Search Results for author:
Found 22 papers, 9 papers with code
Forward and Reverse Gradient-Based Hyperparameter Optimization
We study two procedures (reverse-mode and forward-mode) for computing the gradient of the validation error with respect to the hyperparameters of any iterative learning algorithm such as stochastic gradient descent.
A Bridge Between Hyperparameter Optimization and Learning-to-learn
We consider a class of a nested optimization problems involving inner and outer objectives.
Empirical Risk Minimization under Fairness Constraints
It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable.
Taking Advantage of Multitask Learning for Fair Classification
In this paper we show how it is possible to get the best of both worlds: optimize model accuracy and fairness without explicitly using the sensitive feature in the functional form of the model, thereby treating different individuals equally.
General Fair Empirical Risk Minimization
We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible continuous sensitive attributes.
Learning Fair and Transferable Representations
Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness.
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources.
Scheduling the Learning Rate Via Hypergradients: New Insights and a New Algorithm
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization.
MARTHE: Scheduling the Learning Rate Via Online Hypergradients
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization.
Fair Bayesian Optimization
Moreover, our method can be used in synergy with such specialized fairness techniques to tune their hyperparameters.
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints.
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
To democratize access to machine learning systems, it is essential to automate the tuning.
On the Lack of Robust Interpretability of Neural Text Classifiers
With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models.
Multi-objective Asynchronous Successive Halving
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions.
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models
With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment has become crucial.
More Than Words: Towards Better Quality Interpretations of Text Classifiers
The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of trust by the users.
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
In this work we propose a model-agnostic algorithm that generates counterfactual ensemble explanations for time series anomaly detection models.
Fortuna: A Library for Uncertainty Quantification in Deep Learning
We present Fortuna, an open-source library for uncertainty quantification in deep learning.
Efficient fair PCA for fair representation learning
We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic information.
Geographical Erasure in Language Generation
Large language models (LLMs) encode vast amounts of world knowledge.
Explaining Probabilistic Models with Distributional Values
We argue that often there is a critical mismatch between what one wishes to explain (e. g. the output of a classifier) and what current methods such as SHAP explain (e. g. the scalar probability of a class).
