Short URL for this web page: http://research.microsoft.com/~jplatt
My research is focused on helping people become more effective through the use of machine learning. Part of my research is improving the data/human interface, which includes projects such as:
- Trill: an engine for high-performance in-memory streaming computations.
- Sho: a system for scientific computing on top of .NET
Another part of my research is into systems that automatically create representations for data, including:
- Creating representations that combine logical forms with continuous representations (e.g, representing antonyms in a vector space).
- Improving representations for speech recognition through stacked (deep) networks.
- Generating good representations for text and audio using Oriented Principal Components Analysis.
I've been a researcher at Microsoft Research since 1997, where I led the Statistical Media Processing project, and then the Knowledge Tools group. Before Microsoft, I was Director of Research at Synaptics. I received my Ph.D. from Caltech in 1989, where I studied both computer graphics and machine learning.
In 2006, I received a Technical Academy Award for my research in simulating cloth and other stretchy material for computer graphics.
- I have worked with a number of other people inside and outside Microsoft Research
- I was the program chair for the NIPS 2006 conference, and general chair for the NIPS 2007 conference.
- Asteroids I've discovered
- My Erdös number is 3, through John-Shawe Taylor. By some definition, my Kevin Bacon number is also 3, through Rachel McAdams.
Sorted by topic, then by date
- Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy by D. Zhou, Q. Liu, J. C. Platt, and C. Meek, Proc ICML (2014).
- Stat! - An Interactive Analytics Environment for Big Data by M. Barnett, B. Chandramouli, R. DeLine, S. Drucker, D. Fisher, J. Goldstein, P. Morrison, and J.C. Platt, Proc. SIGMOD (2013)
Learning from the Wisdom of Crowds by Minimax Entropy by D. Zhou, J.C. Platt, S. Basu, and Y. Mao, Proc. NIPS (2012).
Learning representations of data
- Polarity Inducing Latent Semantic Analysis by W.-T. Yih, G. Zweig, J.C. Platt, Proc. EMNLP-CoNLL, (2012).
- Computational Approaches for Sentence Completion by G. Zweig, J.C. Platt, C. Meek, C.J.C. Burges, A. Yessenalina, Q. Liu, Proc. ACL, (2012).
- Learning Discriminative Projections for Text Similarity Measures by W.-T. Yih, K. Toutanova, J.C. Platt, C. Meek, Proc. Co-NLL, (2011).
- Translingual Document Representations from Discriminative Projections by J.C. Platt, K. Toutanova, W.-T. Yih, Proc. Empirical Methods in NLP, (2010).
- FastMap, MetricMap, and Landmark MDS are all Nyström Algorithms By J. C. Platt, 10th International Workshop on Artificial Intelligence and Statistics, pp. 261-268, (2005).
Machine learning for visual object recognition
- Robust Scareware Image Detection by C. Seifert, J. W. Stokes, C. Colcernian, J.C. Platt, L. Lu, Proc. ICASSP (2013).
- Multiple Instance Boosting for Object Detection by P. Viola, J.C. Platt, C. Zhang, Advances in Neural Information Processing Systems, Vol. 18, pp. 1417-1426, (2006).
- Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis by P.Y. Simard, D. Steinkraus, J.C. Platt, Intl. Conf. Document Analysis and Recognition, pp. 958-962, (2003).
- QuickStroke: An Incremental On-line Chinese Handwriting Recognition System by N. P. Matić, J.C. Platt, T. Wang, 16th Intl. Conf. Pattern Recognition, vol. 3, pp. 435-439, (2002).
- A Convolutional Neural Network Hand Tracker by S. Nowlan and J. Platt, NIPS 7, pp.901-908, (1995).
- Postal Address Block Location Using A Convolutional Locator Network by R. Wolf and J. Platt, NIPS 6, pp. 745-752, (1994).
Intelligent signal processing
- Ensemble Deep Learning for Speech Recognition by L. Deng and J.C. Platt, Proc. Interspeech, (2014)
- HRTF Magnitude Synthesis via Sparse Representation of Anthropometric Features by P. Bilinski, J. Ahrens, M. R. P. Thomas, I. J. Tashev, and J. C. Platt, Proc. ICASSP (2014).
- Scalable stacking and learning for building deep architectures By L. Deng, D. Yu, J.C. Platt, in Proc. ICASSP, (2012)
- Redundant Bit Vectors for Quickly Searching High-Dimensional Regions By J. Goldstein, J. C. Platt, C.J.C. Burges, in Deterministic and Statistical Methods in Machine Learning, J. Winkler, M. Niranjan, N. Lawrence, (eds.), Springer Lecture Notes on Computer Science 3635, pp. 137-158, (2005). Previous version: Indexing High-Dimensional Regions for Fast Multimedia Identification, MSR-TR-2003-38, (2003).
- Distortion Discriminant Analysis for Audio Fingerprinting by C.J.C. Burges, J.C. Platt, S. Jana, IEEE Trans. on Speech and Audio Processing, Vol. 11, No. 3, pp. 165-174, (2003). Previous version appeared as “Extracting Noise-Robust Features from Audio Data” by C.J.C. Burges, J.C. Platt, S. Jana, ICASSP 2002, pp. I1021-I1024, (2002).
Please see the Statistical Media Processing project page for other, older papers.
Productivity tools based on machine learning
- Learning from multi-topic web documents for contextual advertisement by Y. Zhang, A.C. Surendran, J.C. Platt, M. Narasimhan, Proc. KDD, pp. 1051-1059, (2008).
- Scalable Summaries of Spoken Conversations by S. Basu, S. Gupta, M. Mahajan, P. Nguyen, J.C. Platt, Proc. Intelligent User Interfaces, (2008).
- Fast Variational Inference for Large-scale Internet Diagnosis by J.C. Platt, E. Kiciman, D.A. Maltz, Advances in Neural Information Processing Systems 20, 1169-1176, (2008).
- Mining Web Logs to Debug Distant Connectivity Problems by E. Kiciman, D.A. Maltz, M. Goldszmidt, J.C. Platt, ACM SIGCOMM 2006 Workshop on Mining Network Data, (2006).
- Automatic Discovery of Personal Topics to Organize Email by A.C. Surendran, J.C. Platt, E. Renshaw, 2nd Conference on Email and Anti-Spam, (2005).
- Automatic Misconfiguration Troubleshooting with PeerPressure by H. J. Wang, J. Platt, Y. Chen, R. Zhang, Y.-M. Wang, Proc. 6th Symposium on Operating Systems Design and Implementation, (2004). Shorter previous version as Peer Pressure for Automatic Troubleshooting pp. 398-399, ACM SIGMETRICS, (2004).
- Inductive Learning Algorithms and Representations for Text Categorization by S. Dumais, J. Platt, D. Heckerman, M. Sahami, 7th International Conference on Information and Knowledge Management, pp. 148-152, (1998).
Improved user interface for media
Please see the ClearType project page.
Support Vector Machines & Large Margin Learning
Please see the Support Vector Machine project page.