Recently Added

  • Beliefs in decision-making cascades 

    Seo, Daewon; Raman, Ravi Kiran; Rhim, Joong Bum; Goyal, Vivek K.; Varshney, Lav R. (Institute of Electrical and Electronics Engineers (IEEE), 2019-10-01)
    This work explores a social learning problem with agents having nonidentical noise variances and mismatched beliefs. We consider an N-agent binary hypothesis test in which each agent sequentially makes a decision based not ...
  • Seeing around corners with edge-resolved transient imaging 

    Rapp, Joshua; Saunders, Charles; Tachella, Julián; Murray-Bruce, John; Altmann, Yoann; Tourneret, Jean-Yves; McLaughlin, Stephen; Dawson, Robin M.A.; Wong, Franco N.C.; Goyal, Vivek K. (2020-11-23)
    Non-line-of-sight (NLOS) imaging is a rapidly growing field seeking to form images of objects outside the field of view, with potential applications in autonomous navigation, reconnaissance, and even medical imaging. The ...
  • Distributed hypothesis testing with social learning and symmetric fusion 

    Rhim, Joong Bum; Goyal, Vivek K. (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2014-12-01)
    We study the utility of social learning in a distributed detection model with agents sharing the same goal: a collective decision that optimizes an agreed upon criterion. We show that social learning is helpful in some ...
  • Social teaching: being informative vs. being right in sequential decision making 

    Rhim, Joong Bum; Goyal, Vivek K. (IEEE, 2013-07)
    We consider sequential Bayesian binary hypothesis testing where each individual agent makes a binary decision motivated only by minimization of her own perception of the Bayes risk. The information available to each agent ...
  • Keep ballots secret: on the futility of social learning in decision making by voting 

    Rhim, Joong Bum; Goyal, Vivek K. (IEEE, 2013-05)
    We show that social learning is not useful in a model of team binary decision making by voting, where each vote carries equal weight. Specifically, we consider Bayesian binary hypothesis testing where agents have any ...
  • Asymptotic analysis of MAP estimation via the replica method and applications to compressed sensing 

    Rangan, Sundeep; Fletcher, Alyson K.; Goyal, Vivek K. (Institute of Electrical and Electronics Engineers (IEEE), 2012-03)
    The replica method is a nonrigorous but well-known technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems. This paper applies the replica method, under the assumption of ...
  • Message-passing de-quantization with applications to compressed sensing 

    Kamilov, U.S.; Goyal, V.K.; Rangan, S. (Institute of Electrical and Electronics Engineers (IEEE), 2012-12)
    Estimation of a vector from quantized linear measurements is a common problem for which simple linear techniques are suboptimal-sometimes greatly so. This paper develops message-passing de-quantization (MPDQ) algorithms ...
  • An information-theoretic characterization of channels that die 

    Varshney, Lav R.; Mitter, Sanjoy K.; Goyal, Vivek K. (Institute of Electrical and Electronics Engineers (IEEE), 2012-09)
    Given the possibility of communication systems failing catastrophically, we investigate limits to communicating over channels that fail at random times. These channels are finite-state semi-Markov channels. We show that ...
  • Reduced damage in electron microscopy by using interaction-free measurement and conditional reillumination 

    Agarwal, Akshay; Berggren, Karl K.; van Staaden, Yuri J.; Goyal, Vivek K.
    Interaction-free measurement (IFM) has been proposed as a means of high-resolution, low-damage imaging of radiation-sensitive samples, such as biomolecules and proteins. The basic setup for IFM is a Mach-Zehnder interferometer, ...
  • Quantization of prior probabilities for collaborative distributed hypothesis testing 

    Rhim, Joong Bum; Varshney, Lav R.; Goyal, Vivek K. (Institute of Electrical and Electronics Engineers (IEEE), 2012-09)
    This paper studies the quantization of prior probabilities, drawn from an ensemble, in distributed detection with data fusion by combination of binary local decisions. Design and performance equivalences between a team of ...

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