Search Results for author: Brian M. Sadler

Found 25 papers, 3 papers with code

Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent

no code implementations24 Sep 2022 Yicheng Chen, Rick S. Blum, Brian M. Sadler

The significant practical advantages of the HB method for learning problems are well known, but the question of reducing communications has not been addressed.

Federated Learning

Quickest Detection for Human-Sensor Systems using Quantum Decision Theory

no code implementations18 Aug 2022 Luke Snow, Vikram Krishnamurthy, Brian M. Sadler

In mathematical psychology, recent models for human decision-making use Quantum Decision Theory to capture important human-centric features such as order effects and violation of the sure-thing principle (total probability law).

Decision Making

FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus

1 code implementation22 Jun 2022 Amrit Singh Bedi, Chen Fan, Alec Koppel, Anit Kumar Sahu, Brian M. Sadler, Furong Huang, Dinesh Manocha

In federated learning (FL), the objective of collaboratively learning a global model through aggregation of model updates across devices tends to oppose the goal of personalization via local information.

Federated Learning

Deceptive Planning for Resource Allocation

no code implementations2 Jun 2022 Yagiz Savas, Mustafa O. Karabag, Brian M. Sadler, Ufuk Topcu

We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations.

Navigate

Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning

no code implementations2 Jun 2022 Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha

In this work, we propose a novel ${\bf K}$ernelized ${\bf S}$tein Discrepancy-based Posterior Sampling for ${\bf RL}$ algorithm (named $\texttt{KSRL}$) which extends model-based RL based upon posterior sampling (PSRL) in several ways: we (i) relax the need for any smoothness or Gaussian assumptions, allowing for complex mixture models; (ii) ensure it is applicable to large-scale training by incorporating a compression step such that the posterior consists of a \emph{Bayesian coreset} of only statistically significant past state-action pairs; and (iii) develop a novel regret analysis of PSRL based upon integral probability metrics, which, under a smoothness condition on the constructed posterior, can be evaluated in closed form as the kernelized Stein discrepancy (KSD).

Continuous Control Model-based Reinforcement Learning +1

One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones

1 code implementation CVPR 2022 Chan Hee Song, Jihyung Kil, Tai-Yu Pan, Brian M. Sadler, Wei-Lun Chao, Yu Su

We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task.

Vision and Language Navigation

Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting

no code implementations5 Feb 2022 Yicheng Chen, Rick S. Blum, Brian M. Sadler

Compared to the classical ADMM, a key feature of OADMM is that transmissions are ordered among workers at each iteration such that a worker with the most informative data broadcasts its local variable to neighbors first, and neighbors who have not transmitted yet can update their local variables based on that received transmission.

Distributed Optimization Federated Learning

Distributed Learning With Sparsified Gradient Differences

no code implementations5 Feb 2022 Yicheng Chen, Rick S. Blum, Martin Takac, Brian M. Sadler

A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.

Phase Retrieval for Radar Waveform Design

no code implementations27 Jan 2022 Samuel Pinilla, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello

The ability of a radar to discriminate in both range and Doppler velocity is completely characterized by the ambiguity function (AF) of its transmit waveform.

Radar waveform design

Joint Radar-Communications Processing from a Dual-Blind Deconvolution Perspective

no code implementations11 Nov 2021 Edwin Vargas, Kumar Vijay Mishra, Roman Jacome, Brian M. Sadler, Henry Arguello

When the radar receiver is not collocated with the transmitter, such as in passive or multistatic radars, the transmitted signal is also unknown apart from the target parameters.

Unique Bispectrum Inversion for Signals with Finite Spectral/Temporal Support

no code implementations11 Nov 2021 Samuel Pinilla, Kumar Vijay Mishra, Brian M. Sadler

In this paper, we present a an approach that uniquely recovers signals with finite spectral support (band-limited signals) from at least $3B$ measurements of its bispectrum function (BF), where $B$ is the signal's bandwidth.

Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems

no code implementations25 Sep 2021 Amanda Prorok, Matthew Malencia, Luca Carlone, Gaurav S. Sukhatme, Brian M. Sadler, Vijay Kumar

In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori.

Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

1 code implementation24 Jun 2021 Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler

Our framework is implemented by a cascade of a convolutional and a graph neural network (CNN / GNN), addressing agent-level visual perception and feature learning, as well as swarm-level communication, local information aggregation and agent action inference, respectively.

Imitation Learning

Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping

no code implementations5 Jun 2021 Zhan Gao, Subhrajit Bhattacharya, Leiming Zhang, Rick S. Blum, Alejandro Ribeiro, Brian M. Sadler

Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data.

Data Augmentation

Physical-Layer Security via Distributed Beamforming in the Presence of Adversaries with Unknown Locations

no code implementations28 Feb 2021 Yagiz Savas, Abolfazl Hashemi, Abraham P. Vinod, Brian M. Sadler, Ufuk Topcu

In such a setting, we develop a periodic transmission strategy, i. e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission.

Ordering for Communication-Efficient Quickest Change Detection in a Decomposable Graphical Model

no code implementations10 Aug 2020 Yicheng Chen, Rick S. Blum, Brian M. Sadler

The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic.

Change Detection

Collaborative Beamforming Under Localization Errors: A Discrete Optimization Approach

no code implementations27 Mar 2020 Erfaun Noorani, Yagiz Savas, Alec Koppel, John Baras, Ufuk Topcu, Brian M. Sadler

In particular, we formulate a discrete optimization problem to choose only a subset of agents to transmit the message signal so that the variance of the signal-to-noise ratio (SNR) received by the base station is minimized while the expected SNR exceeds a desired threshold.

Regret and Belief Complexity Trade-off in Gaussian Process Bandits via Information Thresholding

no code implementations23 Mar 2020 Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Brian M. Sadler, Alec Koppel

Doing so permits us to precisely characterize the trade-off between regret bounds of GP bandit algorithms and complexity of the posterior distributions depending on the compression parameter $\epsilon$ for both discrete and continuous action sets.

Decision Making Decision Making Under Uncertainty

VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms

no code implementations6 Feb 2020 Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler

More specifically, we consider that each robot has access to a visual perception of the immediate surroundings, and communication capabilities to transmit and receive messages from other neighboring robots.

A Projection Operator to Balance Consistency and Complexity in Importance Sampling

no code implementations pproximateinference AABI Symposium 2019 Alec Koppel*, Amrit Singh Bedi*, Brian M. Sadler, and Victor Elvira

IS is asymptotically consistent as the number of MC samples, and hence deltas (particles) that parameterize the density estimate, go to infinity.

Optimally Compressed Nonparametric Online Learning

no code implementations25 Sep 2019 Alec Koppel, Amrit Singh Bedi, Ketan Rajawat, Brian M. Sadler

Batch training of machine learning models based on neural networks is now well established, whereas to date streaming methods are largely based on linear models.

online learning

Decentralized Dictionary Learning Over Time-Varying Digraphs

no code implementations17 Aug 2018 Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler

This paper studies Dictionary Learning problems wherein the learning task is distributed over a multi-agent network, modeled as a time-varying directed graph.

Dictionary Learning

Proximity Without Consensus in Online Multi-Agent Optimization

no code implementations17 Jun 2016 Alec Koppel, Brian M. Sadler, Alejandro Ribeiro

To do so, we depart from the canonical decentralized optimization framework where agreement constraints are enforced, and instead formulate a problem where each agent minimizes a global objective while enforcing network proximity constraints.

Multiagent Systems Systems and Control Computation

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