Search Results for author:
Found 7 papers, 2 papers with code
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
Eventually, we analyze the learning behavior of the peers and observe their ability to rank the agents' performance within the study group and understand which agents give reliable advice.
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents.
Invariant Representations with Stochastically Quantized Neural Networks
In this paper, we propose a methodology for direct computation of the mutual information between a neural layer and a sensitive attribute.
Fair Interpretable Representation Learning with Correction Vectors
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information.
Fair Interpretable Learning via Correction Vectors
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information.
Partitioned Least Squares
In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables that they require to contribute similarly to the final result.
