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Found 6 papers, 3 papers with code
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
TGNet learns an autoregressive model, conditioned on temporal contexts of forecasting targets from temporal-guided embedding.
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem.
Constrained Interacting Submodular Groupings
We introduce the problem of grouping a finite ground set into blocks where each block is a subset of the ground set and where: (i) the blocks are individually highly valued by a submodular function (both robustly and in the average case) while satisfying block-specific matroid constraints; and (ii) block scores interact where blocks are jointly scored highly, thus making the blocks mutually non-redundant.
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals.
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization
Given a classifier ensemble and a set of examples to be classified, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble are evaluated.
Deep Lattice Networks and Partial Monotonic Functions
We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network.
