Regret Circuits: Composability of Regret Minimizers

Authors: Gabriele Farina, Christian Kroer, Tuomas Sandholm

ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We derive a calculus for constructing regret minimizers for composite convex sets that are obtained from convexity-preserving operations on simpler convex sets. We show that local regret minimizers for the simpler sets can be combined with additional regret minimizers into an aggregate regret minimizer for the composite set. As one application, we show that the CFR framework can be constructed easily from our framework. We also show ways to include curtailing (constraining) operations into our framework.
Researcher Affiliation Collaboration 1Computer Science Department, Carnegie Mellon University, Pittsburgh PA 15213 2IEOR Department, Columbia University, New York NY 10027 3Strategic Machine, Inc. 4Strategy Robot, Inc. 5Optimized Markets, Inc.
Pseudocode No The paper uses 'Regret Circuits' represented pictorially through block diagrams (e.g., Figure 1, Figure 2, Figure 3, Figure 4, Figure 7) to illustrate compositions. These are conceptual diagrams, not structured pseudocode or algorithm blocks.
Open Source Code No The paper does not contain any statements about releasing open-source code, nor does it provide links to any code repositories or supplementary materials for the described methodology.
Open Datasets No The paper is theoretical and focuses on mathematical derivations and frameworks; it does not involve training models on datasets, and therefore no dataset information for training is provided.
Dataset Splits No The paper is theoretical and does not involve experimental validation on datasets with specific splits for training, validation, or testing.
Hardware Specification No The paper is purely theoretical and does not involve running experiments that would require specific hardware. Therefore, no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and does not describe empirical experiments that would require specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and focuses on mathematical derivations and conceptual frameworks. It does not describe any empirical experimental setup, hyperparameters, or training configurations.