Research

I'm fascinated by the fact that we can learn unobserved structures of the data in a principled manner. I've been focusing on spectral learning of latent-variable models. I'm generally interested in representation learning and its application in supervised (especially structured) tasks.


Publications

    • Karl Stratos, Do-kyum Kim, Michael Collins, and Daniel Hsu.
      A Spectral Algorithm for Learning Class-Based n-gram Models of Natural Language. [pdf]
      In Proceedings of UAI (2014).
      • Here's a longer version that has an appendix on sample complexity.
      • Here's code cca I used for deriving word embeddings as described in this paper.
      • Here's code greedo for greedy agglomerative clustering of word vectors.
    • Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster and Lyle Ungar.
      Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity [pdf].
      In JMLR (2014).
    • Karl Stratos, Alexander M. Rush, Shay B. Cohen, and Michael Collins.
      Spectral Learning of Refinement HMMs [pdf][slides].
      In Proceedings of CoNLL (2013).
    • Shay B. Cohen, Karl Stratos, Michael Collins, Dean Foster and Lyle Ungar.
      Experiments with Spectral Learning of Latent-Variable PCFGs [pdf].
      In Proceedings of NAACL (2013).
    • Shay B. Cohen, Karl Stratos, Michael Collins, Dean Foster and Lyle Ungar.
      Spectral Learning of Latent-Variable PCFGs [pdf][slides].
      In Proceedings of ACL (2012).
    • Alexander C. Berg, Tamara L. Berg, Hal Daum III, Jesse Dodge, Amit Goyal, Xufeng Han, Alyssa Mensch, Margaret Mitchell, Aneesh Sood, Karl Stratos, Kota Yamaguchi
      Understanding and Predicting Importance in Images [pdf].
      In Proceedings of CVPR (2012).
    • Karl Stratos, Lenhart K. Schubert, and Jonathan Gordon.
      Episodic Logic: Natural Logic + Reasoning [pdf].
      In Proceedings of KEOD (2011).
    • Lenhart Schubert, Jonathan Gordon, Karl Stratos, and Adina Rubinoff.
      Towards Adequate Knowledge and Natural Inference [pdf].
      In Proceedings of AAAI Fall Symposium on Advances in Cognitive Systems (2011).

Activities

2014 Summer: I had the benefit of interning with Slav Petrov and Emily Pitler at Google New York.

2013 Summer: I had the privilege of interning with Sham Kakade and T. J. Hazen at Microsoft Research New England.

2013 Summer: We gave a tutorial on spectral methods in NLP at NAACL 2013! Video [Part 1] [Part 2]

2011 Summer: I participated in the summer workshop at JHU CLSP as part of the "vision" team. I worked mainly on analyzing and modeling what people choose to describe in an image.

2010-2011: At URCS, I was part of the KNEXT project, a joint endeavor of Len Schubert, Jonathan Gordon, and Benjamin Van Durme (among others) to extract commonsense knowledge out of textual resources.

Informal Writings

These are just for fun.
    • Notes on the framework of Ando and Zhang (2005) [pdf].
    • Max margin training ("support vector machines") [pdf].
    • Shift-reduce dependency parsing [pdf].
    • Approximate CCA [pdf].
    • A Hitchhiker's Guide to PCA and CCA [pdf].
    • The Lorentz transformation [pdf].
    • A formulation of the EM algorithm for PCFGs [pdf].
    • A formulation of the EM algorithm for HMMs [pdf].
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