and item representations from the data. In this work, we show that such collaborative information can be leveraged to improve the classification process of new images. Specifically, we propose a multitask learning framework, where the auxiliary task is to reconstruct collaborative latent item representations. A series of experiments on datasets from e-commerce and social media demonstrates that considering collaborative signals helps to significantly improve the performance of the main task of image classification by up to 9.1%.

Permissioned blockchains like Hyperledger Fabric have become quite popular for implementation of enterprise applications. Recent research has mainly focused on improving performance of permissioned blockchains without any consideration of their power/energy consumption. In this paper, we conduct a comprehensive empirical study to understand energy efficiency (throughput/energy) of validator peer in Hyperledger Fabric (a major bottleneck node). We pick a number of optimizations for validator peer from literature (allocated CPUs, software block cache and FPGA based accelerator). First, we propose a methodology to measure power/energy consumption of the two resulting compute platforms (CPU-only and CPU+FPGA). Then, we use our methodology to evaluate energy efficiency of a diverse set of validator peer configurations, and present many useful insights. With careful selection of software optimizations and FPGA accelerator configuration, we improved energy efficiency of validator peer by 10$\times$ compared to vanilla validator peer (i.e., energy-aware provisioning of validator peer can deliver 10$\times$ more throughput while consuming the same amount of energy). In absolute terms, this means 23,000 tx/s with power consumption of 118W from a validator peer using software block cache running on a 4-core server with AMD/Xilinx Alveo U250 FPGA card.

In performative prediction, a predictive model impacts the distribution that generates future data, a phenomenon that is being ignored in classical supervised learning. In this closed-loop setting, the natural measure of performance named performative risk ($\mathrm{PR}$), captures the expected loss incurred by a predictive model \emph{after} deployment. The core difficulty of using the performative risk as an optimization objective is that the data distribution itself depends on the model parameters. This dependence is governed by the environment and not under the control of the learner. As a consequence, even the choice of a convex loss function can result in a highly non-convex $\mathrm{PR}$ minimization problem. Prior work has identified a pair of general conditions on the loss and the mapping from model parameters to distributions that implies the convexity of the performative risk. In this paper, we relax these assumptions and focus on obtaining weaker notions of convexity, without sacrificing the amenability of the $\mathrm{PR}$ minimization problem for iterative optimization methods.

Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modelling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy by differentiating the posterior distribution with GP priors for derivatives of any order. This practically appealing plug-in GP method has been previously perceived as suboptimal and degraded, but this is not necessarily the case. We provide posterior contraction rates for plug-in GPs and establish that they remarkably adapt to derivative orders. We show that the posterior measure of the regression function and its derivatives, with the same choice of hyperparameter that does not depend on the order of derivatives, converges at the minimax optimal rate up to a logarithmic factor for functions in certain classes. This to the best of our knowledge provides the first positive result for plug-in GPs in the context of inferring derivative functionals, and leads to a practically simple nonparametric Bayesian method with guided hyperparameter tuning for simultaneously estimating the regression function and its derivatives. Simulations show competitive finite sample performance of the plug-in GP method. A climate change application on analyzing the global sea-level rise is discussed.

The $k$-Server Problem covers plenty of resource allocation scenarios, and several variations have been studied extensively for decades. We present a model generalizing the $k$-Server Problem by preferences of the requests, where the servers are not identical and requests can express which specific servers should serve them. In our model, requests can either be answered by any server (general requests) or by a specific one (specific requests). If only general requests appear, the instance is one of the original $k$-Server Problem, and a lower bound for the competitive ratio of $k$ applies. If only specific requests appear, a solution with a competitive ratio of $1$ becomes trivial. We show that if both kinds of requests appear, the lower bound raises to $2k-1$. We study deterministic online algorithms and present two algorithms for uniform metrics. The first one has a competitive ratio dependent on the frequency of specific requests. It achieves a worst-case competitive ratio of $3k-2$ while it is optimal when only general requests appear or when specific requests dominate the input sequence. The second has a worst-case competitive ratio of $2k+14$. For the first algorithm, we show a lower bound of $3k-2$, while the second algorithm has a lower bound of $2k-1$ when only general requests appear. The two algorithms differ in only one behavioral rule that significantly influences the competitive ratio. We show that there is a trade-off between performing well against instances of the $k$-Server Problem and mixed instances based on the rule. Additionally, no deterministic online algorithm can be optimal for both kinds of instances simultaneously. Regarding non-uniform metrics, we present an adaption of the Double Coverage algorithm for $2$ servers on the line achieving a competitive ratio of $6$, and an adaption of the Work-Function-Algorithm achieving a competitive ratio of $4k$.

Real-time scheduling theory assists developers of embedded systems in verifying that the timing constraints required by critical software tasks can be feasibly met on a given hardware platform. Fundamental problems in the theory are often formulated as search problems for fixed points of functions and are solved by fixed-point iterations. These fixed-point methods are used widely because they are simple to understand, simple to implement, and seem to work well in practice. These fundamental problems can also be formulated as integer programs and solved with algorithms that are based on theories of linear programming and cutting planes amongst others. However, such algorithms are harder to understand and implement than fixed-point iterations. In this research, we show that ideas like linear programming duality and cutting planes can be used to develop algorithms that are as easy to implement as existing fixed-point iteration schemes but have better convergence properties. We evaluate the algorithms on synthetically generated problem instances to demonstrate that the new algorithms are faster than the existing algorithms.

Both in terrestrial and extraterrestrial environments, the precise and informative model of the ground and the surface ahead is crucial for navigation and obstacle avoidance. The ground surface is not always flat and it may be sloped, bumpy and rough specially in off-road terrestrial scenes. In bumpy and rough scenes the functional relationship of the surface-related features may vary in different areas of the ground, as the structure of the ground surface may vary suddenly and further the measured point cloud of the ground does not bear smoothness. Thus, the ground-related features must be obtained based on local estimates or even point estimates. To tackle this problem, the segment-wise GP-based ground segmentation method with local smoothness estimation is proposed. This method is an extension to our previous method in which a realistic measurement of the length-scale values were provided for the covariance kernel in each line-segment to give precise estimation of the ground for sloped terrains. In this extension, the value of the length-scale is estimated locally for each data point which makes it much more precise for the rough scenes while being not computationally complex and more robust to under-segmentation, sparsity and under-represent-ability. The segment-wise task is performed to estimate a partial continuous model of the ground for each radial range segment. Simulation results show the effectiveness of the proposed method to give a continuous and precise estimation of the ground surface in rough and bumpy scenes while being fast enough for real-world applications.

Many functions have approximately-known upper and/or lower bounds, potentially aiding the modeling of such functions. In this paper, we introduce Gaussian process models for functions where such bounds are (approximately) known. More specifically, we propose the first use of such bounds to improve Gaussian process (GP) posterior sampling and Bayesian optimization (BO). That is, we transform a GP model satisfying the given bounds, and then sample and weight functions from its posterior. To further exploit these bounds in BO settings, we present bounded entropy search (BES) to select the point gaining the most information about the underlying function, estimated by the GP samples, while satisfying the output constraints. We characterize the sample variance bounds and show that the decision made by BES is explainable. Our proposed approach is conceptually straightforward and can be used as a plug in extension to existing methods for GP posterior sampling and Bayesian optimization.

Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has been demonstrated effective is the most fundamental part of GNNs. Although most of GNNs basically follow a message passing manner, litter effort has been made to discover and analyze their essential relations. In this paper, we establish a surprising connection between different propagation mechanisms with a unified optimization problem, showing that despite the proliferation of various GNNs, in fact, their proposed propagation mechanisms are the optimal solution optimizing a feature fitting function over a wide class of graph kernels with a graph regularization term. Our proposed unified optimization framework, summarizing the commonalities between several of the most representative GNNs, not only provides a macroscopic view on surveying the relations between different GNNs, but also further opens up new opportunities for flexibly designing new GNNs. With the proposed framework, we discover that existing works usually utilize naive graph convolutional kernels for feature fitting function, and we further develop two novel objective functions considering adjustable graph kernels showing low-pass or high-pass filtering capabilities respectively. Moreover, we provide the convergence proofs and expressive power comparisons for the proposed models. Extensive experiments on benchmark datasets clearly show that the proposed GNNs not only outperform the state-of-the-art methods but also have good ability to alleviate over-smoothing, and further verify the feasibility for designing GNNs with our unified optimization framework.