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39
Agnostic active learning
 In ICML
, 2006
"... We state and analyze the first active learning algorithm which works in the presence of arbitrary forms of noise. The algorithm, A2 (for Agnostic Active), relies only upon the assumption that the samples are drawn i.i.d. from a fixed distribution. We show that A2 achieves an exponential improvement ..."
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Cited by 173 (15 self)
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We state and analyze the first active learning algorithm which works in the presence of arbitrary forms of noise. The algorithm, A2 (for Agnostic Active), relies only upon the assumption that the samples are drawn i.i.d. from a fixed distribution. We show that A2 achieves an exponential improvement (i.e., requires only O � ln 1 ɛ samples to find an ɛoptimal classifier) over the usual sample complexity of supervised learning, for several settings considered before in the realizable case. These include learning threshold classifiers and learning homogeneous linear separators with respect to an input distribution which is uniform over the unit sphere. 1.
Minimax bounds for active learning
 In COLT
, 2007
"... Abstract. This paper aims to shed light on achievable limits in active learning. Using minimax analysis techniques, we study the achievable rates of classification error convergence for broad classes of distributions characterized by decision boundary regularity and noise conditions. The results cle ..."
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Cited by 78 (7 self)
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Abstract. This paper aims to shed light on achievable limits in active learning. Using minimax analysis techniques, we study the achievable rates of classification error convergence for broad classes of distributions characterized by decision boundary regularity and noise conditions. The results clearly indicate the conditions under which one can expect significant gains through active learning. Furthermore we show that the learning rates derived are tight for “boundary fragment ” classes in ddimensional feature spaces when the feature marginal density is bounded from above and below. 1
Generalized binary search
 In Proceedings of the 46th Allerton Conference on Communications, Control, and Computing
, 2008
"... This paper addresses the problem of noisy Generalized Binary Search (GBS). GBS is a wellknown greedy algorithm for determining a binaryvalued hypothesis through a sequence of strategically selected queries. At each step, a query is selected that most evenly splits the hypotheses under consideratio ..."
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Cited by 56 (0 self)
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This paper addresses the problem of noisy Generalized Binary Search (GBS). GBS is a wellknown greedy algorithm for determining a binaryvalued hypothesis through a sequence of strategically selected queries. At each step, a query is selected that most evenly splits the hypotheses under consideration into two disjoint subsets, a natural generalization of the idea underlying classic binary search. GBS is used in many applications, including fault testing, machine diagnostics, disease diagnosis, job scheduling, image processing, computer vision, and active learning. In most of these cases, the responses to queries can be noisy. Past work has provided a partial characterization of GBS, but existing noisetolerant versions of GBS are suboptimal in terms of query complexity. This paper presents an optimal algorithm for noisy GBS and demonstrates its application to learning multidimensional threshold functions. 1
Faster rates in regression via active learning
 in Proceedings of NIPS
, 2005
"... In this paper we address the theoretical capabilities of active sampling for estimating functions in noise. Specifically, the problem we consider is that of estimating a function from noisy pointwise samples, that is, the measurements which are collected at various points over the domain of the fun ..."
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Cited by 44 (9 self)
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In this paper we address the theoretical capabilities of active sampling for estimating functions in noise. Specifically, the problem we consider is that of estimating a function from noisy pointwise samples, that is, the measurements which are collected at various points over the domain of the function. In the classical (passive) setting the sampling locations are chosen a priori, meaning that the choice of the sample locations precedes the gathering of the function observations. In the active sampling setting, on the other hand, the sample locations are chosen in an online fashion: the decision of where to sample next depends on all the observations made up to that point, in the spirit of the twenty questions game (as opposed to passive sampling where all the questions need to be asked before any answers are given). This extra degree of flexibility leads to improved signal reconstruction in comparison to the performance of classical (passive) methods. We present results characterizing the fundamental limits of active learning for various nonparametric function classes, as well as practical algorithms capable of exploiting the extra flexibility of the active setting and provably improving on classical techniques. In particular, significantly faster rates of convergence are achievable in cases involving functions whose complexity (in a the Kolmogorov sense) is highly concentrated in small regions of space (e.g., piecewise constant functions). Our active learning theory and methods show promise in a number of applications, including field estimation using wireless sensor networks and fault line detection. 1
Compressive sampling for signal classification
 in Proc. 40th Asilomar Conf. Signals, Systems and Computers
, 2006
"... Compressive Sampling (CS), also called Compressed Sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially en ..."
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Cited by 34 (2 self)
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Compressive Sampling (CS), also called Compressed Sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially encode the salient information in the signal. Further, the signal can be reconstructed from these “random projections,” even when the number of observations is far less than the ambient signal dimension. The provable success of CS for signal reconstruction motivates the study of its potential in other applications. This paper investigates the utility of CS projection observations for signal classification (more specifically, mary hypothesis testing). Theoretical error bounds are derived and verified with several simulations.
Active Learning for Adaptive Mobile Sensing Networks”, Proceedings of Information Processing in Sensor Networks (IPSN), 2006 Number of Sensors Contour Type RCE Latency Combined Regular (d
 200) Low (N = 10) Convex (T = 121) MCD MCD MCD Regular Low Nonconvex (T = 51) MCD MCD MCD Random Low Convex MCD MCD MCD Random Low Nonconvex MCD SA MCD Regular High (N = 80) Convex MCD MCD MCD Regular High Nonconvex MCD MCD MCD Random High Convex MCD M
"... This paper investigates dataadaptive path planning schemes for wireless networks of mobile sensor platforms. We focus on applications of environmental monitoring, in which the goal is to reconstruct a spatial map of environmental factors of interest. Traditional sampling theory deals with data coll ..."
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Cited by 22 (3 self)
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This paper investigates dataadaptive path planning schemes for wireless networks of mobile sensor platforms. We focus on applications of environmental monitoring, in which the goal is to reconstruct a spatial map of environmental factors of interest. Traditional sampling theory deals with data collection processes that are completely independent of the target map to be estimated, aside from possible a priori specifications reflective of assumed properties of the target. We refer to such processes as passive learning methods. Alternatively, one can envision sequential, adaptive data collection procedures that use information gleaned from previous observations to guide the process. We refer to such feedbackdriven processes as active learning methods. Active learning is naturally suited to mobile path planning, in which previous samples are used to guide the motion of the mobiles for further sampling. This paper presents some of the most encouraging theoretical results to date that support the effectiveness of active over passive learning, and focuses on new results regarding the capabilities of active learning methods for mobile sensing. Tradeoffs between latency, path lengths, and accuracy are carefully assessed using our theory. Adaptive path planning methods are developed to guide mobiles in order to focus attention in interesting regions of the sensing domain, thus conducting spatial surveys much more rapidly while maintaining the accuracy of the estimated map. The theory and methods are illustrated in the application of water current mapping in a freshwater lake.
MULTIARMED BANDIT PROBLEMS
"... Multiarmed bandit (MAB) problems are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative (competing) projects. Such problems are paradigms of a fundamental conflict between making decisions (allocating resources) that yield ..."
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Cited by 15 (0 self)
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Multiarmed bandit (MAB) problems are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative (competing) projects. Such problems are paradigms of a fundamental conflict between making decisions (allocating resources) that yield
Greedy gossip with eavesdropping
 IN PROC. IEEE INT. SYMP. ON WIRELESS PERVASIVE COMPUTING
, 2008
"... This paper presents greedy gossip with eavesdropping (GGE), a new average consensus algorithm for wireless sensor network applications. Consensus algorithms have recently received much attention in the sensor network community because of their simplicity and completely decentralized nature which ma ..."
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Cited by 15 (6 self)
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This paper presents greedy gossip with eavesdropping (GGE), a new average consensus algorithm for wireless sensor network applications. Consensus algorithms have recently received much attention in the sensor network community because of their simplicity and completely decentralized nature which makes them robust to changes in the network topology and unreliable wireless networking environments. In the sensor network, each node has a measurement value and the aim of average consensus is computing the average of these node values in the absence of a central authority. We prove that GGE converges to the average consensus with probability one. We also illustrate the performance of the algorithm via simulations and conclude that GGE provides a significant performance improvement compared to existing average consensus algorithms such as randomized gossip and geographic gossip.
Human Active Learning
"... We investigate a topic at the interface of machine learning and cognitive science. Human active learning, where learners can actively query the world for information, is contrasted with passive learning from random examples. Furthermore, we compare human active learning performance with predictions ..."
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Cited by 14 (2 self)
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We investigate a topic at the interface of machine learning and cognitive science. Human active learning, where learners can actively query the world for information, is contrasted with passive learning from random examples. Furthermore, we compare human active learning performance with predictions from statistical learning theory. We conduct a series of human category learning experiments inspired by a machine learning task for which active and passive learning error bounds are well understood, and dramatically distinct. Our results indicate that humans are capable of actively selecting informative queries, and in doing so learn better and faster than if they are given random training data, as predicted by learning theory. However, the improvement over passive learning is not as dramatic as that achieved by machine active learning algorithms. To the best of our knowledge, this is the first quantitative study comparing human category learning in active versus passive settings. 1
Upper and lower error bounds for active learning
 in The 44th Annual Allerton Conference on Communication, Control and Computing
"... Abstract — This paper analyzes the potential advantages and theoretical challenges of ”active learning ” algorithms. Active learning involves sequential, adaptive sampling procedures that use information gleaned from previous samples in order to focus the sampling and accelerate the learning process ..."
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Cited by 13 (0 self)
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Abstract — This paper analyzes the potential advantages and theoretical challenges of ”active learning ” algorithms. Active learning involves sequential, adaptive sampling procedures that use information gleaned from previous samples in order to focus the sampling and accelerate the learning process relative to “passive learning ” algorithms, which are based on nonadaptive (usually random) samples. There are a number of empirical and theoretical results suggesting that in certain situations active learning can be significantly more effective than passive learning. However, the fact that active learning algorithms are feedback systems makes their theoretical analysis very challenging. It is known that active learning can provably improve on passive learning if the error or noise rate of the sampling process is bounded. However, if the noise rate is unbounded, perhaps the situation most common in practice, then no previously existing theory demonstrates whether or not active learning offers an advantage. To study this issue, we investigate the basic problem of learning a threshold function from noisy observations. We present an algorithm that provably improves on passive learning, even when the noise is unbounded. Moreover, we derive a minimax lower bound for this learning problem, demonstrating that our proposed active learning algorithm converges at the nearoptimal rate. I.