Multicalibrated Regression for Downstream Fairness

• URL: http://arxiv.org/abs/2209.07312v1
• Date: Thu, 15 Sep 2022 14:16:01 GMT
• Title: Multicalibrated Regression for Downstream Fairness
• Authors: Ira Globus-Harris and Varun Gupta and Christopher Jung and Michael Kearns and Jamie Morgenstern and Aaron Roth
• Abstract summary: We show how to take a regression function $hatf$ that is appropriately multicalibrated'' and efficiently post-process it. The post-processing requires no labeled data, and only a modest amount of unlabeled data and computation.
• Score: 17.084765209458762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints. The post-processing requires no labeled data, and only a modest amount of unlabeled data and computation. The computational and sample complexity requirements of computing $\hat f$ are comparable to the requirements for solving a single fair learning task optimally, but it can in fact be used to solve many different downstream fairness-constrained learning problems efficiently. Our post-processing method easily handles intersecting groups, generalizing prior work on post-processing regression functions to satisfy fairness constraints that only applied to disjoint groups. Our work extends recent work showing that multicalibrated regression functions are ``omnipredictors'' (i.e. can be post-processed to optimally solve unconstrained ERM problems) to constrained optimization.

Related papers

• Regression under demographic parity constraints via unlabeled post-processing [5.762345156477737]
  We present a general-purpose post-processing algorithm that generates predictions that meet the demographic parity. Unlike prior methods, our approach is fully theory-driven. We require precise control over the gradient norm of the convex function. Our algorithm is backed by finite-sample analysis and post-processing bounds, with experimental results validating our theoretical findings.
  arXiv  Detail & Related papers  (2024-07-22T08:11:58Z)
• Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data [17.657917523817243]
  We develop and analyze algorithms for instrumental variable regression by viewing the problem as a conditional optimization problem. In the context of least-squares instrumental variable regression, our algorithms neither require matrix inversions nor mini-batches. We derive rates of convergence in expectation, that are of order $mathcalO(log T/T)$ and $mathcalO (1/T1-iota)$ for any $iota>0$.
  arXiv  Detail & Related papers  (2024-05-29T19:21:55Z)
• How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? [92.90857135952231]
  Transformers pretrained on diverse tasks exhibit remarkable in-context learning (ICL) capabilities. We study ICL in one of its simplest setups: pretraining a linearly parameterized single-layer linear attention model for linear regression.
  arXiv  Detail & Related papers  (2023-10-12T15:01:43Z)
• Smoothly Giving up: Robustness for Simple Models [30.56684535186692]
  Examples of algorithms to train such models include logistic regression and boosting. We use $Served-Served joint convex loss functions, which tunes between canonical convex loss functions, to robustly train such models. We also provide results for boosting a COVID-19 dataset for logistic regression, highlighting the efficacy approach across multiple relevant domains.
  arXiv  Detail & Related papers  (2023-02-17T19:48:11Z)
• Online Sub-Sampling for Reinforcement Learning with General Function Approximation [111.01990889581243]
  In this paper, we establish an efficient online sub-sampling framework that measures the information gain of data points collected by an RL algorithm. For a value-based method with complexity-bounded function class, we show that the policy only needs to be updated for $proptooperatornamepolylog(K)$ times. In contrast to existing approaches that update the policy for at least $Omega(K)$ times, our approach drastically reduces the number of optimization calls in solving for a policy.
  arXiv  Detail & Related papers  (2021-06-14T07:36:25Z)
• Examining and Combating Spurious Features under Distribution Shift [94.31956965507085]
  We define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics. We prove that even when there is only bias of the input distribution, models can still pick up spurious features from their training data. Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations.
  arXiv  Detail & Related papers  (2021-06-14T05:39:09Z)
• Solving weakly supervised regression problem using low-rank manifold regularization [77.34726150561087]
  We solve a weakly supervised regression problem. Under "weakly" we understand that for some training points the labels are known, for some unknown, and for others uncertain due to the presence of random noise or other reasons such as lack of resources. In the numerical section, we applied the suggested method to artificial and real datasets using Monte-Carlo modeling.
  arXiv  Detail & Related papers  (2021-04-13T23:21:01Z)
• A Hypergradient Approach to Robust Regression without Correspondence [85.49775273716503]
  We consider a variant of regression problem, where the correspondence between input and output data is not available. Most existing methods are only applicable when the sample size is small. We propose a new computational framework -- ROBOT -- for the shuffled regression problem.
  arXiv  Detail & Related papers  (2020-11-30T21:47:38Z)
• Active Sampling for Min-Max Fairness [28.420886416425077]
  We propose simple active sampling and reweighting strategies for optimizing min-max fairness. The ease of implementation and the generality of our robust formulation make it an attractive option for improving model performance on disadvantaged groups. For convex learning problems, such as linear or logistic regression, we provide a fine-grained analysis, proving the rate of convergence to a min-max fair solution.
  arXiv  Detail & Related papers  (2020-06-11T23:57:55Z)
• Non-Adaptive Adaptive Sampling on Turnstile Streams [57.619901304728366]
  We give the first relative-error algorithms for column subset selection, subspace approximation, projective clustering, and volume on turnstile streams that use space sublinear in $n$. Our adaptive sampling procedure has a number of applications to various data summarization problems that either improve state-of-the-art or have only been previously studied in the more relaxed row-arrival model.
  arXiv  Detail & Related papers  (2020-04-23T05:00:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.