Search Results for author:
Found 10 papers, 4 papers with code
Adversarial Multiclass Classification: A Risk Minimization Perspective
Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses.
Adversarial Surrogate Losses for Ordinal Regression
Ordinal regression seeks class label predictions when the penalty incurred for mistakes increases according to an ordering over the labels.
Kernel Robust Bias-Aware Prediction under Covariate Shift
Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase logarithmic loss for the target distribution while matching feature expectation constraints from the source distribution.
Efficient and Consistent Adversarial Bipartite Matching
Many important structured prediction problems, including learning to rank items, correspondence-based natural language processing, and multi-object tracking, can be formulated as weighted bipartite matching optimizations.
Distributionally Robust Graphical Models
Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency.
Consistent Robust Adversarial Prediction for General Multiclass Classification
We propose a robust adversarial prediction framework for general multiclass classification.
Fairness for Robust Log Loss Classification
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications.
AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning
We propose a method that enables practitioners to conveniently incorporate custom non-decomposable performance metrics into differentiable learning pipelines, notably those based upon neural network architectures.
Multiplicative Filter Networks
Although deep networks are typically used to approximate functions over high dimensional inputs, recent work has increased interest in neural networks as function approximators for low-dimensional-but-complex functions, such as representing images as a function of pixel coordinates, solving differential equations, or representing signed distance fields or neural radiance fields.
Fairness for Robust Learning to Rank
While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for different protected attributes such as gender or race.
