Search Results for author:
Found 6 papers, 3 papers with code
SOPE: Spectrum of Off-Policy Estimators
In this paper, we present a new perspective on this bias-variance trade-off and show the existence of a spectrum of estimators whose endpoints are SIS and IS.
Fairness Guarantees under Demographic Shift
Recent studies have demonstrated that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes.
Distributional Depth-Based Estimation of Object Articulation Models
Our core contributions include a novel representation for distributions over rigid body transformations and articulation model parameters based on screw theory, von Mises-Fisher distributions, and Stiefel manifolds.
Offline Contextual Bandits with High Probability Fairness Guarantees
We present RobinHood, an offline contextual bandit algorithm designed to satisfy a broad family of fairness constraints.
A Manifold Approach to Learning Mutually Orthogonal Subspaces
Although many machine learning algorithms involve learning subspaces with particular characteristics, optimizing a parameter matrix that is constrained to represent a subspace can be challenging.
Projected Natural Actor-Critic
Natural actor-critics are a popular class of policy search algorithms for finding locally optimal policies for Markov decision processes.
