Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics. In this context, each decision is taken by an expert who is typically chosen uniformly at random from a pool of experts. However, these decisions may be imperfect due to limited experience, implicit biases, or faulty probabilistic reasoning. Can we improve the accuracy and fairness of the overall decision making process by optimizing the assignment between experts and decisions? In this paper, we address the above problem from the perspective of sequential decision making and show that, for different fairness notions from the literature, it reduces to a sequence of (constrained) weighted bipartite matchings, which can be solved efficiently using algorithms with approximation guarantees. Moreover, these algorithms also benefit from posterior sampling to actively trade off exploitation---selecting expert assignments which lead to accurate and fair decisions---and exploration---selecting expert assignments to learn about the experts' preferences and biases. We demonstrate the effectiveness of our algorithms on both synthetic and real-world data and show that they can significantly improve both the accuracy and fairness of the decisions taken by pools of experts.

Several algorithms for solving constraint satisfaction problems are based on survey propagation, a variational inference scheme used to obtain approximate marginal probability estimates for variable assignments. These marginals correspond to how frequently each variable is set to true among satisfying assignments, and are used to inform branching decisions during search; however, marginal estimates obtained via survey propagation are approximate and can be self-contradictory. We introduce a more general branching strategy based on streamlining constraints, which sidestep hard assignments to variables. We show that streamlined solvers consistently outperform decimation-based solvers on random k-SAT instances for several problem sizes, shrinking the gap between empirical performance and theoretical limits of satisfiability by 16.3% on average for k = 3, 4, 5, 6.

The bird is a beautiful silver-gray, and as she dies twitching in the lasernet I'm grateful for two things: First, that she didn't make a sound. Second, that this will be the very last time. They're called corpse doves--because the darkest part of their gray plumage surrounds the lighter part, giving the impression that skeleton faces are peeking out from behind trash cans and bushes--and their crime is having the ability to carry diseases that would be compatible with humans. I open my hand, triggering the display from my imprinted handheld, and record an image to verify the elimination. A ding from my palm lets me know I've reached my quota for the day and, with that, the year. I'm tempted to give this one a send-off, a real burial with holy words and some flowers, but then I hear a pack of streetrats hooting beside me. My city-issued vest is reflective and nanopainted so it projects a slight glow. I don't know if it's to keep us safe like they say, or if it's just that so many of us are ex-cons working court-ordered labor, and civilians want to be able to keep an eye on us. Either way, everyone treats us like we're invisible--everyone except children.

The LDS model is built on the state-space model and assumes latent factors evolvewith linear dynamics. Ontheother hand, GPFAmodels thelatent vectors by non-parametric Gaussian processes.

Compared toprevious works, our approach can learn landmarks that are more flexible in terms of capturing large changes in viewpoint.

We demonstrate that supervised learning in the transductive model enjoys such structure.