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Found 31 papers, 8 papers with code
Multidimensional Shape Constraints
We propose new multi-input shape constraints across four intuitive categories: complements, diminishers, dominance, and unimodality constraints.
Global Optimization Networks
We consider the problem of estimating a good maximizer of a black-box function given noisy examples.
Regularization Strategies for Quantile Regression
We show that minimizing an expected pinball loss over a continuous distribution of quantiles is a good regularizer even when only predicting a specific quantile.
Understanding Memory B Cell Selection
However, memory B cells appear to be purposely selected earlier in the affinity maturation process and have lower affinity.
Deep k-NN for Noisy Labels
Modern machine learning models are often trained on examples with noisy labels that hurt performance and are hard to identify.
Robust Optimization for Fairness with Noisy Protected Groups
Second, we introduce two new approaches using robust optimization that, unlike the naive approach of only relying on $\hat{G}$, are guaranteed to satisfy fairness criteria on the true protected groups G while minimizing a training objective.
Optimizing Black-box Metrics with Adaptive Surrogates
We address the problem of training models with black-box and hard-to-optimize metrics by expressing the metric as a monotonic function of a small number of easy-to-optimize surrogates.
Deontological Ethics By Monotonicity Shape Constraints
We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes."
Optimizing Generalized Rate Metrics with Three Players
We present a general framework for solving a large class of learning problems with non-linear functions of classification rates.
On Making Stochastic Classifiers Deterministic
Stochastic classifiers arise in a number of machine learning problems, and have become especially prominent of late, as they often result from constrained optimization problems, e. g. for fairness, churn, or custom losses.
Optimizing Generalized Rate Metrics through Game Equilibrium
We present a general framework for solving a large class of learning problems with non-linear functions of classification rates.
Pairwise Fairness for Ranking and Regression
We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity.
Minimum-Margin Active Learning
We present a new active sampling method we call min-margin which trains multiple learners on bootstrap samples and then chooses the examples to label based on the candidates' minimum margin amongst the bootstrapped models.
Diminishing Returns Shape Constraints for Interpretability and Regularization
We investigate machine learning models that can provide diminishing returns and accelerating returns guarantees to capture prior knowledge or policies about how outputs should depend on inputs.
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.
Proxy Fairness
We consider the problem of improving fairness when one lacks access to a dataset labeled with protected groups, making it difficult to take advantage of strategies that can improve fairness but require protected group labels, either at training or runtime.
Interpretable Set Functions
We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label.
To Trust Or Not To Trust A Classifier
Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI.
Metric-Optimized Example Weights
Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed.
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.
Launch and Iterate: Reducing Prediction Churn
Practical applications of machine learning often involve successive training iterations with changes to features and training examples.
Fast and Flexible Monotonic Functions with Ensembles of Lattices
For many machine learning problems, there are some inputs that are known to be positively (or negatively) related to the output, and in such cases training the model to respect that monotonic relationship can provide regularization, and makes the model more interpretable.
Satisfying Real-world Goals with Dataset Constraints
The goal of minimizing misclassification error on a training set is often just one of several real-world goals that might be defined on different datasets.
A Light Touch for Heavily Constrained SGD
Minimizing empirical risk subject to a set of constraints can be a useful strategy for learning restricted classes of functions, such as monotonic functions, submodular functions, classifiers that guarantee a certain class label for some subset of examples, etc.
Monotonic Calibrated Interpolated Look-Up Tables
Real-world machine learning applications may require functions that are fast-to-evaluate and interpretable.
Multi-Task Averaging
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets.
Shadow Dirichlet for Restricted Probability Modeling
Although the Dirichlet distribution is widely used, the independence structure of its components limits its accuracy as a model.
Lattice Regression
We present a new empirical risk minimization framework for approximating functions from training samples for low-dimensional regression applications where a lattice (look-up table) is stored and interpolated at run-time for an efficient hardware implementation.
