arXiv research

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

HyperFair integrates fairness in recommender systems using probabilistic soft logic.

LightGCNet simplifies AI for soft sensors, reducing complexity and training time.

RPPs improve deep learning models with soft equivariance constraints.

Framework learns stochastic dynamics from endpoint and intermediate distributions using soft energy constraints.

Generative AI connects to Schrödinger bridge problems with soft constraints for stability.

Soft-Radial Projection solves gradient saturation in constrained deep learning.

New method combines gradient optimization with constraint-based techniques for causal discovery.

Adversarial MoE learns category-specific models for product search.

Automatically learns flexible symmetry constraints in neural networks using gradients.

In industrial systems, certain process variables that need to be monitored for detecting faults are often difficult or impossible to measure. Soft sensor techniques are widely used to estimate such difficult-to-measure process variables from easy-to-measure ones. Soft sensor modeling requires training datasets includin…

Paper proposes a new sparse group k-max regularization for sparsity constraints.

Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…

Optimal control problems on Riemannian manifolds are solved by penalizing constraint violations.

FedSGM tackles constrained federated learning with unified framework.

Soft-constrained PINN solves ODEs with minimal data, improving efficiency and robustness.

2D-PT improves sampling in constrained optimization problems.

We propose to jointly learn multi-view geometry and warping between views of the same object instances for robust cross-view object detection. What makes multi-view object instance detection difficult are strong changes in viewpoint, lighting conditions, high similarity of neighbouring objects, and strong variability 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…

The present work extends the randomized shortest-paths framework (RSP), interpolating between shortest-path and random-walk routing in a network, in three directions. First, it shows how to deal with equality constraints on a subset of transition probabilities and develops a generic algorithm for solving this constrain…

CriticSMC improves planning efficiency in constrained environments.

One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation becomes an appealing objective because of its operational meaning and desirable propert…

In this work, we introduce a deep learning-based polar code construction algorithm. The core idea is to represent the information/frozen bit indices of a polar code as a binary vector which can be interpreted as trainable weights of a neural network (NN). For this, we demonstrate how this binary vector can be relaxed t…

We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics. In particular, we develop a flexible methodological framework where the modeled functions and derivatives of a given order are subject to inequality or equality constraints. We then chara…

Study fairness in intervention to maximize outcomes.

New approach to control diffusion processes with soft constraints.

End-to-end text-to-speech (TTS) synthesis is a method that directly converts input text to output acoustic features using a single network. A recent advance of end-to-end TTS is due to a key technique called attention mechanisms, and all successful methods proposed so far have been based on soft attention mechanisms. H…

Deep learning models reconstruct volatility surfaces from noisy data under no-arbitrage constraints.

Study statistical guarantees for DRO with OT and OT-regularized divergences.

New algorithms optimize actions under time-varying constraints without projecting.

Soft diamond regularizers improve deep learning performance and sparsity.

CoRe method improves time series forecasting coherency without strict constraints.

This paper compares unstructured and structured EM-based semi-supervised learning methods.

In this paper, the extension of the framework of Learning from Constraints (LfC) to a distributed setting where multiple parties, connected over the network, contribute to the learning process is studied. LfC relies on the generic notion of "constraint" to inject knowledge into the learning problem and, due to its gene…

The graph-based semi-supervised label propagation algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, available methods typically calculate the weights…

Compressed Federated Distillation reduces communication in federated learning.

Many graph clustering quality functions suffer from a resolution limit, the inability to find small clusters in large graphs. So called resolution-limit-free quality functions do not have this limit. This property was previously introduced for hard clustering, that is, graph partitioning. We investigate the resolution-…

Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…

HardNet adds hard constraints to neural networks without sacrificing performance.

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. Most of existing RLfD methods require demonstrations to be perfect and sufficient, which yet is unrealistic to meet in practice. To work o…

Physics-constrained neural nets solve EM fields of charged particle beams.

A new GP method enforces physical constraints in probabilistic terms.

Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source node $V$ in an acyclic causal directed graph, to maximize the expected value of a target node $Y$ (located downstream of $V$). Our setting im…

Soft labeling impacts OOD detection in neural networks.

Developed a new thresholding method that connects soft and hard thresholding.

Soft cells fill space without gaps, derived from minimal surfaces and deformed using edge bending.

Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the mean field family of variational inference algorithms, soft- or hard-constrained inference using Lagrangian relaxation or linear programmin…

Paper studies sparsity and DAG constraints for learning linear DAGs.