S. Kulkarni

发表

An alternative proof for convergence of stochastic approximation algorithms

S. Kulkarni, C. Horn, 1996, IEEE Trans. Autom. Control..

EQUIVALENT NECESSARY AND SUFFICIENT CONDITIONS ON NOISE SEQUENCES FOR STOCHASTIC APPROXIMATION ALGORITHMS

E. Chong, S. Kulkarni, I-Jeng Wang, 1996 .

On metric entropy, Vapnik-Chervonenkis dimension, and learnability for a class of distributions

S. Kulkarni, Decision Systems., 1989 .

Covering numbers for real-valued function classes

Sanjeev R. Kulkarni, Peter L. Bartlett, S. E. Posner, 1997, IEEE Trans. Inf. Theory.

Divergence estimation of continuous distributions based on data-dependent partitions

Qing Wang, Sanjeev R. Kulkarni, Sergio Verdú, 2005, IEEE Transactions on Information Theory.

Universal estimation of entropy and divergence via block sorting

S. Kulkarni, S. Verdú, H. Cai, 2002, Proceedings IEEE International Symposium on Information Theory,.

Federated Learning With Quantized Global Model Updates

S. Kulkarni, H. Poor, Deniz Gündüz, 2020, ArXiv.

PAC learning with generalized samples and an application to stochastic geometry

John N. Tsitsiklis, Sanjeev R. Kulkarni, Ofer Zeitouni, 1992, COLT '92.

PAC Learning with Generalized Samples and an Applicaiton to Stochastic Geometry

John N. Tsitsiklis, Sanjeev R. Kulkarni, Ofer Zeitouni, 1993, IEEE Trans. Pattern Anal. Mach. Intell..

Local Versus Nonlocal Computation of Length of Digitized Curves

John N. Tsitsiklis, Sanjeev R. Kulkarni, Sanjoy K. Mitter, 1994, IEEE Trans. Pattern Anal. Mach. Intell..

An existence theorem and lattice approximations for a variational problem arising in computer vision

S. Mitter, S. Kulkarni, T.J. Richardon, 1990 .

A deterministic approach to throughput scaling in wireless networks

P. Viswanath, S. Kulkarni, 2002, Proceedings IEEE International Symposium on Information Theory,.

Model and controller selection policies based on output prediction errors

P. Ramadge, S. Kulkarni, 1996, IEEE Trans. Autom. Control..

An upper bound on the convergence time for distributed binary consensus

Sanjeev R. Kulkarni, Pan Hui, Paul W. Cuff, 2012, 2012 15th International Conference on Information Fusion.

Controller switching based on output prediction errors

P. Ramadge, S. Kulkarni, J. Hocherman-Frommer, 1998, IEEE Trans. Autom. Control..

Divergence Estimation for Multidimensional Densities Via $k$-Nearest-Neighbor Distances

Qing Wang, Sanjeev R. Kulkarni, Sergio Verdú, 2009, IEEE Transactions on Information Theory.

Bandit problems with side observations

H. Vincent Poor, Sanjeev R. Kulkarni, Chih-Chun Wang, 2005, IEEE Transactions on Automatic Control.

A general classification rule for probability measures

S. Kulkarni, O. Zeitouni, 1995 .

Problems of computational and informational complexity in machine vision and learning

Sanjeev R. Kulkarni, S. Kulkarni, 1991 .

Rates of convergence of nearest neighbor estimation under arbitrary sampling

Sanjeev R. Kulkarni, S. E. Posner, S. Kulkarni, 1995, IEEE Trans. Inf. Theory.

The Problem of Induction

G. Harman, S. Kulkarni, 2006 .

Convex set estimation from support line measurements and applications to target reconstruction from laser radar data

Sanjeev R. Kulkarni, Alan S. Willsky, Avinash S. Lele, 1990, Photonics West - Lasers and Applications in Science and Engineering.

Learning Pattern Classification - A Survey

Sanjeev R. Kulkarni, Gábor Lugosi, Santosh S. Venkatesh, 1998, IEEE Trans. Inf. Theory.

Finite-time lower bounds for the two-armed bandit problem

Sanjeev R. Kulkarni, Gábor Lugosi, G. Lugosi, 2000, IEEE Trans. Autom. Control..

Active Learning Using Arbitrary Binary Valued Queries

John N. Tsitsiklis, Sanjeev R. Kulkarni, Sanjoy K. Mitter, 1993, Machine Learning.

A Comprehensive Approach to Fusion for Microsensor Networks: Distributed and Hierarchical Inference, Communication, and Adaption REPORT DOCUMENTATION PAGE

John N. Tsitsiklis, Tommi S. Jaakkola, Sergio Verdu, 2000 .

Output distribution of the Burrows-Wheeler transform

S. Kulkarni, S. Verdú, K. Visweswariah, 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).

Consistency in Models for Communication Constrained Distributed Learning

H. Vincent Poor, Sanjeev R. Kulkarni, Joel B. Predd, 2004, COLT.

Distributed learning in wireless sensor networks

H. Vincent Poor, Sanjeev R. Kulkarni, Joel B. Predd, 2005, IEEE Signal Processing Magazine.

On the existence and memory requirements of convergent on-line decision rules

P. Ramadge, S. Kulkarni, 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science Proposal for Thesis Research in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

A. Willsky, S. Kulkarni, J. Fisher, 2022 .

Statistical Learning Theory as a Framework for the Philosophy of Induction

Sanjeev R. Kulkarni, G. Harman, S. Kulkarni, 2011 .

Induction and Simplicity

G. Harman, S. Kulkarni, 2006 .

September 1989 LIDS-P-1910 On Metric Entropy , Vapnik-Chervonenkis Dimension , and Learnability for a Class of Distributions 1

S. Kulkarni, 1989 .

Minimax Lower Bounds for the Two-Armed Bandit Problem

G. Lugosi, S. Kulkarni, 1997 .

Bandit problems with side observations

S. Kulkarni, H. Poor, Chih-Chun Wang, 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management

Warrren B Powell, S. Kulkarni, A. George, 2008 .

The complexity of model classes, and smoothing noisy data 1 1 An earlier version of this paper was p

P. Bartlett, S. Kulkarni, 1998 .

The complexity of model classes and smoothing noisy data

P. Bartlett, S. Kulkarni, 1996, Proceedings of 35th IEEE Conference on Decision and Control.

Adaptive Chain Coding for Arbitrary

S. Kulkarni, J. Koplowitz, C. L. Chang, 2008 .

S. Kulkarni, D. Malioutov, A. Akansu, 2016 .

Finite-Dimensional Bounds on Z m and Binary LDPC Codes with Belief Propagation Decoders : Stability Conditions on Z m Codes and Cutoff Rate Analysis on Non-symmetric Binary Channels

Sanjeev R. Kulkarni, Chih-Chun Wang, H. Vincent, 2004 .

Controller Switching Based on Output Prediction Errors-Automatic Control, IEEE Transactions on

P. Ramadge, S. Kulkarni, J. Hocherman-Frommer, 1998 .

Learning Function Estimation

G. Harman, S. Kulkarni, 2011 .

Convergence of the Robbins-Monro algorithm under arbitrary disturbances

Sanjeev R. Kulkarni, S. Kulkarni, C. Horn, 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

Some discrete approximations to a variational method for image segmentation

S. Mitter, S. Kulkarni, Decision Systems., 1991 .

Consistency and rates of convergence of k/sub n/ nearest neighbor estimation under arbitrary sampling

S. Kulkarni, S. Posner, 1994, Proceedings of 1994 Workshop on Information Theory and Statistics.

Estimation by the nearest neighbor rule under arbitrary sampling

S. Kulkarni, S. Posner, S. R. Kulkarni, 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

A Paradigm for Class Identiication Problems

S. Kulkarni, D. Tse, 1992 .

Robust fitting of ellipses and spheroids

H. Vincent Poor, Sanjeev R. Kulkarni, Jieqi Yu, 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

Learning pattern classification— a survey (invited paper)

Sanjeev R. Kulkarni, Gábor Lugosi, Santosh S. Venkatesh, 2000 .

( SPECIAL SECTION - DISTRIBUTED SIGNAL PROCESSING IN SENSOR NETWORKS )

Rabab K. Ward, Petar M. Djuric, Ling Guan, 2006 .

On Finite-Dimensional Bounds for LDPC-like Codes with Iterative Decoding

S. Kulkarni, Chih-Chun Wang, H. Vincent, 2004 .

Aggregating Forecasts of Chance from Incoherent and Abstaining Experts

S. Kulkarni, D. Osherson, H. Poor, 2007 .

Statistical Measures of Dependence for Financial Data

S. Kulkarni, D. Malioutov, A. Akansu, 2016 .

Energy-distortion tradeoffs in multiple-access channels with feedback

S. Kulkarni, S. Verdú, H. Poor, 2010, 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo).

Zero-error target tracking through limited querying of binary sensors

Hua Li, Sanjeev R. Kulkarni, Edwin K. P. Chong, 2006 .

Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process

S. Kulkarni, D. Malioutov, A. Akansu, 2016 .

Prediction error based controller switching policies

P. Ramadge, S. Kulkarni, 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

Nonparametric control algorithms for nonlinear fading memory systems

S. Kulkarni, S. Sandilya, 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

Response to Shaffer, Thagard, Strevens and Hanson

G. Harman, S. Kulkarni, 2009 .

Density Evolution for Asymmetric Memoryless Channels 1

S. Kulkarni, Chih-Chun Wang, H. Vincent, 2003 .

Finite-memory hypothesis testing with semi-bounded likelihood ratio

S. Kulkarni, J. Koplowitz, 1997, Proceedings of IEEE International Symposium on Information Theory.

Overview: Financial Signal Processing and Machine Learning

Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov, 2016 .

On the existence of strongly consistent rules for estimation and classification

S. Kulkarni, O. Zeitouni, 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

Source codes as random number generators

Sanjeev R. Kulkarni, Sergio Verdú, Karthik Visweswariah, 1997, Proceedings of IEEE International Symposium on Information Theory.

An Elementary Introduction to Statistical Learning Theory

Sanjeev R. Kulkarni, Gilbert Harman, G. Harman, 2011 .

Reliable Reasoning: Induction and Statistical Learning Theory

Sanjeev R. Kulkarni, Gilbert Harman, G. Harman, 2007 .

Inductive Simplicity and the Matrix

Sanjeev R. Kulkarni, Gilbert Harman, G. Harman, 2003 .

Universal Estimation of Information Measures for Analog Sources

Sanjeev R. Kulkarni, Sergio Verdu, Qing Wang, 2009, Found. Trends Commun. Inf. Theory.

Probability Estimation in the Rare-Events Regime

Sanjeev R. Kulkarni, Pramod Viswanath, Aaron B. Wagner, 2011, IEEE Transactions on Information Theory.

Multicasting in Large Wireless Networks: Bounds on the Minimum Energy Per Bit

Sanjeev R. Kulkarni, Aman Jain, Sergio Verdú, 2009, IEEE Transactions on Information Theory.

Multicasting in large random wireless networks: Bounds on the minimum energy per bit

Sanjeev R. Kulkarni, Aman Jain, Sergio Verdú, 2009, 2009 IEEE International Symposium on Information Theory.

CVaR Minimizations in Support Vector Machines

Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry M. Malioutov, 2016 .

Financial Signal Processing and Machine Learning

Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov, 2016 .

Federated Learning With Quantized Global Model Updates pdf

Deniz Gündüz, Sanjeev R. Kulkarni, H. Vincent Poor, 2020, ArXiv.

A Nearest-Neighbor Approach to Estimating Divergence between Continuous Random Vectors

Sanjeev R. Kulkarni, Sergio Verdú, Qing Wang, 2006, 2006 IEEE International Symposium on Information Theory.

Relative Frequencies of Generalized Simulated Annealing

Sanjeev R. Kulkarni, Edwin K. P. Chong, Jan Hannig, 2006, Math. Oper. Res..

Relative Frequencies of Non-homogeneous Markov Chains in Simulated Annealing and Related Algorithms

J. Hannig, E.K.P. Chong, S.R. Kulkarni, 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

8. Kernel Rules

G. Harman, S. Kulkarni, 2011 .

Universal output prediction and nonparametric regression for arbitrary data

S. Kulkarni, S. Posner, 1999 .

Nonparametric output prediction for nonlinear fading memory systems

Sanjeev R. Kulkarni, S. E. Posner, S. Kulkarni, 1999, IEEE Trans. Autom. Control..

Data-dependent kn-NN and kernel estimators consistent for arbitrary processes

Sanjeev R. Kulkarni, Sathyakama Sandilya, S. E. Posner, 2002, IEEE Trans. Inf. Theory.

Some contributions to fixed-distribution learning theory

Sanjeev R. Kulkarni, Mathukumalli Vidyasagar, M. Vidyasagar, 2000, IEEE Trans. Autom. Control..

Consistency in models for distributed learning under communication constraints

H. Vincent Poor, Sanjeev R. Kulkarni, Joel B. Predd, 2005, IEEE Transactions on Information Theory.

Energy Efficiency of Decode-and-Forward for Wideband Wireless Multicasting

Sanjeev R. Kulkarni, Aman Jain, Sergio Verdú, 2011, IEEE Transactions on Information Theory.

Minimum Energy per Bit for Wideband Wireless Multicasting: Performance of Decode-and-Forward

Sanjeev R. Kulkarni, Aman Jain, Sergio Verdú, 2010, 2010 Proceedings IEEE INFOCOM.

Degraded Gaussian multirelay channel: capacity and optimal power allocation

Sanjeev R. Kulkarni, Sergio Verdú, Alex Reznik, 2004, IEEE Transactions on Information Theory.

A "Small World" Approach to Heterogeneous Networks

Sanjeev R. Kulkarni, Alex Reznik, Sergio Verdú, 2003, Commun. Inf. Syst..

Learning decision rules for pattern classification under a family of probability measures

M. Vidyasagar, S. Kulkarni, 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

Efficiently synthesizing virtual video

Sanjeev R. Kulkarni, Peter J. Ramadge, Tomio Echigo, 2003, IEEE Trans. Circuits Syst. Video Technol..

Convergence of Federated Learning Over a Noisy Downlink pdf

Deniz Gündüz, Sanjeev R. Kulkarni, H. Vincent Poor, 2020, IEEE Transactions on Wireless Communications.

Communication-estimation tradeoffs in wireless sensor networks

Sanjeev R. Kulkarni, Stuart C. Schwartz, Sung-Hyun Son, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

Aggregating disparate judgments using a coherence penalty

H. Vincent Poor, Sanjeev R. Kulkarni, Guanchun Wang, 2009, 2009 43rd Annual Conference on Information Sciences and Systems.

Upper bounds to transport capacity of wireless networks

Sanjeev R. Kulkarni, Pramod Viswanath, Aleksandar Jovicic, 2004, IEEE Transactions on Information Theory.

Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations

Sanjeev R. Kulkarni, Robert E. Schapire, Aurelie C. Lozano, 2005, NIPS.

$$\sin (\omega x)$$sin(ωx) Can Approximate Almost Every Finite Set of Samples

G. Harman, S. Kulkarni, Hariharan Narayanan, 2015 .

Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment

H. Vincent Poor, Sanjeev R. Kulkarni, Daniel N. Osherson, 2011, Decis. Anal..

Probabilistic Coherence and Proper Scoring Rules

H. Vincent Poor, Sanjeev R. Kulkarni, Daniel N. Osherson, 2007, IEEE Transactions on Information Theory.

A Review of Some Extensions to the PAC Learning Model

Sanjeev R. Kulkarni, S. Kulkarni, 1996 .

Précis of Reliable Reasoning: Induction and Statistical Learning Theory

Sanjeev R. Kulkarni, Gilbert Harman, G. Harman, 2009 .

A Collaborative Training Algorithm for Distributed Learning

H. Vincent Poor, Sanjeev R. Kulkarni, Joel B. Predd, 2009, IEEE Transactions on Information Theory.

Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models

Sanjeev R. Kulkarni, Pan Hui, Paul W. Cuff, 2011, 2011 IEEE 17th International Conference on Parallel and Distributed Systems.

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

S. Kulkarni, H. Poor, Deniz Gündüz, 2020, IEEE Transactions on Wireless Communications.

Citizen Forecasts of the 2008 U.S. Presidential Election

Michael K. Miller, S. Kulkarni, D. Osherson, 2012 .

An Introduction to Elementary Statistical Learning Theory

Sanjeev R. Kulkarni, Gilbert Harman, G. Harman, 2011 .

Rapid estimation of camera motion from compressed video with application to video annotation

Sanjeev R. Kulkarni, Yap-Peng Tan, Peter J. Ramadge, 2000, IEEE Trans. Circuits Syst. Video Technol..

Federated Learning with Downlink Device Selection

H. Vincent Poor, Sanjeev R. Kulkarni, Mohammad Mohammadi Amiri, 2021, 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

Machine Learning Methods for Attack Detection in the Smart Grid

H. Vincent Poor, Sanjeev R. Kulkarni, Fatos T. Yarman-Vural, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models

H. Vincent Poor, Sanjeev R. Kulkarni, Fatos T. Yarman-Vural, 2013, IEEE Journal on Selected Areas in Communications.

Universal estimation of divergence for continuous distributions via data-dependent partitions

Sanjeev R. Kulkarni, Sergio Verdú, Qing Wang, 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

Distributed models for sparse attack construction and state vector estimation in the smart grid

H. Vincent Poor, Sanjeev R. Kulkarni, Fatos T. Yarman-Vural, 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

On-line learning of functions of bounded variation under various sampling schemes

Sanjeev R. Kulkarni, S. E. Posner, S. Kulkarni, 1993, COLT '93.

Multiagent Reinforcement Learning Based Spectrum Sensing Policies for Cognitive Radio Networks

H. Vincent Poor, Sanjeev R. Kulkarni, Visa Koivunen, 2013, IEEE Journal of Selected Topics in Signal Processing.

Principal curves with bounded turn

S. Kulkarni, S.R. Kulkarni, S. Sandilya, 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).

Wishful Thinking and Social Influence in the 2008 U . S . Presidential Election

Michael K. Miller, S. Kulkarni, D. Osherson, 2011 .

Mutual information scheduling for ranking

Sanjeev R. Kulkarni, Paul W. Cuff, Hamza Aftab, 2011, 14th International Conference on Information Fusion.

Density evolution for asymmetric memoryless channels

H. Vincent Poor, Sanjeev R. Kulkarni, Chih-Chun Wang, 2005, IEEE Transactions on Information Theory.

Update Aware Device Scheduling for Federated Learning at the Wireless Edge

H. Vincent Poor, Deniz Gündüz, Sanjeev R. Kulkarni, 2020, 2020 IEEE International Symposium on Information Theory (ISIT).

Aggregating Probabilistic Forecasts from Incoherent and Abstaining Experts

H. Vincent Poor, Sanjeev R. Kulkarni, Daniel N. Osherson, 2008, Decis. Anal..

Aggregating Forecasts of Chance from Incoherent and Abstaining Experts

Joel B. Predd, S. Kulkarni, D. Osherson, 2007 .

Exact solution of the Poisson-Boltzmann equation for two spheres with fixed surface potentials

S. Kulkarni, E. Barouch, 1986 .

Blind Federated Edge Learning pdf

Deniz Gündüz, Sanjeev R. Kulkarni, H. Vincent Poor, 2020, IEEE Transactions on Wireless Communications.

Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks

Sanjeev R. Kulkarni, Fernando Pérez-Cruz, S. Kulkarni, 2010, IEEE Signal Processing Letters.

A deterministic approach to throughput scaling in wireless networks

Sanjeev R. Kulkarni, Pramod Viswanath, P. Viswanath, 2002, IEEE Transactions on Information Theory.

Exhausting Error-Prone Patterns in LDPC Codes pdf

H. Vincent Poor, Sanjeev R. Kulkarni, Chih-Chun Wang, 2006, ArXiv.

Improving augmented reality using recommender systems

Sanjeev R. Kulkarni, Pan Hui, Zhuo Zhang, 2013, RecSys.

Robust ellipse and spheroid fitting

H. Vincent Poor, Sanjeev R. Kulkarni, Jieqi Yu, 2012, Pattern Recognit. Lett..

Attribute-Distributed Learning: Models, Limits, and Algorithms

H. Vincent Poor, Sanjeev R. Kulkarni, Haipeng Zheng, 2011, IEEE Transactions on Signal Processing.

Incremental Reformulated Automatic Relevance Determination

H. Vincent Poor, Sanjeev R. Kulkarni, Dmitriy Shutin, 2012, IEEE Transactions on Signal Processing.

Smarter security in the smart grid

H. Vincent Poor, Sanjeev R. Kulkarni, Fatos T. Yarman-Vural, 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

The application of differential privacy for rank aggregation: Privacy and accuracy

Sanjeev R. Kulkarni, Paul W. Cuff, Shang Shang, 2014, 17th International Conference on Information Fusion (FUSION).

Multiresolution chain coding of contours

Sanjeev R. Kulkarni, Jack Koplowitz, J. S. Lerman, 1994, Proceedings of 1st International Conference on Image Processing.

Finding All Small Error-Prone Substructures in LDPC Codes

H. Vincent Poor, Sanjeev R. Kulkarni, Chih-Chun Wang, 2009, IEEE Transactions on Information Theory.

Finite-Dimensional Bounds on ${\BBZ}_m$ and Binary LDPC Codes With Belief Propagation Decoders

H. Vincent Poor, Sanjeev R. Kulkarni, Chih-Chun Wang, 2006, IEEE Transactions on Information Theory.

Automated analysis and annotation of basketball video

Sanjeev R. Kulkarni, Yap-Peng Tan, Peter J. Ramadge, 1997, Electronic Imaging.

Universal Divergence Estimation for Finite-Alphabet Sources

Sanjeev R. Kulkarni, Sergio Verdú, Haixiao Cai, 2006, IEEE Transactions on Information Theory.

Improving aggregated forecasts of probability

H. Vincent Poor, Sanjeev R. Kulkarni, Daniel N. Osherson, 2011, 2011 45th Annual Conference on Information Sciences and Systems.

Scalable Algorithms for Aggregating Disparate Forecasts of Probability

H. Vincent Poor, Sanjeev R. Kulkarni, Daniel N. Osherson, 2006, 2006 9th International Conference on Information Fusion.

Measurement and understanding of Cyberlocker URL-sharing sites: Focus on movie files

Sanjeev R. Kulkarni, Pan Hui, Zhuo Zhang, 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

Regularized Variational Bayesian Learning of Echo State Networks with Delay&Sum Readout

H. Vincent Poor, Sanjeev R. Kulkarni, Dmitriy Shutin, 2012, Neural Computation.

Self-Reflecting Methods of Learning Research

Chris L. S. Coryn, Daniel A. Braun, Michael E. Lusignan, 2012 .

A metric entropy bound is not sufficient for learnability

Sanjeev R. Kulkarni, Ofer Zeitouni, T. J. Richardson, 1994, IEEE Trans. Inf. Theory.

A framework for measuring video similarity and its application to video query by example

Sanjeev R. Kulkarni, Yap-Peng Tan, Peter J. Ramadge, 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

Combining cost-based and rule-based knowledge in complex resource allocation problems

Sanjeev R. Kulkarni, Warren B. Powell, Arun Marar, 2006 .

Capacity and Optimal Resource Allocation in the Degraded Gaussian Relay Channel with Multiple Relays

Sanjeev R. Kulkarni, Alex Reznik, Sergio Verdu, 2002 .

Controller switching based on output predictions

Sanjeev R. Kulkarni, Peter J. Ramadge, Judith Hocherman-Frommer, 1999 .

Strong Consistency of the Good-Turing Estimator

Sanjeev R. Kulkarni, Pramod Viswanath, Aaron B. Wagner, 2006, 2006 IEEE International Symposium on Information Theory.

Introduction: Classification, Learning, Features, and Applications

Sanjeev R. Kulkarni, Gilbert Harman, G. Harman, 2011 .

Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations

Sanjeev R. Kulkarni, Robert E. Schapire, Aurelie C. Lozano, 2014, IEEE Transactions on Information Theory.

Weighted averaging and stochastic approximation

Sanjeev R. Kulkarni, Edwin K. P. Chong, I-Jeng Wang, 1996, Proceedings of 35th IEEE Conference on Decision and Control.

Estimating correspondence in digital video

Sanjeev R. Kulkarni, Peter J. Ramadge, Richard J. Radke, 2001, Proceedings International Conference on Information Technology: Coding and Computing.

Density Evolution for Asymmetric Memoryless Channels: The Perfect Projection Condition and the Typicality of the Linear LDPC Code Ensemble

H. Vincent Poor, Sanjeev R. Kulkarni, Chih-Chun Wang, 2004 .

Reduction of Timed Hybrid Systems

S. Di Gennaro, S.R. Kulkarni, C. Horn, 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

Density estimation from an individual numerical sequence

A. Nobel, S. Kulkarni, G. Morvai, 1998, Proceedings. 1998 IEEE International Symposium on Information Theory (Cat. No.98CH36252).

Noise Conditions for Prespecified Convergence Rates of Stochastic Approximation Algorithms

Sanjeev R. Kulkarni, Edwin K. P. Chong, I-Jeng Wang, 1999, IEEE Trans. Inf. Theory.

Iterative collaborative filtering for recommender systems with sparse data

Sanjeev R. Kulkarni, Zhuo Zhang, Paul W. Cuff, 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

Detection of shilling attacks in recommender systems via spectral clustering

Sanjeev R. Kulkarni, Zhuo Zhang, S. Kulkarni, 2014, 17th International Conference on Information Fusion (FUSION).

A randomwalk based model incorporating social information for recommendations

Sanjeev R. Kulkarni, Pan Hui, Paul W. Cuff, 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

Local tests for consistency of support hyperplane data

Sanjeev R. Kulkarni, W. Clem Karl, George C. Verghese, 1996, Journal of Mathematical Imaging and Vision.

Neural Networks: Perceptrons

Sanjeev R. Kulkarni, Gilbert Harman, G. Harman, 2011 .

Model Classes, Approximation, and Metrics for Dynamic Processing of Urban Terrain Data

Richard G. Baraniuk, Stanley Osher, Andrew J. Kurdila, 2013 .

The Problem of Induction

G. Harman, S. Kulkarni, 2006 .

Nonbacktracking Bounds on the Influence in Independent Cascade Models

Sanjeev R. Kulkarni, Eun Jee Lee, Emmanuel Abbe, 2017, NIPS.

Universal entropy estimation via block sorting

Sanjeev R. Kulkarni, Sergio Verdú, Haixiao Cai, 2004, IEEE Transactions on Information Theory.

Temporal Causal Modeling

Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov, 2016 .

Convex shape reconstruction from noisy ray probe measurements

Sanjeev R. Kulkarni, J. S. Lerman, S. Kulkarni, 1995, Proceedings., International Conference on Image Processing.

RAY PROBE MEASUREMENTS

S. Kulkarni, J. S. Lermanl, 1995 .