arXiv research

Interactive steering improves hierarchical clustering for diverse user needs.

The paper introduces an adjacency constraint to improve goal-conditioned HRL.

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

Hierarchical clustering is a popular unsupervised data analysis method. For many real-world applications, we would like to exploit prior information about the data that imposes constraints on the clustering hierarchy, and is not captured by the set of features available to the algorithm. This gives rise to the problem …

Proposes a hierarchical curriculum loss to improve model accuracy and interpretability.

Researchers develop methods for inference in hierarchical models using neural simulations.

Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchical…

Bayesian models use hyperparameters to indirectly assign priors, and this work shows how these priors can be derived from maximum entropy principles.

C-HMCNN(h) improves HMC classification by leveraging class hierarchy.

New MMM captures hierarchical marketing effects and sign restrictions.

Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.

New method optimizes hierarchical multi-label classification results.

Memory-limited learning tackles adversarial bandits with reduced storage.

CoRe method improves time series forecasting coherency without strict constraints.

Two new Hie-TAN and Hie-TAN-Lite algorithms improve TAN for hierarchical feature spaces.

Time-varying mixture densities occur in many scenarios, for example, the distributions of keywords that appear in publications may evolve from year to year, video frame features associated with multiple targets may evolve in a sequence. Any models that realistically cater to this phenomenon must exhibit two important p…

This work analyzes Gibbs samplers for Bayesian hierarchical models without dimensionality constraints.

Paper presents a framework to automatically discover constraints from data.

FOSC-X: An extended framework for extracting multiple optimal flat clusterings from hierarchical cluster trees

In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices $D_0$ and $D_1$ are inputted, along with a mixing parameter $α\in [0,1]$. The dissimilarities can be non-Euclidean and the weights of the observations can be non-uniform. The fi…

We develop a highly scalable optimization method called "hierarchical group-thresholding" for solving a multi-task regression model with complex structured sparsity constraints on both input and output spaces. Despite the recent emergence of several efficient optimization algorithms for tackling complex sparsity-induci…

DS2CF-Net learns hierarchical representations with deep coupled factorization and enriched prior.

BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.

Study compares local and global models for hierarchical forecasting accuracy.

Unified framework for integrating linear constraints in time series forecasting.

In this work, we introduce Graph Pointer Networks (GPNs) trained using reinforcement learning (RL) for tackling the traveling salesman problem (TSP). GPNs build upon Pointer Networks by introducing a graph embedding layer on the input, which captures relationships between nodes. Furthermore, to approximate solutions to…

Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in com…

Proposes a new algorithm for efficient probabilistic reconciliation of forecasts.

We study collaborative machine learning (ML) across wireless devices, each with its own local dataset. Offloading these datasets to a cloud or an edge server to implement powerful ML solutions is often not feasible due to latency, bandwidth and privacy constraints. Instead, we consider federated edge learning (FEEL), w…

Eikonal-Constrained QRL improves goal-reaching in reinforcement learning.

We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations. We derive a nonparametric model of monotonic functions that allows for interpretable priors and principled quantification of hierarchical uncertainty. W…

This paper proposes to learn hierarchical compositional AND-OR model for interpretable image synthesis by sparsifying the generator network. The proposed method adopts the scene-objects-parts-subparts-primitives hierarchy in image representation. A scene has different types (i.e., OR) each of which consists of a number…

Unified framework for hierarchical image classification with epistemic uncertainty.

Hierarchical text classification has many real-world applications. However, labeling a large number of documents is costly. In practice, we can use semi-supervised learning or weakly supervised learning (e.g., dataless classification) to reduce the labeling cost. In this paper, we propose a path cost-sensitive learning…

The task of matching co-referent records is known among other names as rocord linkage. For large record-linkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonable clear structure. For such problems, unsupervised or semi-supervised methods are preferable to supervised met…

Proposes an evolutionary approach to fitting acyclic VAR models.

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…

Hierarchical Latent Attribute Models (HLAMs) are a family of discrete latent variable models that are attracting increasing attention in educational, psychological, and behavioral sciences. The key ingredients of an HLAM include a binary structural matrix and a directed acyclic graph specifying hierarchical constraints…

Variable selection for models including interactions between explanatory variables often needs to obey certain hierarchical constraints. The weak or strong structural hierarchy requires that the existence of an interaction term implies at least one or both associated main effects to be present in the model. Lately, thi…

HATCH learns optimal recommendations with resource constraints.

Data aggregation improves HAC for resource-constrained systems.

We propose a method to model multi-agent behaviors with limited observation and mechanical constraints.

Fair HAC algorithms ensure clustering fairness across protected groups.

Study high-dimensional covariance matrix estimators for complex portfolios, improving financial metrics.

A novel nonstationary permanental process relaxes kernel constraints and captures complex data patterns.

Sparse deep neural networks(DNNs) are efficient in both memory and compute when compared to dense DNNs. But due to irregularity in computation of sparse DNNs, their efficiencies are much lower than that of dense DNNs on regular parallel hardware such as TPU. This inefficiency leads to poor/no performance benefits for s…

We add a set of convex constraints to the lasso to produce sparse interaction models that honor the hierarchy restriction that an interaction only be included in a model if one or both variables are marginally important. We give a precise characterization of the effect of this hierarchy constraint, prove that hierarchy…

Paper tackles NAS problem by modeling it as a sparse supernet.