@conference {17564,
	title = {Approximation algorithms for throughput maximization in wireless networks with delay constraints},
	booktitle = {2011 Proceedings IEEE INFOCOM},
	year = {2011},
	month = {2011/04/10/15},
	pages = {1116 - 1124},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We study the problem of throughput maximization in multi-hop wireless networks with end-to-end delay constraints for each session. This problem has received much attention starting with the work of Grossglauser and Tse (2002), and it has been shown that there is a significant tradeoff between the end-to-end delays and the total achievable rate. We develop algorithms to compute such tradeoffs with provable performance guarantees for arbitrary instances, with general interference models. Given a target delay-bound Δ(c) for each session c, our algorithm gives a stable flow vector with a total throughput within a factor of O (logΔm/log log Δm) of the maximum, so that the per-session (end-to-end) delay is O ((logΔm/log log Δm Δ(c))2), where Δm = maxc{Δ(c)}; note that these bounds depend only on the delays, and not on the network size, and this is the first such result, to our knowledge.},
	keywords = {Approximation algorithms, Approximation methods, approximation theory, Delay, delay constraints, delays, general interference model, Interference, multihop wireless networks, optimisation, Optimized production technology, radio networks, radiofrequency interference, target delay bound, Throughput, throughput maximization, Wireless networks},
	isbn = {978-1-4244-9919-9},
	doi = {10.1109/INFCOM.2011.5934887},
	author = {Guanhong Pei and Anil Kumar,V. S and Parthasarathy,S. and Srinivasan, Aravind}
}
@conference {12446,
	title = {Illumination robust dictionary-based face recognition},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {777 - 780},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we present a face recognition method based on simultaneous sparse approximations under varying illumination. Our method consists of two main stages. In the first stage, a dictionary is learned for each face class based on given training examples which minimizes the representation error with a sparseness constraint. In the second stage, a test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. Furthermore, to handle changes in lighting conditions, we use a relighting approach based on a non-stationary stochastic filter to generate multiple images of the same person with different lighting. As a result, our algorithm has the ability to recognize human faces with good accuracy even when only a single or a very few images are provided for training. The effectiveness of the proposed method is demonstrated on publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms.},
	keywords = {albedo, approximation theory, classification, competitive face recognition algorithms, Databases, Dictionaries, Face, face recognition, face recognition method, filtering theory, human face recognition, illumination robust dictionary-based face recognition, illumination variation, image representation, learned dictionary, learning (artificial intelligence), lighting, lighting conditions, multiple images, nonstationary stochastic filter, publicly available databases, relighting, relighting approach, representation error, residual vectors, Robustness, simultaneous sparse approximations, simultaneous sparse signal representation, sparseness constraint, Training, varying illumination, vectors},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6116670},
	author = {Patel, Vishal M. and Tao Wu and Biswas,S. and Phillips,P.J. and Chellapa, Rama}
}
@conference {17559,
	title = {Approximation Algorithms for Computing Capacity of Wireless Networks with SINR Constraints},
	booktitle = {IEEE INFOCOM 2008. The 27th Conference on Computer Communications},
	year = {2008},
	month = {2008/04/13/18},
	pages = {1166 - 1174},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A fundamental problem in wireless networks is to estimate its throughput capacity - given a set of wireless nodes, and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused on either random distributions of points, or has assumed simple graph-based models for wireless interference. In this paper, we study capacity estimation problem using the more general Signal to Interference Plus Noise Ratio (SINR) model for interference, on arbitrary wireless networks. The problem becomes much harder in this setting, because of the non-locality of the SINR model. Recent work by Moscibroda et al. (2006) has shown that the throughput in this model can differ from graph based models significantly. We develop polynomial time algorithms to provably approximate the total throughput in this setting.},
	keywords = {Algorithm design and analysis, approximation algorithm, Approximation algorithms, approximation theory, Computer networks, Computer science, graph theory, graph-based model, Interference constraints, polynomial time algorithm, Propagation losses, Protocols, radio networks, radiofrequency interference, signal to interference plus noise ratio, Signal to noise ratio, Throughput, wireless interference, wireless network, Wireless networks},
	isbn = {978-1-4244-2025-4},
	doi = {10.1109/INFOCOM.2008.172},
	author = {Chafekar,D. and Kumart,V. S.A and Marathe,M. V and Parthasarathy,S. and Srinivasan, Aravind}
}
@conference {16506,
	title = {Capturing Approximated Data Delivery Tradeoffs},
	booktitle = {Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on},
	year = {2008},
	month = {2008/04//},
	pages = {1471 - 1473},
	abstract = {This paper presents a middleware data delivery setting with a proxy that is required to maximize the completeness of captured updates, specified in its clients{\textquoteright} profiles, while minimizing at the same time the delay in delivering the updates to clients. The two objectives may conflict when the monitoring budget is limited. Therefore, any solution should consider this tradeoff in satisfying both objectives. We term this problem the "proxy dilemma" and formalize it as a biobjective optimization problem. Such problem occurs in many contemporary applications, such as mobile and sensor networks, and poses scalability challenges in delivering up-to-date data from remote resources to meet client specifications. We present a Pareto set as a formal solution to the proxy dilemma. We discuss the complexity of generating a Pareto set for the proxy dilemma and suggest an approximation scheme to this problem.},
	keywords = {approximation scheme, approximation theory, biobjective optimization problem, Middleware, middleware data delivery tradeoffs, mobile networks, Pareto optimisation, Pareto set, proxy dilemma problem, sensor networks},
	doi = {10.1109/ICDE.2008.4497593},
	author = {Roitman,H. and Gal,A. and Raschid, Louiqa}
}
@conference {17586,
	title = {Distributions on level-sets with applications to approximation algorithms},
	booktitle = {42nd IEEE Symposium on Foundations of Computer Science, 2001. Proceedings},
	year = {2001},
	month = {2001/10/08/11},
	pages = {588 - 597},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We consider a family of distributions on fixed-weight vectors in {0, 1}t; these distributions enjoy certain negative correlation properties and also satisfy pre-specified conditions on their marginal distributions. We show the existence of such families, and present a linear-time algorithm to sample from them. This yields improved approximation algorithms for the following problems: (a) low-congestion multi-path routing; (b) maximum coverage versions of set cover; (c) partial vertex cover problems for bounded-degree graphs; and (d) the Group Steiner Tree problem. For (a) and (b), the improvement is in the approximation ratio; for (c), we show how to speedup existing approximation algorithms while preserving the best-known approximation ratio; we also improve the approximation ratio for certain families of instances of unbounded degree. For (d), we derive an approximation algorithm whose approximation guarantee is at least as good as what is known; our algorithm is shown to have a better approximation guarantee for the worst known input families for existing algorithms.},
	keywords = {Approximation algorithms, approximation guarantee, approximation theory, bounded-degree graphs, computational geometry, Concrete, fixed-weight vectors, group Steiner tree problem, level-sets, linear-time algorithm, low-congestion multi-path routing, marginal distributions, maximum coverage versions, negative correlation properties, partial vertex cover problems, trees (mathematics)},
	isbn = {0-7695-1116-3},
	doi = {10.1109/SFCS.2001.959935},
	author = {Srinivasan, Aravind}
}
