Sewoong Oh – Talks & Slides

A few past talks

PacGAN: the power of two samples for generative adversarial networks

• Princeton Workshop: slides and video file

SIGMETRICS, Rising Star Award Lecture, June 2017 – Matrix Completion and Crowdsourcing

Breaking the bandwidth barrier: local adaptive entropy estimator

• ICSA, Shanghai, China, December 2016 – Local Estimator for Differential Entropy

Achieving budget-optimality with adaptive schemes in crowdsourcing

• Workshop on Statistical Physics, learning, inference, and networks, Les Houches, France, March 2017

• ITA, UCSD, February 2017

• Statistics Department, University of Wisconsin, February 2017 – Adaptive Crowdsourcing

• CESG tele-seminar, Texas A&M, January 2017

• NIPS workshop on crowdsourcing, Barcelona, Spain, December 2016 – Adaptive Crowdsourcing

• INFORMS, Nashville, TN, November 2016 – Adaptive Crowdsourcing

• Machine Learning seminar, USC, October 2016

Spy vs. spy: Rumor source obfuscation

• ITA seminar, UCSD, May 2016

• ITA, UCSD, February 2016

• LIDS Seminar, MIT, November 2015 – Rumor source obfuscation

• Information Theory Workshop, Jeju Island, October 2015 – Rumor source obfuscation

Rumor source obfuscation A novel approach to designing messaging protocols over social networks that hides the source of a message presented at WNCG seminar, UT Austin, September 2015

• GRAMSIA 2015 – Rumor source obfuscation

Differential privacy

Privacy region and its applications Nexus of information and computation theories, Institut Henri Poincare, Paris, France, March 2016

• ITA 2015 – the composition theorem

Rank Aggregation from Pairwise Comparisons

• ISMP 2012 – Rank aggregation from pairwise comparisons

Designing Reliable and Efficient Crowdsourcing Systems

• Nexus of information and computation theories, Institut Henri Poincare, March 2016 – Optimality of BP for crowdsourcing

• IDeAS (Princeton) 2012 – Message-passing algorithm for singular vector computations

• CISS (Princeton) 2012 – Budget-optimal task allocation for reliable crowdsourcing systems

• IPAM workshop on mathematical challenges in graphical models 2012 – Message-passing algorithm for singular vector computations

Iterative learning for reliable crowdsourcing systems A novel approach to designing reliable and cost-efficient crowdsourcing systems presented at Twenty-fifth Conference on Neural Information Processing Systems(NIPS), 2011

• Allerton (UIUC) 2011 – Budget-optimal crowdsourcing using low-rank matrix approximations

Iterative learning from a crowd Brief presentation of the crowdsourcing model and our learning algorithm at Interdisciplinary Workshop on Information and Decision in Social Networks(WIDS), 2011

Fast Singular Vector Computation for Extremely Large Matrices

Gossip PCA Presentation at the ACM SIGMETRICS, 2011

Inferring Low-rank Matrices from Partial Information

• Stanford University PhD Oral Exam 2010 – Matrix completion: fundamental limits and efficient algorithms

• ACM SIGMETIRCS 2010 – Distributed Sensor localization from local connectivity

• IDEAS Seminar (Princeton) 2010 – Matrix completion: fundamental limits and efficient algorithms

• ITA Seminar (UCSD) 2010 – Matrix completion: fundamental limits and efficient algorithms

• Allerton (UIUC) 2009 – Matrix completion from noisy entries

• ISIT 2009 – Matrix completion from a few entries