95 citations · 273 across the 34 of their papers we have counts for
10 papers · 1 filter
Integral Probability Metrics PAC-Bayes Bounds
Ron Amit, Baruch Epstein, Shay Moran +1
We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). We provide instances of t…
Enhancing Causal Estimation through Unlabeled Offline Data
Ron Teichner, Ron Meir, Danny Eitan
Consider a situation where a new patient arrives in the Intensive Care Unit (ICU) and is monitored by multiple sensors. We wish to assess relevant unmeasured physiological variable…
Metalearning Linear Bandits by Prior Update
Amit Peleg, Naama Pearl, Ron Meir
Fully Bayesian approaches to sequential decision-making assume that problem parameters are generated from a known prior. In practice, such information is often lacking. This proble…
Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders
Baruch Epstein, Ron Meir
Autoencoders are widely used for unsupervised learning and as a regularization scheme in semi-supervised learning. However, theoretical understanding of their generalization proper…
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Ron Amit, Ron Meir
In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning of novel future tasks. Under the assumption that future tasks are 'related' to previ…
Joint auto-encoders: a flexible multi-task learning framework
Baruch Epstein, Ron Meir, Tomer Michaeli
The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availab…
Optimal Encoding and Decoding for Point Process Observations: an Approximate Closed-Form Filter
Yuval Harel, Ron Meir, Manfred Opper
The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but l…
An Analytically Tractable Bayesian Approximation to Optimal Point Process Filtering
Yuval Harel, Ron Meir, Manfred Opper
The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but l…
Optimal Population Codes for Control and Estimation
Alex Susemihl, Ron Meir, Manfred Opper
Agents acting in the natural world aim at selecting appropriate actions based on noisy and partial sensory observations. Many behaviors leading to decision mak- ing and action sele…
Mean Field Bayes Backpropagation: scalable training of multilayer neural networks with binary weights
Daniel Soudry, Ron Meir
Significant success has been reported recently using deep neural networks for classification. Such large networks can be computationally intensive, even after training is over. Imp…