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Found 6 papers, 2 papers with code
High-Dimensional Prediction for Sequential Decision Making
We study the problem of making predictions of an adversarially chosen high-dimensional state that are unbiased subject to an arbitrary collection of conditioning events, with the goal of tailoring these events to downstream decision makers.
The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation
To further counter-weigh our negative result, we show that if a property $\Gamma^1$ is not elicitable by itself, but is elicitable conditionally on another elicitable property $\Gamma^0$, then there is a canonical algorithm that jointly multicalibrates $\Gamma^1$ and $\Gamma^0$; this generalizes past work on mean-moment multicalibration.
Batch Multivalid Conformal Prediction
Multivalid coverage guarantees are stronger than marginal coverage guarantees in two ways: (1) They hold even conditional on group membership -- that is, the target coverage level $1-\alpha$ holds conditionally on membership in each of an arbitrary (potentially intersecting) group in a finite collection $\mathcal{G}$ of regions in the feature space.
Practical Adversarial Multivalid Conformal Prediction
It is computationally lightweight -- comparable to split conformal prediction -- but does not require having a held-out validation set, and so all data can be used for training models from which to derive a conformal score.
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications
We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round.
Online Multivalid Learning: Means, Moments, and Prediction Intervals
We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples $(x, y)$.
