227 citations · 239 across the 19 of their papers we have counts for
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cs.LG2018
Multi Instance Learning For Unbalanced Data
Mark Kozdoba, Edward Moroshko, Lior Shani +4
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is suffici…
math.ST2018★ 8 cited
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters
Mark Kozdoba, Jakub Marecek, Tigran Tchrakian +1
Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever t…
stat.ML2018
Interdependent Gibbs Samplers
Mark Kozdoba, Shie Mannor
Gibbs sampling, as a model learning method, is known to produce the most accurate results available in a variety of domains, and is a de facto standard in these domains. Yet, it is…