Year |
Citation |
Score |
2015 |
Xiao J, Xu P, Zhang Y, Ehinger K, Finkelstein A, Kulkarni S. What can we learn from eye tracking data on 20,000 images? Journal of Vision. 15: 790. PMID 26326478 DOI: 10.1167/15.12.790 |
0.336 |
|
2015 |
Ozay M, Esnaola I, Yarman Vural FT, Kulkarni SR, Poor HV. Machine Learning Methods for Attack Detection in the Smart Grid. Ieee Transactions On Neural Networks and Learning Systems. PMID 25807571 DOI: 10.1109/Tnnls.2015.2404803 |
0.772 |
|
2015 |
Shang S, Cuff P, Hui P, Kulkarni S. An Upper Bound on the Convergence Time for Quantized Consensus of Arbitrary Static Graphs Ieee Transactions On Automatic Control. 60: 1127-1132. DOI: 10.1109/Tac.2014.2342071 |
0.389 |
|
2013 |
Yu J, Kulkarni SR, Poor HV. Dimension expansion and customized spring potentials for sensor localization Eurasip Journal On Advances in Signal Processing. 2013. DOI: 10.1186/1687-6180-2013-20 |
0.697 |
|
2013 |
Lunden J, Kulkarni SR, Koivunen V, Poor HV. Multiagent reinforcement learning based spectrum sensing policies for cognitive radio networks Ieee Journal On Selected Topics in Signal Processing. 7: 858-868. DOI: 10.1109/Jstsp.2013.2259797 |
0.497 |
|
2013 |
Ozay M, Esnaola I, Vural FTY, Kulkarni SR, Poor HV. Sparse attack construction and state estimation in the smart grid: Centralized and distributed models Ieee Journal On Selected Areas in Communications. 31: 1306-1318. DOI: 10.1109/Jsac.2013.130713 |
0.77 |
|
2013 |
Ozay M, Vural FTY, Kulkarni SR, Poor HV. Fusion of image segmentation algorithms using consensus clustering 2013 Ieee International Conference On Image Processing, Icip 2013 - Proceedings. 4049-4053. DOI: 10.1109/ICIP.2013.6738834 |
0.72 |
|
2013 |
Zheng H, Kulkarni SR, Poor HV. A sequential predictor retraining algorithm and its application to market prediction Annals of Operations Research. 208: 209-225. DOI: 10.1007/S10479-013-1396-2 |
0.682 |
|
2012 |
Shutin D, Zechner C, Kulkarni SR, Poor HV. Regularized variational Bayesian learning of echo state networks with delay&sum readout. Neural Computation. 24: 967-95. PMID 22168555 DOI: 10.1162/Neco_A_00253 |
0.553 |
|
2012 |
Miller MK, Wang G, Kulkarni SR, Poor HV, Osherson DN. Citizen Forecasts of the 2008 U.S. Presidential Election Politics and Policy. 40: 1019-1052. DOI: 10.1111/J.1747-1346.2012.00394.X |
0.541 |
|
2012 |
Shutin D, Kulkarni SR, Poor HV. Incremental reformulated automatic relevance determination Ieee Transactions On Signal Processing. 60: 4977-4981. DOI: 10.1109/Tsp.2012.2200478 |
0.521 |
|
2012 |
Jain A, Gündüz D, Kulkarni SR, Poor HV, Verdú S. Energy-distortion tradeoffs in gaussian joint source-channel coding problems Ieee Transactions On Information Theory. 58: 3153-3168. DOI: 10.1109/Tit.2012.2184912 |
0.578 |
|
2012 |
Ozay M, Esnaola I, Yarman Vural FT, Kulkarni SR, Vincent Poor H. Smarter security in the smart grid 2012 Ieee 3rd International Conference On Smart Grid Communications, Smartgridcomm 2012. 312-317. DOI: 10.1109/SmartGridComm.2012.6486002 |
0.663 |
|
2012 |
Ozay M, Esnaola I, Yarman Vural FT, Kulkarni SR, Vincent Poor H. Distributed models for sparse attack construction and state vector estimation in the smart grid 2012 Ieee 3rd International Conference On Smart Grid Communications, Smartgridcomm 2012. 306-311. DOI: 10.1109/SmartGridComm.2012.6486001 |
0.706 |
|
2012 |
Yu J, Kulkarni SR, Poor HV. Robust ellipse and spheroid fitting Pattern Recognition Letters. 33: 492-499. DOI: 10.1016/J.Patrec.2011.11.025 |
0.66 |
|
2011 |
Wang G, Kulkarni SR, Poor HV, Osherson DN. Aggregating large sets of probabilistic forecasts by weighted coherent adjustment Decision Analysis. 8: 128-144. DOI: 10.1287/Deca.1110.0206 |
0.634 |
|
2011 |
Shutin D, Buchgraber T, Kulkarni SR, Poor HV. Fast variational sparse bayesian learning with automatic relevance determination for superimposed signals Ieee Transactions On Signal Processing. 59: 6257-6261. DOI: 10.1109/Tsp.2011.2168217 |
0.495 |
|
2011 |
Zheng H, Kulkarni SR, Poor HV. Attribute-distributed learning: Models, limits, and algorithms Ieee Transactions On Signal Processing. 59: 386-398. DOI: 10.1109/Tsp.2010.2088393 |
0.714 |
|
2011 |
Jain A, Kulkarni SR, Verdú S. Energy efficiency of decode-and-forward for wideband wireless multicasting Ieee Transactions On Information Theory. 57: 7695-7713. DOI: 10.1109/Tit.2011.2170120 |
0.55 |
|
2011 |
Wagner AB, Viswanath P, Kulkarni SR. Probability estimation in the rare-events regime Ieee Transactions On Information Theory. 57: 3207-3229. DOI: 10.1109/Tit.2011.2137210 |
0.317 |
|
2011 |
Jain A, Kulkarni SR, Verdu S. Multicasting in large wireless networks: Bounds on the minimum energy per bit Ieee Transactions On Information Theory. 57: 14-32. DOI: 10.1109/Tit.2010.2090228 |
0.552 |
|
2011 |
Shutin D, Buchgraber T, Kulkarni SR, Poor HV. Fast adaptive variational sparse Bayesian learning with automatic relevance determination Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 2180-2183. DOI: 10.1109/ICASSP.2011.5946760 |
0.478 |
|
2011 |
Wu Y, Zheng H, Calderbank R, Kulkarni S, Poor HV. On optimal precoding in wireless multicast systems Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 3068-3071. DOI: 10.1109/ICASSP.2011.5946306 |
0.398 |
|
2011 |
Lundén J, Koivunen V, Kulkarni SR, Poor HV. Reinforcement learning based distributed multiagent sensing policy for cognitive radio networks 2011 Ieee International Symposium On Dynamic Spectrum Access Networks, Dyspan 2011. 642-646. DOI: 10.1109/DYSPAN.2011.5936261 |
0.397 |
|
2011 |
Wang G, Kulkarni S, Poor HV, Osherson DN. Improving aggregated forecasts of probability 2011 45th Annual Conference On Information Sciences and Systems, Ciss 2011. DOI: 10.1109/CISS.2011.5766208 |
0.315 |
|
2011 |
Zheng H, Kulkarni SR, Poor HV. Consensus clustering: The filtered stochastic best-one-element-move algorithm 2011 45th Annual Conference On Information Sciences and Systems, Ciss 2011. DOI: 10.1109/CISS.2011.5766165 |
0.445 |
|
2011 |
Lunden J, Koivunen V, Kulkarni SR, Poor HV. Exploiting spatial diversity in multiagent reinforcement learning based spectrum sensing 2011 4th Ieee International Workshop On Computational Advances in Multi-Sensor Adaptive Processing, Camsap 2011. 325-328. DOI: 10.1109/CAMSAP.2011.6136016 |
0.364 |
|
2011 |
Shutin D, Kulkarni SR, Poor HV. Stationary point variational Bayesian attribute-distributed sparse learning with l 1 sparsity constraints 2011 4th Ieee International Workshop On Computational Advances in Multi-Sensor Adaptive Processing, Camsap 2011. 277-280. DOI: 10.1109/CAMSAP.2011.6136003 |
0.394 |
|
2011 |
Kulkarni SR, Harman G. Statistical learning theory: A tutorial Wiley Interdisciplinary Reviews: Computational Statistics. 3: 543-556. DOI: 10.1002/Wics.179 |
0.351 |
|
2010 |
Yu J, Zheng H, Kulkarni SR, Poor HV. Two-stage outlier elimination for robust curve and surface fitting Eurasip Journal On Advances in Signal Processing. 2010. DOI: 10.1155/2010/154891 |
0.749 |
|
2010 |
Brunton SL, Rowley CW, Kulkarni SR, Clarkson C. Maximum power point tracking for photovoltaic optimization using ripple-based extremum seeking control Ieee Transactions On Power Electronics. 25: 2531-2540. DOI: 10.1109/Tpel.2010.2049747 |
0.301 |
|
2010 |
Pérez-Cruz F, Kulkarni SR. Robust and low complexity distributed kernel least squares learning in sensor networks Ieee Signal Processing Letters. 17: 355-358. DOI: 10.1109/Lsp.2010.2040926 |
0.44 |
|
2010 |
Jain A, Gündüz D, Kulkarni SR, Poor HV, Verdú S. Energy-distortion tradeoff with multiple sources and feedback 2010 Information Theory and Applications Workshop, Ita 2010 - Conference Proceedings. 142-146. DOI: 10.1109/ITA.2010.5454131 |
0.341 |
|
2010 |
Jain A, Gündüz D, Kulkarni SR, Poor HV, Verdú S. Energy efficient lossy transmission over sensor networks with feedback Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 5558-5561. DOI: 10.1109/ICASSP.2010.5495231 |
0.392 |
|
2010 |
Shutin D, Zheng H, Fleury BH, Kulkarni SR, Poor HV. Space-alternating attribute-distributed sparse learning 2010 2nd International Workshop On Cognitive Information Processing, Cip2010. 209-214. DOI: 10.1109/CIP.2010.5604254 |
0.502 |
|
2010 |
Zheng H, Kulkarni SR, Poor HV. Attribute-distributed learning: The iterative covariance optimization algorithm and its applications Proceedings of the 2010 American Control Conference, Acc 2010. 6783-6788. |
0.381 |
|
2009 |
Predd JB, Seiringer R, Lieb EH, Osherson DN, Poor HV, Kulkarni SR. Probabilistic coherence and proper scoring rules Ieee Transactions On Information Theory. 55: 4786-4792. DOI: 10.1109/Tit.2009.2027573 |
0.739 |
|
2009 |
Wang Q, Kulkarni SR, Verdú S. Divergence estimation for multidimensional densities via κ-nearest-neighbor distances Ieee Transactions On Information Theory. 55: 2392-2405. DOI: 10.1109/Tit.2009.2016060 |
0.383 |
|
2009 |
Wang CC, Kulkarni SR, Poor HV. Finding all small error-prone substructures in LDPC codes Ieee Transactions On Information Theory. 55: 1976-1999. DOI: 10.1109/Tit.2009.2015993 |
0.475 |
|
2009 |
Predd JB, Kulkarni SR, Poor HV. A collaborative training algorithm for distributed learning Ieee Transactions On Information Theory. 55: 1856-1871. DOI: 10.1109/Tit.2009.2012992 |
0.81 |
|
2009 |
Zheng H, Kulkarni SR, Poor HV. Collaborative training in sensor networks: A graphical model approach Machine Learning For Signal Processing Xix - Proceedings of the 2009 Ieee Signal Processing Society Workshop, Mlsp 2009. DOI: 10.1109/MLSP.2009.5306188 |
0.485 |
|
2009 |
Wang G, Kulkarni S, Poor HV. Aggregating disparate judgments using a coherence penalty Proceedings - 43rd Annual Conference On Information Sciences and Systems, Ciss 2009. 23-27. DOI: 10.1109/CISS.2009.5054683 |
0.43 |
|
2009 |
Yu J, Zheng H, Kulkarni SR, Poor HV. Outlier elimination for robust ellipse and ellipsoid fitting Camsap 2009 - 2009 3rd Ieee International Workshop On Computational Advances in Multi-Sensor Adaptive Processing. 33-36. DOI: 10.1109/CAMSAP.2009.5413262 |
0.475 |
|
2009 |
Yu J, Kulkarni SR, Poor HV. Robust fitting of ellipses and spheroids Conference Record - Asilomar Conference On Signals, Systems and Computers. 94-98. DOI: 10.1109/ACSSC.2009.5470160 |
0.464 |
|
2009 |
Zheng H, Kulkarni SR, Poor HV. Cooperative training for attribute-distributed data: Trade-off between data transmission and performance 2009 12th International Conference On Information Fusion, Fusion 2009. 664-671. |
0.316 |
|
2008 |
Predd JB, Osherson DN, Kulkarni SR, Poor HV. Aggregating Probabilistic Forecasts from Incoherent and Abstaining Experts Decision Analysis. 5: 177-189. DOI: 10.1287/Deca.1080.0119 |
0.435 |
|
2008 |
Zheng H, Kulkarni SR, Poor HV. Dimensionally distributed learning models and algorithm Proceedings of the 11th International Conference On Information Fusion, Fusion 2008. DOI: 10.1109/ICIF.2008.4632362 |
0.514 |
|
2007 |
Wang CC, Kulkarni SR, Poor HV. Finite-Dimensional bounds on ℤm and binary LDPC codes with belief propagation decoders Ieee Transactions On Information Theory. 53: 56-81. DOI: 10.1109/Tit.2006.887085 |
0.423 |
|
2007 |
Predd JB, Kulkarni SR, Poor HV. A collaborative training algorithm for multi-sensor adaptive processing 2007 2nd Ieee International Workshop On Computational Advances in Multi-Sensor Adaptive Processing, Campsap. 297-300. DOI: 10.1109/CAMSAP.2007.4498024 |
0.777 |
|
2007 |
Predd JB, Kulkarni SR, Poor HV. Distributed Learning in Wireless Sensor Networks Wireless Sensor Networks: Signal Processing and Communications Perspectives. 185-214. DOI: 10.1002/9780470061794.ch8 |
0.77 |
|
2006 |
Hannig J, Chong EKP, Kulkarni SR. Relative frequencies of generalized simulated annealing Mathematics of Operations Research. 31: 199-216. DOI: 10.1287/Moor.1050.0177 |
0.367 |
|
2006 |
Cai H, Kulkarni SR, Verdú S. An algorithm for universal lossless compression with side information Ieee Transactions On Information Theory. 52: 4008-4016. DOI: 10.1109/Tit.2006.880020 |
0.399 |
|
2006 |
Cai H, Kulkarni SR, Verdú S. Universal divergence estimation for finite-alphabet sources Ieee Transactions On Information Theory. 52: 3456-3475. DOI: 10.1109/Tit.2006.878182 |
0.346 |
|
2006 |
Predd JB, Kulkarni SR, Poor HV. Consistency in models for distributed learning under communication constraints Ieee Transactions On Information Theory. 52: 52-63. DOI: 10.1109/Tit.2005.860420 |
0.782 |
|
2006 |
Predd JB, Kulkarni SR, Poor HV, Osherson DN. Scalable algorithms for aggregating disparate forecasts of probability 2006 9th International Conference On Information Fusion, Fusion. DOI: 10.1109/ICIF.2006.301582 |
0.778 |
|
2006 |
Predd JB, Kulkarni SR, Poor HV. Distributed kernel regression: An algorithm for training collaboratively 2006 Ieee Information Theory Workshop, Itw 2006. 332-336. |
0.813 |
|
2005 |
Predd JB, Kulkarni SR, Vincent Poor H. Regression in sensor networks: Training distributively with alternating projections Proceedings of Spie - the International Society For Optical Engineering. 5910: 1-15. DOI: 10.1117/12.620194 |
0.807 |
|
2005 |
Wang C, Kulkarni S, Poor H. Density Evolution for Asymmetric Memoryless Channels Ieee Transactions On Information Theory. 51: 4216-4236. DOI: 10.1109/Tit.2005.858931 |
0.437 |
|
2005 |
Wang Q, Kulkarni SR, Verdú S. Divergence estimation of continuous distributions based on data-dependent partitions Ieee Transactions On Information Theory. 51: 3064-3074. DOI: 10.1109/Tit.2005.853314 |
0.37 |
|
2004 |
Reznik A, Kulkarni SR, Verdú S. Degraded Gaussian multirelay channel: Capacity and optimal power allocation Ieee Transactions On Information Theory. 50: 3037-3046. DOI: 10.1109/Tit.2004.838373 |
0.552 |
|
2004 |
Jovičić A, Viswanath P, Kulkarni SR. Upper bounds to transport capacity of wireless networks Ieee Transactions On Information Theory. 50: 2555-2565. DOI: 10.1109/Tit.2004.836936 |
0.308 |
|
2004 |
Cai H, Kulkarni S, Verdu S. Universal Entropy Estimation Via Block Sorting Ieee Transactions On Information Theory. 50: 1551-1561. DOI: 10.1109/Tit.2004.830771 |
0.393 |
|
2003 |
Kulkarni SR, Reznik A, Verdu S. A "Small World" Approach to Heterogeneous Networks Communications in Information and Systems. 3: 325-348. DOI: 10.4310/Cis.2003.V3.N4.A6 |
0.563 |
|
2003 |
Radke RJ, Ramadge PJ, Kulkarni SR, Echigo T. Efficiently synthesizing virtual video Ieee Transactions On Circuits and Systems For Video Technology. 13: 325-337. DOI: 10.1109/Tcsvt.2003.811364 |
0.35 |
|
2000 |
Vidyasagar M, Kulkarni S. Some contributions to fixed-distribution learning theory Ieee Transactions On Automatic Control. 45: 217-234. DOI: 10.1109/9.839945 |
0.384 |
|
2000 |
Bartlett PL, Ben-David S, Kulkarni SR. Learning changing concepts by exploiting the structure of change Machine Learning. 41: 153-174. DOI: 10.1023/A:1007604202679 |
0.361 |
|
1999 |
Kulkarni SR, Posner SE. Universal output prediction and nonparametric regression for arbitrary data Lecture Notes in Control and Information Sciences. 254-268. DOI: 10.1007/Bfb0109733 |
0.32 |
|
1998 |
Kulkarni S, Lugosi G, Venkatesh S. Learning pattern classification-a survey Ieee Transactions On Information Theory. 44: 2178-2206. DOI: 10.1109/18.720536 |
0.311 |
|
1998 |
Bartlett PL, Kulkarni SR. The complexity of model classes, and smoothing noisy data Systems & Control Letters. 34: 133-140. DOI: 10.1016/S0167-6911(98)00008-5 |
0.318 |
|
1997 |
Wang I-, Chong EKP, Kulkarni SR. Weighted averaging and stochastic approximation Mathematics of Control, Signals, and Systems. 10: 41-60. DOI: 10.1007/Bf01219775 |
0.393 |
|
1996 |
Wang S, Liu B, Kulkarni SR. Model-based reconstruction of multiple circular and elliptical objects from a limited number of projections. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society. 5: 1386-90. PMID 18285230 DOI: 10.1109/83.535853 |
0.353 |
|
1996 |
Kulkarni S, Horn C. An alternative proof for convergence of stochastic approximation algorithms Ieee Transactions On Automatic Control. 41: 419-424. DOI: 10.1109/9.486642 |
0.399 |
|
1996 |
Wang I, Chong EKP, Kulkarni SR. Equivalent necessary and sufficient conditions on noise sequences for stochastic approximation algorithms Advances in Applied Probability. 28: 784-801. DOI: 10.1017/S0001867800046498 |
0.342 |
|
1995 |
Kulkarni SR, Zeitouni O. A General Classification Rule for Probability Measures The Annals of Statistics. 23: 1393-1407. DOI: 10.1214/Aos/1176324714 |
0.378 |
|
1994 |
Kulkarni SR, Mitter SK, Tsitsiklis JN, Richardson TJ. Local Versus Nonlocal Computation of Length of Digitized Curves Ieee Transactions On Pattern Analysis and Machine Intelligence. 16: 711-718. DOI: 10.1109/34.297951 |
0.547 |
|
1994 |
Dudley RM, Kulkarni SR, Richardson T, Zeitouni O. A Metric Entropy Bound is Not Sufficient for Learnability Ieee Transactions On Information Theory. 40: 883-885. DOI: 10.1109/18.335898 |
0.355 |
|
1993 |
Kulkarni SR, Mitter SK, Tsitsiklis JN, Zeitouni O. PAC Learning with Generalized Samples and an Application to Stochastic Geometry Ieee Transactions On Pattern Analysis and Machine Intelligence. 15: 933-942. DOI: 10.1109/34.232080 |
0.554 |
|
1993 |
Kulkarni SR, Mitter SK, Tsitsiklis JN. Active Learning Using Arbitrary Binary Valued Queries Machine Learning. 11: 23-35. DOI: 10.1023/A:1022627018023 |
0.602 |
|
1992 |
Lele AS, Willsky AS, Kulkarni SR. Convex-polygon estimation from support-line measurements and applications to target reconstruction from laser-radar data Journal of the Optical Society of America a: Optics and Image Science, and Vision. 9: 1693-1714. DOI: 10.1364/Josaa.9.001693 |
0.303 |
|
1991 |
Kulkarni SR, Zeitouni O. Can one decide the type of the mean from the empirical measure? Statistics & Probability Letters. 12: 323-327. DOI: 10.1016/0167-7152(91)90100-6 |
0.362 |
|
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