Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised Learning

• URL: http://arxiv.org/abs/2303.15866v2
• Date: Wed, 26 Apr 2023 11:36:47 GMT
• Title: Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised Learning
• Authors: Teng-Hui Huang and Hesham El Gamal
• Abstract summary: We propose two novel formulations that enable the development of computational efficient solvers based the alternating principle. The proposed solvers offer computational efficiency, theoretical convergence guarantees, local minima complexity with the number of views, and exceptional accuracy as compared with the state-of-the-art techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we adopt Wyner common information framework for unsupervised multi-view representation learning. Within this framework, we propose two novel formulations that enable the development of computational efficient solvers based on the alternating minimization principle. The first formulation, referred to as the {\em variational form}, enjoys a linearly growing complexity with the number of views and is based on a variational-inference tight surrogate bound coupled with a Lagrangian optimization objective function. The second formulation, i.e., the {\em representational form}, is shown to include known results as special cases. Here, we develop a tailored version from the alternating direction method of multipliers (ADMM) algorithm for solving the resulting non-convex optimization problem. In the two cases, the convergence of the proposed solvers is established in certain relevant regimes. Furthermore, our empirical results demonstrate the effectiveness of the proposed methods as compared with the state-of-the-art solvers. In a nutshell, the proposed solvers offer computational efficiency, theoretical convergence guarantees (local minima), scalable complexity with the number of views, and exceptional accuracy as compared with the state-of-the-art techniques. Our focus here is devoted to the discrete case and our results for continuous distributions are reported elsewhere.

Related papers

• On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning [85.75164588939185]
  We study the discriminative probabilistic modeling problem on a continuous domain for (multimodal) self-supervised representation learning. We conduct generalization error analysis to reveal the limitation of current InfoNCE-based contrastive loss for self-supervised representation learning.
  arXiv  Detail & Related papers  (2024-10-11T18:02:46Z)
• Efficient Fairness-Performance Pareto Front Computation [51.558848491038916]
  We show that optimal fair representations possess several useful structural properties. We then show that these approxing problems can be solved efficiently via concave programming methods.
  arXiv  Detail & Related papers  (2024-09-26T08:46:48Z)
• A Double Tracking Method for Optimization with Decentralized Generalized Orthogonality Constraints [4.6796315389639815]
  Decentralized optimization problems are unsolvable in the presence of distributed constraints. We introduce a novel algorithm that tracks the gradient of the objective function and the Jacobian of the constraint mapping simultaneously. We present numerical results on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-09-08T06:57:35Z)
• Alternating Minimization Schemes for Computing Rate-Distortion-Perception Functions with $f$-Divergence Perception Constraints [10.564071872770146]
  We study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources. We characterize the optimal parametric solutions. We provide sufficient conditions on the distortion and the perception constraints.
  arXiv  Detail & Related papers  (2024-08-27T12:50:12Z)
• Learning Constrained Optimization with Deep Augmented Lagrangian Methods [54.22290715244502]
  A machine learning (ML) model is trained to emulate a constrained optimization solver. This paper proposes an alternative approach, in which the ML model is trained to predict dual solution estimates directly. It enables an end-to-end training scheme is which the dual objective is as a loss function, and solution estimates toward primal feasibility, emulating a Dual Ascent method.
  arXiv  Detail & Related papers  (2024-03-06T04:43:22Z)
• Sample-Efficient Multi-Agent RL: An Optimization Perspective [103.35353196535544]
  We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. We introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. We show that our algorithm provides comparable sublinear regret to the existing works.
  arXiv  Detail & Related papers  (2023-10-10T01:39:04Z)
• An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees [71.57324258813675]
  We propose a novel methodology for addressing the hyperspectral image deconvolution problem. A new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network. The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium framework.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• Linearization Algorithms for Fully Composite Optimization [61.20539085730636]
  This paper studies first-order algorithms for solving fully composite optimization problems convex compact sets. We leverage the structure of the objective by handling differentiable and non-differentiable separately, linearizing only the smooth parts.
  arXiv  Detail & Related papers  (2023-02-24T18:41:48Z)
• SCORE: Approximating Curvature Information under Self-Concordant Regularization [0.0]
  We propose a generalized Gauss-Newton with Self-Concordant Regularization (GGN-SCORE) algorithm that updates the minimization speed each time it receives a new input. The proposed algorithm exploits the structure of the second-order information in the Hessian matrix, thereby reducing computational overhead.
  arXiv  Detail & Related papers  (2021-12-14T13:03:04Z)
• Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems [2.335152769484957]
  We derive and detail a theoretical analysis of an efficient consensus algorithm based on gradient proximal (PG) approach. The proposed algorithm is also applied to another particular convolutional problem for the anomaly detection task.
  arXiv  Detail & Related papers  (2020-11-19T20:52:48Z)
• Adaptive Discretization for Model-Based Reinforcement Learning [10.21634042036049]
  We introduce the technique of adaptive discretization to design an efficient model-based episodic reinforcement learning algorithm. Our algorithm is based on optimistic one-step value iteration extended to maintain an adaptive discretization of the space.
  arXiv  Detail & Related papers  (2020-07-01T19:36:46Z)

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.