Search Results for author:
Found 14 papers, 3 papers with code
Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
We establish an equivalence between swap variants of omniprediction and multicalibration and swap agnostic learning.
Loss Minimization through the Lens of Outcome Indistinguishability
This decomposition highlights the utility of a new multi-group fairness notion that we call calibrated multiaccuracy, which lies in between multiaccuracy and multicalibration.
Making Decisions under Outcome Performativity
This performative prediction setting raises new challenges for learning "optimal" decision rules.
Is your model predicting the past?
When does a machine learning model predict the future of individuals and when does it recite patterns that predate the individuals?
Planting Undetectable Backdoors in Machine Learning Models
Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm or in Random ReLU networks.
Low-Degree Multicalibration
This stringent notion -- that predictions be well-calibrated across a rich class of intersecting subpopulations -- provides its strong guarantees at a cost: the computational and sample complexity of learning multicalibrated predictors are high, and grow exponentially with the number of class labels.
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
In this work, we introduce a new notion -- \emph{decision calibration} -- that requires the predicted distribution and true distribution to be ``indistinguishable'' to a set of downstream decision-makers.
Outcome Indistinguishability
Prediction algorithms assign numbers to individuals that are popularly understood as individual "probabilities" -- what is the probability of 5-year survival after cancer diagnosis?
A Distributional Framework for Data Valuation
Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data points when training machine learning models.
Tracking and Improving Information in the Service of Fairness
As algorithmic prediction systems have become widespread, fears that these systems may inadvertently discriminate against members of underrepresented populations have grown.
Preference-Informed Fairness
We introduce and study a new notion of preference-informed individual fairness (PIIF) that is a relaxation of both individual fairness and envy-freeness.
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
Prediction systems are successfully deployed in applications ranging from disease diagnosis, to predicting credit worthiness, to image recognition.
Fairness Through Computationally-Bounded Awareness
We study the problem of fair classification within the versatile framework of Dwork et al. [ITCS '12], which assumes the existence of a metric that measures similarity between pairs of individuals.
Calibration for the (Computationally-Identifiable) Masses
We develop and study multicalbration -- a new measure of algorithmic fairness that aims to mitigate concerns about discrimination that is introduced in the process of learning a predictor from data.
