Well File:
- Well Planning ( results)
- Shallow Hazard Analysis ( results)
- Well Plat ( results)
- Wellbore Schematic ( results)
- Directional Survey ( results)
- Fluid Sample ( results)
- Log ( results)
- Density ( results)
- Gamma Ray ( results)
- Mud ( results)
- Resistivity ( results)
- Report ( results)
- Daily Report ( results)
- End of Well Report ( results)
- Well Completion Report ( results)
- Rock Sample ( results)
Uri Stemmer
Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta
Neural Information Processing Systems http://nips.cc/
Differentially Private k-Means with Constant Multiplicative Error
Uri Stemmer, Haim Kaplan
We design new differentially private algorithms for the Euclidean k-means problem, both in the centralized model and in the local model of differential privacy. In both models, our algorithms achieve significantly improved error guarantees than the previous state-of-the-art. In addition, in the local model, our algorithm significantly reduces the number of interaction rounds. Although the problem has been widely studied in the context of differential privacy, all of the existing constructions achieve only super constant approximation factors.
The Limits of Post-Selection Generalization
Jonathan Ullman, Adam Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke
Neural Information Processing Systems http://nips.cc/
Differentially Private k-Means with Constant Multiplicative Error
Uri Stemmer, Haim Kaplan
Neural Information Processing Systems http://nips.cc/
Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta
With a typically large number of participants in local algorithms (n in the millions), this reduction in time complexity, in particular at the user side, is crucial for the use of such algorithms in practice. We implemented Algorithm TreeHist to verify our theoretical analysis and compared its performance with the performance of Google's RAPPOR code.