Social sensing has emerged as a new sensing paradigm where humans (or devices on their behalf) collectively report measurements about the physical world. This paper focuses on a quality-cost-aware task allocation problem in multi-attribute social sensing applications. The goal is to identify a task allocation strategy …
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48 results for “online sensing”
In point-based sensing systems such as coordinate measuring machines (CMM) and laser ultrasonics where complete sensing is impractical due to the high sensing time and cost, adaptive sensing through a systematic exploration is vital for online inspection and anomaly quantification. Most of the existing sequential sampl…
CovidSens uses social media to track COVID-19 spread.
problem Accurate and timely dissemination of COVID-19 information.
method Social sensing to analyze online user data.
result Real-time COVID-19 spread tracking system.
Optimizes sensor usage for detecting abrupt changes in sensor data.
problem Efficiently detect abrupt changes in sensor data with limited resources.
method Proposes a computationally efficient online sensing scheme that balances exploration and exploitation.
result Expected delay bounds match information-theoretic lower bounds, establishing optimality of the method.
In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the …
We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a formal criterion on the eff…
Online algorithm identifies PDEs from noisy data snapshots.
problem Identifying PDEs from sequential solution snapshots.
method Combines weak-form discretization with online proximal gradient descent.
result Efficiently identifies and tracks systems with time-varying coefficients.
Algorithm learns expert weights to minimize regret in adversarial setting.
problem Learning to aggregate expert forecasts with no-regret guarantee in adversarial conditions.
method Online mirror descent algorithm for logarithmic pooling of expert forecasts.
result Achieves expected regret compared to best weights.
Proposes standards for evaluating online machine learning methods in evolving data streams.
problem Difficulty in evaluating online machine learning methods under realistic conditions.
method Proposes comprehensive evaluation standards, performance measures, and evaluation strategies.
result Provides a new Python framework (float) for modular integration of libraries and custom code.
In this paper, we study adaptive online convex optimization, and aim to design a universal algorithm that achieves optimal regret bounds for multiple common types of loss functions. Existing universal methods are limited in the sense that they are optimal for only a subclass of loss functions. To address this limitatio…
CDSSD detects sparse changes in partially observable data streams.
problem Online change detection of sparse changes in partially observable high-dimensional data streams.
method Smooth-sparse decomposition, spike-slab variational Bayesian inference, adaptive sampling via Thompson sampling.
result CDSSD effectively detects sparse changes in partially observable data streams.
Online change detection algorithm using random Fourier features.
problem Online non-parametric change point detection in multivariate data streams.
method Sequential testing procedure based on random Fourier features.
result The algorithm has optimal detection delay in the minimax sense.
High-velocity streams of high-dimensional data pose significant "big data" analysis challenges across a range of applications and settings. Online learning and online convex programming play a significant role in the rapid recovery of important or anomalous information from these large datastreams. While recent advance…
Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic…
The paper addresses statistical inference for online decision-making in a contextual bandit setting.
problem Understanding the performance of reward models in online decision-making with contextual information.
method The paper uses the contextual bandit framework with a linear reward model and the -greedy policy to address the exploration-exploitation dilemma. It employs the martingale central limit theorem and inverse propensity score weighting to establish asymptotic normality of parameter estimators.
result The online ordinary least squares estimator and the online weighted least squares estimator are asymptotically normal, providing insights into the performance of the reward model.
Recent breakthrough results in compressed sensing (CS) have established that many high dimensional objects can be accurately recovered from a relatively small number of non- adaptive linear projection observations, provided that the objects possess a sparse representation in some basis. Subsequent efforts have shown th…
New test detects local changes in high-dimensional Gaussian graphical models online.
problem Detecting abrupt changes in high-dimensional Gaussian graphical models in real-time.
method Develops a novel test based on the ℓ∞ norm of the normalized covariance matrix.
result Demonstrates good performance in various experimental settings.
Data driven soft sensor design has recently gained immense popularity, due to advances in sensory devices, and a growing interest in data mining. While partial least squares (PLS) is traditionally used in the process literature for designing soft sensors, the statistical literature has focused on sparse learners, such …
Regret minimization is treated as the golden rule in the traditional study of online learning. However, regret minimization algorithms tend to converge to the static optimum, thus being suboptimal for changing environments. To address this limitation, new performance measures, including dynamic regret and adaptive regr…
A model-free approach to fingertip slip and disturbance detection for grasp stability inferencecs.RO
Robotic grasp stability improved with fingertip slippage detection.
problem Improving grasp stability in robotic manipulation.
method Task-relevant feature extraction and efficient classifier design for fingertip slippage detection.
result The proposed method effectively detects object slippage with fingertips in an online fashion.
New method detects changes in high-dimensional Gaussian data streams.
problem Detecting changes in high-dimensional data streams.
method Likelihood ratio tests across scales and coordinates.
result Patience (null rejection rate) at desired level, response delay under alternative.
Optimal algorithms for mixable losses in dynamic environments with reduced redundancy.
problem Online optimization of mixable loss functions in a dynamic environment.
method Introduce online mixture schemes with polynomial and logarithmic time complexities.
result Achieves optimal redundancy up to a constant multiplicity gap.
A simplified Bayesian approach for online sports rating.
problem Rating players/teams based on game outcomes.
method Bayesian approach using Kalman filter principles.
result Known algorithms like Elo and TrueSkill are special cases.
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports. To mine these data properly, attributable to their innate ambiguity, a Word Sense Disambiguation (WSD) algorithm can avoid numbers…
We consider online learning problems where the aim is to achieve regret which is efficient in the sense that it is the same order as the lowest regret amongst K experts. This is a substantially stronger requirement that achieving or regret with respect to the best expert and standard algorithm…
Near-Linear Time Algorithm with Near-Logarithmic Regret Per Switch for Mixable/Exp-Concave Lossescs.LG
Near-logarithmic regret per switch achieved for mixable/exp-concave losses.
problem Online optimization of mixable loss functions with dynamic environments.
method Online mixture framework using static solvers and hyper-expert creations.
result Near-logarithmic regret per switch with sub-polynomial complexity.
This paper bridges offline and online RL by studying policy finetuning with a reference policy.
problem Sample-efficient reinforcement learning in online and offline settings.
method Design of policy finetuning algorithms and analysis of sample complexity.
result Theoretical analysis shows that the optimal policy finetuning algorithm is either offline reduction or purely online RL.
We show that the Subgradient algorithm is universal for online learning on the simplex in the sense that it simultaneously achieves regret for adversarial costs and pseudo-regret for i.i.d costs. To the best of our knowledge this is the first demonstration of a universal algorithm on the simplex tha…
Improves multi-objective learning by adapting to local subintervals.
problem Learning a predictor satisfying multiple objectives in an online, changing data setting.
method Adapting an existing multi-objective learning method with an adaptive online algorithm.
result Improves predictions over subgroups and remains robust under distribution shift.
A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous ma…
We introduce a novel class of rotation invariants of two dimensional curves based on iterated integrals. The invariants we present are in some sense complete and we describe an algorithm to calculate them, giving explicit computations up to order six. We present an application to online (stroke-trajectory based) charac…
In this paper, we consider the problem of predicting observations generated online by an unknown, partially observed linear system, which is driven by stochastic noise. For such systems the optimal predictor in the mean square sense is the celebrated Kalman filter, which can be explicitly computed when the system model…
Develops an online method for solving constrained optimization problems with debiasing techniques.
problem Online inference of solutions to constrained optimization problems with equality and inequality constraints.
method Stochastic Sequential Quadratic Programming (SSQP) with momentum debiasing.
result Achieves global almost-sure convergence and local asymptotic normality with optimal primal-dual limiting covariance.
Interactive applications incorporating high-data rate sensing and computer vision are becoming possible due to novel runtime systems and the use of parallel computation resources. To allow interactive use, such applications require careful tuning of multiple application parameters to meet required fidelity and latency …
AUCRSS detects change points in partially observed multivariate autocorrelated data.
problem Detecting change points in multivariate autocorrelated data with limited sensing resources.
method Adaptive Upper Confidence Region (AUCRSS) with state space model (SSM), adaptive sampling policy, and generalized likelihood ratio test.
result The method outperforms existing approaches in detecting change points efficiently.
This paper addresses the problem of prediction with expert advice for outcomes in a geodesic space with non-positive curvature in the sense of Alexandrov. Via geometric considerations, and in particular the notion of barycenters, we extend to this setting the definition and analysis of the classical exponentially weigh…
New algorithms for multitask learning with long-term memory.
problem Learning from tasks partitioned into unknown segments with associated hypotheses.
method Online multitask learning algorithms exploiting segmentation and hypothesis association.
result Regret bounds and efficient algorithms for various hypothesis classes.
Paper develops an online covariance estimator for nonsmooth stochastic approximation problems.
problem Estimating covariance in nonsmooth, potentially non-monotone settings.
method Online batch-means covariance matrix estimator.
result Estimator achieves convergence rate of .
Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviewscs.LG
This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment ana…
New algorithm expands FTRL framework with improved worst-case regret bounds.
problem Online learning with improved worst-case regret bounds.
method Generalized implicit Follow-The-Regularized-Leader (FTRL) algorithm.
result Unified framework for designing updates improving worst-case regret bounds.
Efficiently estimates neural connectivity using ensemble stimulation.
problem Estimating functional connectivity in large, behaving neural populations.
method Noisy group testing combined with convex optimization and Bayesian inference.
result Connectivity can be inferred with logarithmic growth in tests, even for large networks.
Method fuses low and high-resolution data for better health estimates.
problem Improving high-resolution health estimates from mixed data sources.
method Fusion of unbiased low-resolution and potentially biased high-resolution data, learning a distribution consistent with sampling bias.
result Significant reduction in bias in high-resolution estimates.
Improved bounds for multicalibration and omniprediction in online and distributional settings.
problem Achieving efficient multicalibration and omniprediction in fairness and loss minimization.
method Proposed an efficient algorithm achieving improved rates for multicalibration and omniprediction.
result Achieved -swap multicalibration error for convex Lipschitz functions.
SMAPGAN: Generative Adversarial Network Based Semi-Supervised Styled Map Tiles Generating Methodeess.IV
Traditional online map tiles, widely used on the Internet such as Google Map and Baidu Map, are rendered from vector data. Timely updating online map tiles from vector data, of which the generating is time-consuming, is a difficult mission. It is a shortcut to generate map tiles in time from remote sensing images, whic…
In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative meth…
New method minimizes regret in AMDP with high probability.
problem Pessimistic regret analysis in AMDP.
method Developed a new policy search method achieving optimistic regret.
result Achieved sublinear optimistic regret with high probability.
We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset. Our online sketching algorithm compresses an N element dataset to a sketch of size in time, where . This sketch can correctly report the nearest neighbors of any …
We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. The challenge is to use past data to actively price future data and give learning guarantees even when an agent's cost for revealing her data may depend arbitrarily on the data itself. We achieve this goal by showin…