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1. Jointly Optimizing Placement and Inference for Beacon-based Localization

   Schaff, C.; Yunis, D.; Chakrabarti, A; Walter, M.R. (October 2017, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot’s location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot’s location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy. 

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2. Jointly Optimizing Placement and Inference for Beacon-based Localization

   Schaff, Charles; Yunis, David; Chakrabarti, Ayan; Walter, Matthew R. (October 2017, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot’s location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot’s location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy. 

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3. A Flexible Generative Framework for Graph-based Semi-supervised Learning

   Ma, Jiaqi; Tang, Weijing; Zhu, Ji; Mei, Qiaozhu (January 2019, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019))

   We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-supervised learning tasks. However, conventional graph-based regularization methods and recent graph neural networks do not fully leverage the interrelations between the features, the graph, and the labels. In this work, we propose a flexible generative framework for graph-based semi-supervised learning, which approaches the joint distribution of the node features, labels, and the graph structure. Borrowing insights from random graph models in network science literature, this joint distribution can be instantiated using various distribution families. For the inference of missing labels, we exploit recent advances of scalable variational inference techniques to approximate the Bayesian posterior. We conduct thorough experiments on benchmark datasets for graph-based semi-supervised learning. Results show that the proposed methods outperform the state-of-the-art models in most settings. 

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4. OptRand: Optimistically Responsive Reconfigurable Distributed Randomness

   Bhat, Adithya; Shrestha, Nibesh; Kate, Aniket; Nayak, Kartik. (January 2023, Network and Distributed System Security Symposium (NDSS))

   Public random beacons publish random numbers at regular intervals, which anyone can obtain and verify. The design of public distributed random beacons has been an exciting research direction with significant implications for blockchains, voting, and beyond. Distributed random beacons, in addition to being bias-resistant and unpredictable, also need to have low communication overhead and latency, high resilience to faults, and ease of reconfigurability. Existing synchronous random beacon protocols sacrifice one or more of these properties. In this work, we design an efficient unpredictable synchronous random beacon protocol, OptRand, with quadratic (in the number $$n$$ of system nodes) communication complexity per beacon output. First, we innovate by employing a novel combination of bilinear pairing based publicly verifiable secret-sharing and non-interactive zero-knowledge proofs to build a linear (in $$n$$) sized publicly verifiable random sharing. Second, we develop a state machine replication protocol with linear-sized inputs that is also optimistically responsive, i.e., it can progress responsively at actual network speed during optimistic conditions, despite the synchrony assumption, and thus incur low latency. In addition, we present an efficient reconfiguration mechanism for OptRand that allows nodes to leave and join the system. Our experiments show our protocols perform significantly better compared to state-of-the-art protocols under optimistic conditions and on par with state-of-the-art protocols in the normal case. We are also the first to implement a reconfiguration mechanism for distributed beacons and demonstrate that our protocol continues to be live during reconfigurations. 

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5. Graph Representation Learning via Multi-task Knowledge Distillation

   Ma, Jiaqi; Mei, Qiaozhu (January 2019, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) Graph Representation Learning Workshop)

   Machine learning on graph structured data has attracted much research interest due to its ubiquity in real world data. However, how to efficiently represent graph data in a general way is still an open problem. Traditional methods use handcraft graph features in a tabular form but suffer from the defects of domain expertise requirement and information loss. Graph representation learning overcomes these defects by automatically learning the continuous representations from graph structures, but they require abundant training labels, which are often hard to fulfill for graph-level prediction problems. In this work, we demonstrate that, if available, the domain expertise used for designing handcraft graph features can improve the graph-level representation learning when training labels are scarce. Specifically, we proposed a multi-task knowledge distillation method. By incorporating network-theory-based graph metrics as auxiliary tasks, we show on both synthetic and real datasets that the proposed multi-task learning method can improve the prediction performance of the original learning task, especially when the training data size is small. 

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