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This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, designing online control algorithms for unknown LTV systems remains a challenging task. At a cost of NP-hard offline planning, prior works have introduced online convex optimization algorithms, although they suffer from nonparametric rate of regret. In this paper, we propose the first computationally tractable online algorithm with regret guarantees that avoids offline planning over the state linear feedback policies. Our algorithm is based on the optimism in the face of uncertainty (OFU) principle in which we optimistically select the best model in a high confidence region. Our algorithm is then more explorative when compared to previous approaches. To overcome non-stationarity, we propose either a restarting strategy (R-OFU) or a sliding window (SW-OFU) strategy. With proper configuration, our algorithm is attains sublinear regret $O(T^{2/3})$. These algorithms utilize data from the current phase for tracking variations on the system dynamics. We corroborate our theoretical findings with numerical experiments, which highlight the effectiveness of our methods. To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.

Motion planning is a ubiquitous problem that is often a bottleneck in robotic applications. We demonstrate that motion planning problems such as minimum constraint removal, belief-space planning, and visibility-aware motion planning (VAMP) benefit from a path-dependent formulation, in which the state at a search node is represented implicitly by the path to that node. A naive approach to computing the feasibility of a successor node in such a path-dependent formulation takes time linear in the path length to the node, in contrast to a (possibly very large) constant time for a more typical search formulation. For long-horizon plans, performing this linear-time computation, which we call the lookback, for each node becomes prohibitive. To improve upon this, we introduce the use of a fully persistent spatial data structure (FPSDS), which bounds the size of the lookback. We then focus on the application of the FPSDS in VAMP, which involves incremental geometric computations that can be accelerated by filtering configurations with bounding volumes using nearest-neighbor data structures. We demonstrate an asymptotic and practical improvement in the runtime of finding VAMP solutions in several illustrative domains. To the best of our knowledge, this is the first use of a fully persistent data structure for accelerating motion planning.

In this paper, we propose a novel uniform generalization bound on the time and inverse temperature for stochastic gradient Langevin dynamics (SGLD) in a non-convex setting. While previous works derive their generalization bounds by uniform stability, we use Rademacher complexity to make our generalization bound independent of the time and inverse temperature. Using Rademacher complexity, we can reduce the problem to derive a generalization bound on the whole space to that on a bounded region and therefore can remove the effect of the time and inverse temperature from our generalization bound. As an application of our generalization bound, an evaluation on the effectiveness of the simulated annealing in a non-convex setting is also described. For the sample size $n$ and time $s$, we derive evaluations with orders $\sqrt{n^{-1} \log (n+1)}$ and $|(\log)^4(s)|^{-1}$, respectively. Here, $(\log)^4$ denotes the $4$ times composition of the logarithmic function.

The ever-growing data privacy concerns have transformed machine learning (ML) architectures from centralized to distributed, leading to federated learning (FL) and split learning (SL) as the two most popular privacy-preserving ML paradigms. However, implementing either conventional FL or SL alone with diverse network conditions (e.g., device-to-device (D2D) and cellular communications) and heterogeneous clients (e.g., heterogeneous computation/communication/energy capabilities) may face significant challenges, particularly poor architecture scalability and long training time. To this end, this article proposes two novel hybrid distributed ML architectures, namely, hybrid split FL (HSFL) and hybrid federated SL (HFSL), by combining the advantages of both FL and SL in D2D-enabled heterogeneous wireless networks. Specifically, the performance comparison and advantages of HSFL and HFSL are analyzed generally. Promising open research directions are presented to offer commendable reference for future research. Finally, primary simulations are conducted upon considering three datasets under non-independent and identically distributed settings, to verify the feasibility of our proposed architectures, which can significantly reduce communication/computation cost and training time, as compared with conventional FL and SL.

We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning, and allows us to explore many combinations of different linkage criteria and cannot-link constraints. We prove the equivalence of existing clustering methods to some of those combinations and introduce new algorithms for combinations that have not been studied before. We study both theoretical and empirical properties of these combinations and prove that some of these define an ultrametric on the graph. We conduct a systematic comparison of various instantiations of GASP on a large variety of both synthetic and existing signed clustering problems, in terms of accuracy but also efficiency and robustness to noise. Lastly, we show that some of the algorithms included in our framework, when combined with the predictions from a CNN model, result in a simple bottom-up instance segmentation pipeline. Going all the way from pixels to final segments with a simple procedure, we achieve state-of-the-art accuracy on the CREMI 2016 EM segmentation benchmark without requiring domain-specific superpixels.

Empowering agents with a compositional understanding of their environment is a promising next step toward solving long-horizon planning problems. On the one hand, we have seen encouraging progress on variational inference algorithms for obtaining sets of object-centric latent representations ("slots") from unstructured scene observations. On the other hand, generating scenes from slots has received less attention, in part because it is complicated by the lack of a canonical object order. A canonical object order is useful for learning the object correlations necessary to generate physically plausible scenes similar to how raster scan order facilitates learning pixel correlations for pixel-level autoregressive image generation. In this work, we address this lack by learning a fixed object order for a hierarchical variational autoencoder with a single level of autoregressive slots and a global scene prior. We cast autoregressive slot inference as a set-to-sequence modeling problem. We introduce an auxiliary loss to train the slot prior to generate objects in a fixed order. During inference, we align a set of inferred slots to the object order obtained from a slot prior rollout. To ensure the rolled out objects are meaningful for the given scene, we condition the prior on an inferred global summary of the input. Experiments on compositional environments and ablations demonstrate that our model with global prior, inference with aligned slot order, and auxiliary loss achieves state-of-the-art sample quality.

Aggregating signals from a collection of noisy sources is a fundamental problem in many domains including crowd-sourcing, multi-agent planning, sensor networks, signal processing, voting, ensemble learning, and federated learning. The core question is how to aggregate signals from multiple sources (e.g. experts) in order to reveal an underlying ground truth. While a full answer depends on the type of signal, correlation of signals, and desired output, a problem common to all of these applications is that of differentiating sources based on their quality and weighting them accordingly. It is often assumed that this differentiation and aggregation is done by a single, accurate central mechanism or agent (e.g. judge). We complicate this model in two ways. First, we investigate the setting with both a single judge, and one with multiple judges. Second, given this multi-agent interaction of judges, we investigate various constraints on the judges' reporting space. We build on known results for the optimal weighting of experts and prove that an ensemble of sub-optimal mechanisms can perform optimally under certain conditions. We then show empirically that the ensemble approximates the performance of the optimal mechanism under a broader range of conditions.

In deep reinforcement learning (RL), data augmentation is widely considered as a tool to induce a set of useful priors about semantic consistency and improve sample efficiency and generalization performance. However, even when the prior is useful for generalization, distilling it to RL agent often interferes with RL training and degenerates sample efficiency. Meanwhile, the agent is forgetful of the prior due to the non-stationary nature of RL. These observations suggest two extreme schedules of distillation: (i) over the entire training; or (ii) only at the end. Hence, we devise a stand-alone network distillation method to inject the consistency prior at any time (even after RL), and a simple yet efficient framework to automatically schedule the distillation. Specifically, the proposed framework first focuses on mastering train environments regardless of generalization by adaptively deciding which {\it or no} augmentation to be used for the training. After this, we add the distillation to extract the remaining benefits for generalization from all the augmentations, which requires no additional new samples. In our experiments, we demonstrate the utility of the proposed framework, in particular, that considers postponing the augmentation to the end of RL training.

There is no shortage of outlier detection (OD) algorithms in the literature, yet a vast body of them are designed for a single machine. With the increasing reality of already cloud-resident datasets comes the need for distributed OD techniques. This area, however, is not only understudied but also short of public-domain implementations for practical use. This paper aims to fill this gap: We design Sparx, a data-parallel OD algorithm suitable for shared-nothing infrastructures, which we specifically implement in Apache Spark. Through extensive experiments on three real-world datasets, with several billions of points and millions of features, we show that existing open-source solutions fail to scale up; either by large number of points or high dimensionality, whereas Sparx yields scalable and effective performance. To facilitate practical use of OD on modern-scale datasets, we open-source Sparx under the Apache license at //tinyurl.com/sparx2022.

The demand for artificial intelligence has grown significantly over the last decade and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, in order to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges, first and foremost the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.