arXiv research

S3C2 uses Siamese networks for semi-supervised clustering with pairwise constraints.

Constrained clustering has been well-studied for algorithms such as $K$-means and hierarchical clustering. However, how to satisfy many constraints in these algorithmic settings has been shown to be intractable. One alternative to encode many constraints is to use spectral clustering, which remains a developing area. I…

An important form of prior information in clustering comes in form of cannot-link and must-link constraints. We present a generalization of the popular spectral clustering technique which integrates such constraints. Motivated by the recently proposed $1$-spectral clustering for the unconstrained problem, our method is…

New algorithm clusters data and learns kernels without relaxing constraints.

Proposes methods to find alternative blockmodels in networks.

We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items. Such constraints are meant to reflect side-information that is not expressed directly in the feature vectors of the data items. The relative-distance constraints used in this work are part…

Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorithm based on the information-maximization principle. The proposed method is an extension of a previous unsupervised information-maximization …

It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping. However, such approaches typically do not leverage available domain knowledge while extracting the phenotypes; hence, some of the su…

Unified clustering model handles both pairwise and cardinality constraints for better performance.

New method for MAP inference using Benders' decomposition.

Proposes a method to improve hierarchical clustering using set-level structural priors.

Under certain topological assumptions, we show that two monotone Lagrangian submanifolds embedded in the standard symplectic vector space with the same monotonicity constant cannot link one another and that, individually, their smooth knot type is determined entirely by the homotopy theoretic data which classifies the …

A new algorithm tackles submodular bandit problems with multiple constraints.

This work proposes an online learning approach to tighten constraints in stochastic control problems.

Simplifies neural network constraints with computationally efficient method.

Reduces Lie (bi-)algebroids and Dirac manifolds using constraint vector bundles.

Optimistic algorithm reduces regret and constraint violations in online convex optimization with adversarial constraints.

Holistic GLMs add constraints for better model quality.

A new ML method teaches constraints directly to models.

Paper tackles constrained bandit problems with a new learning framework.

In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is i…

Survey of Gaussian process constraints for modeling expensive data.

This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the proj…

We provide a dynamic programming principle for stochastic optimal control problems with expectation constraints. A weak formulation, using test functions and a probabilistic relaxation of the constraint, avoids restrictions related to a measurable selection but still implies the Hamilton-Jacobi-Bellman equation in the …

Iterative method learns unknown constraints for MPC control.

We reformulate data-dependent constraints to ensure they are always met with high probability.

Physics-constrained GANs generate samples that meet deterministic constraints.

New algorithm reduces regret and constraint violation in online convex optimization with complex constraints.

The paper explores how to learn models that respect constraints in probabilistic learning.

Algorithm ensures privacy while strictly adhering to constraints.

Proposes NUV priors for half-space and box constraints.

Meta-gradient D4PG optimizes performance and constraint adherence in RL.

Geometrically characterizes virtual nonlinear nonholonomic constraints using symplectic methods.

This work is a further study on the Generalized Constraint Neural Network (GCNN) model [1], [2]. Two challenges are encountered in the study, that is, to embed any type of prior information and to select its imposing schemes. The work focuses on the second challenge and studies a new constraint imposing scheme for equa…

The paper improves Gaussian processes by adding sum constraints, enhancing prediction accuracy.

Develops a new method for optimizing with uncertain data.

Efficient algorithms decide algebraic constraints of causal graphs.

The paper introduces MU for NMF with $β$-divergences and disjoint constraints.

FISAR uses neural networks to optimize safe reinforcement learning with forward-invariant constraints.

We propose a general method for deformation quantization of any second-class constrained system on a symplectic manifold. The constraints determining an arbitrary constraint surface are in general defined only locally and can be components of a section of a non-trivial vector bundle over the phase-space manifold. The c…

Solves Einstein constraint equations on compact manifolds with specified boundaries.

Algorithm finds real line mapping from points under ordinal constraints.

HardCoRe-NAS finds fitting neural networks adhering to hard resource constraints.

Formal constraints improve RL safety in complex environments.

Study optimizes portfolio allocation policies using off-policy data and constraints.

Unified approach adjusts classifiers to meet system-level constraints.

Algorithm minimizes regret while adhering to unknown safety constraints.

CoCoRL learns safe constraints from demonstrations with unknown rewards.