Now showing items 16679-16698 of 35570

    • Learning Document-Level Semantic Properties from Free-Text Annotations 

      Branavan, Satchuthanan R.; Chen, Harr; Eisenstein, Jacob; Barzilay, Regina (AI Access Foundation, 2009-04)
      This paper presents a new method for inferring the semantic properties of documents by leveraging free-text keyphrase annotations. Such annotations are becoming increasingly abundant due to the recent dramatic growth in ...
    • Learning dynamics with social comparisons and limited memory 

      Block, Juan I.; Fudenberg, Drew; Levine, David K. (The Econometric Society, 2018-05)
      We study models of learning in games where agents with limited memory use social information to decide when and how to change their play. When agents observe only the aggregate distribution of payoffs and recall only ...
    • Learning efficient random maximum a-posteriori predictors with non-decomposable loss functions 

      Hazan, Tamir; Maji, Subhransu; Keshet, Joseph; Jaakkola, Tommi S. (Neural Information Processing Systems, 2013)
      In this work we develop efficient methods for learning random MAP predictors for structured label problems. In particular, we construct posterior distributions over perturbations that can be adjusted via stochastic gradient ...
    • Learning efficiently with approximate inference via dual losses 

      Meshi, Ofer; Sontag, David Alexander; Jaakkola, Tommi S.; Globerson, Amir (International Machine Learning Society, 2010-01)
      Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation. Previous approaches for learning for ...
    • Learning Experiments Using AB Testing at Scale 

      Chudzicki, Christopher; Pritchard, David E.; Chen, Zhongzhou (Association for Computing Machinery (ACM), 2015-03)
      We report the one of the first applications of treatment/control group learning experiments in MOOCs. We have compared the efficacy of deliberate practice-practicing a key procedure repetitively-with traditional practice ...
    • Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions 

      Amato, Christopher; Liu, Miao; Sivakumar, Kavinayan P; Omidshafiei, Shayegan; How, Jonathan P (Institute of Electrical and Electronics Engineers (IEEE), 2017-12)
      This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs ...
    • Learning From Animal Models of Obsessive-Compulsive Disorder 

      Monteiro, Patricia; Feng, Guoping (Elsevier, 2015-05)
      Obsessive-compulsive disorder (OCD) affects 2%-3% of the population worldwide and can cause significant distress and disability. Substantial challenges remain in the field of OCD research and therapeutics. Approved ...
    • Learning from each other: causal inference and American political development 

      Jenkins, Jeffery A.; McCarty, Nolan; Stewart III, Charles H (Springer Science and Business Media LLC, 2019-11)
      Within political science, a movement focused on increasing the credibility of causal inferences (CIs) has gained considerable traction in recent years. While CI has been incorporated extensively into most disciplinary ...
    • Learning from Experience, Simply 

      Lin, Song; Zhang, Juanjuan; Hauser, John R. (Institute for Operations Research and the Management Sciences (INFORMS), 2014-09)
      There is substantial academic interest in modeling consumer experiential learning. However, (approximately) optimal solutions to forward-looking experiential learning problems are complex, limiting their behavioral ...
    • Learning from neighboring strokes: Combining appearance and context for multi-domain sketch recognition 

      Ouyang, Tom Yu; Davis, Randall (Neural Information Processing Systems Foundation, Inc., 2009)
      We propose a new sketch recognition framework that combines a rich representation of low level visual appearance with a graphical model for capturing high level relationships between symbols. This joint model of appearance ...
    • Learning from open source software projects to improve scientific review 

      Ghosh, Satrajit S.; Klein, Arno; Avants, Brian; Millman, Jarrod K. (Frontiers Research Foundation, 2012-04)
      Peer-reviewed publications are the primary mechanism for sharing scientific results. The current peer-review process is, however, fraught with many problems that undermine the pace, validity, and credibility of science. ...
    • Learning From Others and Spontaneous Exploration: A Cross-Cultural Investigation 

      Shneidman, Laura; Gweon, Hyowon; Woodward, Amanda L.; Schulz, Laura E (Wiley Blackwell, 2016-05)
      How does early social experience affect children's inferences and exploration? Following prior work on children's reasoning in pedagogical contexts, this study examined U.S. children with less experience in formal schooling ...
    • Learning from potentially-biased statistics: Household inflation perceptions and expectations in Argentina 

      Cavallo, Alberto F.; Cruces, Guillermo; Perez-Truglia, Ricardo (Brookings Institution Press, 2016-03)
      When forming expectations, households may be influenced by perceived bias in the information they receive. In this paper, we study how individuals learn from potentially biased statistics using data from both a natural ...
    • Learning from Weaving for Digital Fabrication in Architecture 

      Muslimin, Rizal (MIT Press, 2010-08)
      This project restructures weaving performance in architecture by analyzing the tacit knowledge of traditional weavers through perceptual study and converting it into an explicit rule in computational design. Three ...
    • Learning Gaussian Graphical Models with Observed or Latent FVSs 

      Liu, Ying; Willsky, Alan S. (Neural Information Processing Systems Foundation, 2013-12)
      Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely used in many applications, and the trade-off between the modeling capacity and the efficiency of learning and inference has been an important research ...
    • Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures 

      Tan, Vincent Yan Fu; Anandkumar, Animashree; Willsky, Alan S. (Institute of Electrical and Electronics Engineers, 2010-04)
      The problem of learning tree-structured Gaussian graphical models from independent and identically distributed (i.i.d.) samples is considered. The influence of the tree structure and the parameters of the Gaussian distribution ...
    • Learning Gestures Using A Passive Data-Glove With RFID Tags 

      Kantareddy, Sai Nithin R.; Sun, Yongbin; Bhattacharyya, Rahul; Sarma, Sanjay E (Institute of Electrical and Electronics Engineers (IEEE), 2019-09)
      Hand gesture recognition enables non-tactile interfaces for human-machine interactions. Cameras are currently powerful tools to recognize these gestures, however, use of cameras is constrained by privacy concerns and need ...
    • Learning graphical models for hypothesis testing and classification 

      Tan, Vincent Yan Fu; Sanghavi, Sujay; Fisher, John W., III; Willsky, Alan S. (Institute of Electrical and Electronics Engineers (IEEE), 2010-07)
      Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques to exploit this modeling ability for ...
    • Learning graphical models from the Glauber dynamics 

      Bresler, Guy; Gamarnik, David; Shah, Devavrat (Institute of Electrical and Electronics Engineers (IEEE), 2014-09)
      In this paper we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics. The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) ...
    • Learning Graphical Models From the Glauber Dynamics 

      Bresler, Guy; Gamarnik, David; Shah, Devavrat (Institute of Electrical and Electronics Engineers (IEEE), 2017-06)
      In this paper, we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics (also known as the Gibbs sampler). The Glauber dynamics is a Markov chain that sequentially ...