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Found 46 papers, 16 papers with code
Two Simple Ways to Learn Individual Fairness Metric from Data
Individual fairness was proposed to address some of the shortcomings of group fairness.
Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets
Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes.
Aligners: Decoupling LLMs and Alignment
Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications.
tinyBenchmarks: evaluating LLMs with fewer examples
The versatility of large language models (LLMs) led to the creation of diverse benchmarks that thoroughly test a variety of language models' abilities.
Estimating Fréchet bounds for validating programmatic weak supervision
We develop methods for estimating Fr\'echet bounds on (possibly high-dimensional) distribution classes in which some variables are continuous-valued.
Fusing Models with Complementary Expertise
Training AI models that generalize across tasks and domains has long been among the open problems driving AI research.
An Investigation of Representation and Allocation Harms in Contrastive Learning
In this paper, we demonstrate that contrastive learning (CL), a popular variant of SSL, tends to collapse representations of minority groups with certain majority groups.
Large Language Model Routing with Benchmark Datasets
There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them.
Conditional independence testing under misspecified inductive biases
Conditional independence (CI) testing is a fundamental and challenging task in modern statistics and machine learning.
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing computational overhead, assuming a batch size smaller than the number of neurons.
Simple Disentanglement of Style and Content in Visual Representations
Learning visual representations with interpretable features, i. e., disentangled representations, remains a challenging problem.
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
We consider the task of training machine learning models with data-dependent constraints.
How does overparametrization affect performance on minority groups?
The benefits of overparameterization for the overall performance of modern machine learning (ML) models are well known.
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Time-varying stochastic optimization problems frequently arise in machine learning practice (e. g. gradual domain shift, object tracking, strategic classification).
Understanding new tasks through the lens of training data via exponential tilting
However, it is conceivable that the training data can be reweighted to be more representative of the new (target) task.
Domain Adaptation meets Individual Fairness. And they get along
In this paper, we leverage this connection between algorithmic fairness and distribution shifts to show that algorithmic fairness interventions can help ML models overcome distribution shifts, and that domain adaptation methods (for overcoming distribution shifts) can mitigate algorithmic biases.
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms
Sampling biases in training data are a major source of algorithmic biases in machine learning systems.
Post-processing for Individual Fairness
In this work, we propose general post-processing algorithms for individual fairness (IF).
On sensitivity of meta-learning to support data
Meta-learning algorithms are widely used for few-shot learning.
Individually Fair Gradient Boosting
Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern.
Statistical inference for individual fairness
As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e. g., gender and racial biases) has come to the fore of the public's attention.
Individually Fair Ranking
We develop an algorithm to train individually fair learning-to-rank (LTR) models.
Outlier Robust Optimal Transport
In light of recent advances in solving the OT problem, OT distances are widely used as loss functions in minimum distance estimation.
Individually Fair Rankings
We develop an algorithm to train individually fair learning-to-rank (LTR) models.
Does enforcing fairness mitigate biases caused by subpopulation shift?
Many instances of algorithmic bias are caused by subpopulation shifts.
There is no trade-off: enforcing fairness can improve accuracy
If the algorithmic biases in an ML model are due to sampling biases in the training data, then enforcing algorithmic fairness may improve the performance of the ML model on unbiased test data.
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
In this paper, we cast fair machine learning as invariant machine learning.
Two Simple Ways to Learn Individual Fairness Metrics from Data
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness.
Minimax optimal approaches to the label shift problem in non-parametric settings
We study the minimax rates of the label shift problem in non-parametric classification.
Auditing ML Models for Individual Bias and Unfairness
We consider the task of auditing ML models for individual bias/unfairness.
Federated Learning with Matched Averaging
Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store the data in the cloud.
Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions
We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical.
Training individually fair ML models with Sensitive Subspace Robustness
We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs.
Dirichlet Simplex Nest and Geometric Inference
We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting for the model's convex geometry and low dimensional simplicial structure.
Precision Matrix Estimation with Noisy and Missing Data
Estimating conditional dependence graphs and precision matrices are some of the most common problems in modern statistics and machine learning.
Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0
This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios.
An inexact subsampled proximal Newton-type method for large-scale machine learning
We propose a fast proximal Newton-type algorithm for minimizing regularized finite sums that returns an $\epsilon$-suboptimal point in $\tilde{\mathcal{O}}(d(n + \sqrt{\kappa d})\log(\frac{1}{\epsilon}))$ FLOPS, where $n$ is number of samples, $d$ is feature dimension, and $\kappa$ is the condition number.
On conditional parity as a notion of non-discrimination in machine learning
We identify conditional parity as a general notion of non-discrimination in machine learning.
Feature-distributed sparse regression: a screen-and-clean approach
Most existing approaches to distributed sparse regression assume the data is partitioned by samples.
Evaluating the statistical significance of biclusters
Biclustering (also known as submatrix localization) is a problem of high practical relevance in exploratory analysis of high-dimensional data.
Communication-efficient sparse regression: a one-shot approach
We devise a one-shot approach to distributed sparse regression in the high-dimensional setting.
On model selection consistency of penalized M-estimators: a geometric theory
Penalized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure.
Exact post-selection inference, with application to the lasso
We develop a general approach to valid inference after model selection.
Learning Mixtures of Linear Classifiers
We consider a discriminative learning (regression) problem, whereby the regression function is a convex combination of k linear classifiers.
On model selection consistency of regularized M-estimators
Regularized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure.
Proximal Newton-type methods for minimizing composite functions
We generalize Newton-type methods for minimizing smooth functions to handle a sum of two convex functions: a smooth function and a nonsmooth function with a simple proximal mapping.
