Search Results for author:
Found 18 papers, 3 papers with code
Oracle Efficient Online Multicalibration and Omniprediction
We develop a new online multicalibration algorithm that is well defined for infinite benchmark classes $F$, and is oracle efficient (i. e. for any class $F$, the algorithm has the form of an efficient reduction to a no-regret learning algorithm for $F$).
Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliance
While explainability is a desirable characteristic of increasingly complex black-box models, modern explanation methods have been shown to be inconsistent and contradictory.
Generative Models of Huge Objects
In this we extend the work of Goldreich, Goldwasser and Nussboim (SICOMP 2010) that focused on the implementation of huge objects that are indistinguishable from the uniform distribution, satisfying some global properties (which they coined truthfulness).
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.
Omnipredictors for Constrained Optimization
In this paper, we introduce omnipredictors for constrained optimization and study their complexity and implications.
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability
In this setting, we show that the sample complexity of outcome indistinguishability is characterized by the fat-shattering dimension of $D$.
KL Divergence Estimation with Multi-group Attribution
Estimating the Kullback-Leibler (KL) divergence between two distributions given samples from them is well-studied in machine learning and information theory.
Omnipredictors
We suggest a rigorous new paradigm for loss minimization in machine learning where the loss function can be ignored at the time of learning and only be taken into account when deciding an action.
Multicalibrated Partitions for Importance Weights
We significantly strengthen previous work that use the MaxEntropy approach, that define the importance weights based on a distribution $Q$ closest to $P$, that looks the same as $R$ on every set $C \in \mathcal{C}$, where $\mathcal{C}$ may be a huge collection of sets.
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?
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers
We study the problem of robustly estimating the mean of a $d$-dimensional distribution given $N$ examples, where most coordinates of every example may be missing and $\varepsilon N$ examples may be arbitrarily corrupted.
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.
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
We develop and study multicalibration as a new measure of fairness in machine learning that aims to mitigate inadvertent or malicious discrimination that is introduced at training time (even from ground truth data).
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.
Generalization in Adaptive Data Analysis and Holdout Reuse
We also formalize and address the general problem of data reuse in adaptive data analysis.
Preserving Statistical Validity in Adaptive Data Analysis
We show that, surprisingly, there is a way to estimate an exponential in $n$ number of expectations accurately even if the functions are chosen adaptively.
