22 citations · 65 across the 9 of their papers we have counts for
6 papers · 1 filter
Optimizing Black-box Metrics with Iterative Example Weighting
Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan +2
We consider learning to optimize a classification metric defined by a black-box function of the confusion matrix. Such black-box learning settings are ubiquitous, for example, when…
Optimizing Black-box Metrics with Adaptive Surrogates
Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan +2
We address the problem of training models with black-box and hard-to-optimize metrics by expressing the metric as a monotonic function of a small number of easy-to-optimize surroga…
Proxy Fairness
Maya Gupta, Andrew Cotter, Mahdi Milani Fard +1
We consider the problem of improving fairness when one lacks access to a dataset labeled with protected groups, making it difficult to take advantage of strategies that can improve…
Efficient Learning and Planning with Compressed Predictive States
William L. Hamilton, Mahdi Milani Fard, Joelle Pineau
Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quant…
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces
Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand +2
We address the problem of automatic generation of features for value function approximation. Bellman Error Basis Functions (BEBFs) have been shown to improve the error of policy ev…
PAC-Bayesian Policy Evaluation for Reinforcement Learning
Mahdi MIlani Fard, Joelle Pineau, Csaba Szepesvari
Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, large…