Finding Optimally Robust Data Mixtures via Concave Maximization

• URL: http://arxiv.org/abs/2406.01477v1
• Date: Mon, 3 Jun 2024 16:06:12 GMT
• Title: Finding Optimally Robust Data Mixtures via Concave Maximization
• Authors: Anvith Thudi, Chris J. Maddison,
• Abstract summary: Group distribution optimization (group DRO) is one popular way to learn variations of the performance of non-Income models. We show that a method we call MixMax selects a particular mixture with entropic ascent, and, crucially, we prove that optimally fitting this distribution over the set of bounded weights returns a group DRO optimal model.
• Score: 18.144960432059634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training on mixtures of data distributions is now common in many modern machine learning pipelines, useful for performing well on several downstream tasks. Group distributionally robust optimization (group DRO) is one popular way to learn mixture weights for training a specific model class, but group DRO methods suffer for non-linear models due to non-convex loss functions and when the models are non-parametric. We address these challenges by proposing to solve a more general DRO problem, giving a method we call MixMax. MixMax selects mixture weights by maximizing a particular concave objective with entropic mirror ascent, and, crucially, we prove that optimally fitting this mixture distribution over the set of bounded predictors returns a group DRO optimal model. Experimentally, we tested MixMax on a sequence modeling task with transformers and on a variety of non-parametric learning problems. In all instances MixMax matched or outperformed the standard data mixing and group DRO baselines, and in particular, MixMax improved the performance of XGBoost over the only baseline, data balancing, for variations of the ACSIncome and CelebA annotations datasets.

Related papers

• Convex Clustering [4.577104493960515]
  convex clustering has several uncommon features that distinguish it from prior art.<n>Its unique global minimizer is stable with respect to all its inputs.<n>We highlight important algorithms and give insight into how their computational costs scale with the problem size.
  arXiv  Detail & Related papers  (2025-07-11T23:38:15Z)
• A Primal-Dual Algorithm for Faster Distributionally Robust Optimization [12.311794669976047]
  We present Drago, a primal-dual algorithm that achieves a state-of-the-art linear convergence rate on strongly convex-strongly concave DRO problems. We support our theoretical results with numerical benchmarks in classification and regression.
  arXiv  Detail & Related papers  (2024-03-16T02:06:14Z)
• Fast Semisupervised Unmixing Using Nonconvex Optimization [80.11512905623417]
  We introduce a novel convex convex model for semi/library-based unmixing. We demonstrate the efficacy of Alternating Methods of sparse unsupervised unmixing.
  arXiv  Detail & Related papers  (2024-01-23T10:07:41Z)
• A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization [53.14532968909759]
  We introduce an efficient single-loop primal-dual block-coordinate algorithm called ALEXR. We establish the convergence rates of ALEXR in both convex and strongly convex cases under smoothness and non-smoothness conditions. Experimental results on GDRO and partial Area Under the ROC Curve for cFCCO demonstrate the promising performance of our algorithm.
  arXiv  Detail & Related papers  (2023-12-04T19:00:07Z)
• Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization [61.39201891894024]
  Group distributionally robust optimization (group DRO) can minimize the worst-case loss over pre-defined groups. We reformulate the group DRO framework by proposing Q-Diversity. Characterized by an interactive training mode, Q-Diversity relaxes the group identification from annotation into direct parameterization.
  arXiv  Detail & Related papers  (2023-05-20T07:02:27Z)
• Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework [18.6306170209029]
  Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approaches for solving optimization problems. We propose a simple approach in which the distribution of random perturbations is approximated using a parametric family of distributions. We show that this new source of error can be controlled by suitable DRO formulations.
  arXiv  Detail & Related papers  (2022-12-03T03:26:34Z)
• Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis [16.499651513178012]
  Distributionally robust optimization (DRO) is a widely-used approach to learn models that are robust against distribution shift. We provide non-asymptotic convergence guarantees even though the objective function is possibly prominent nonsmooth- and has normalized gradient descent.
  arXiv  Detail & Related papers  (2021-10-24T14:56:38Z)
• Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation [71.27989298860481]
  We address the non- optimisation problem of finding a matrix on the Stiefel manifold that maximises a quadratic objective function. We propose a simple yet effective sparsity-promoting algorithm for finding the dominant eigenspace matrix.
  arXiv  Detail & Related papers  (2021-09-30T19:17:35Z)
• 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)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)

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.