arXiv research

New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.

We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep generative relational model (SDREM) builds a deep network architecture that can approximate potential nonlinear mappings between nodes' featur…

Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node at- tributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochas…

Quantifying the importance and power of individual nodes depending on their position in socio-economic networks constitutes a problem across a variety of applications. Examples include the reach of individuals in (online) social networks, the importance of individual banks or loans in financial networks, the relevance …

The ability to estimate joint, conditional and marginal probability distributions over some set of variables is of great utility for many common machine learning tasks. However, estimating these distributions can be challenging, particularly in the case of data containing a mix of discrete and continuous variables. Thi…

Study efficient algorithms for identifying minimum interventional sets to learn causal relationships.

New algorithms verify and search causal graphs with minimal interventions.

DRew dynamically rewires message passing to improve long-range tasks.

We present a new family of zero-field Ising models over $N$ binary variables/spins obtained by consecutive "gluing" of planar and $O(1)$-sized components and subsets of at most three vertices into a tree. The polynomial-time algorithm of the dynamic programming type for solving exact inference (computing partition func…

A UCB algorithm reduces regret in cooperative multi-agent graph bandits.

Graph neural networks (GNNs) have achieved outstanding performance in learning graph-structured data and various tasks. However, many current GNNs suffer from three common problems when facing large-size graphs or using a deeper structure: neighbors explosion, node dependence, and oversmoothing. Such problems attribute…

There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs s…

Extreme Learning Machines (ELMs) have become a popular tool in the field of Artificial Intelligence due to their very high training speed and generalization capabilities. Another advantage is that they have a single hyper-parameter that must be tuned up: the number of hidden nodes. Most traditional approaches dictate t…

Causal inference improves heterophilic graph learning.

AdaCAD improves semi-supervised classification by focusing on intra-class nodes.

This work characterizes optimal multiclass learning with regularization.

Extract common latent factors from graphs for better representation learning.

Cost-aware BO minimizes function evaluations with varying costs.

Paper optimizes broker performance by estimating execution costs.

Paper proposes new costs for learning multiple centers in MDNs.

Using a methodology similar to that used the in the worldwide research, the cost performance of Dutch large-scale transport infrastructure projects is determined. In the Netherlands, cost overruns are as common as cost underruns but because cost overruns are larger than cost underruns projects on average have a cost ov…

Proposes resilience metrics for large blackout costs with logarithmic resilience.

Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, b…

Optimal benchmark design varies based on costs in financial manipulation.

The paper analyzes the benefit-cost ratio for feature selection in machine learning.

Study calculates liquidity costs for delta hedging of European options.

New model considers varying costs in learning, outperforming existing methods.

Transaction costs appear in financial markets in more than one form. There are several results in the literature on small proportional transaction cost and not that many on fixed transaction cost. In the present work, we heuristically study the effect of both types of transaction cost by focusing on a portfolio optimiz…

New algorithm reduces misclassification costs in neural networks.

The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the cost of evaluation for black-box objective functions is non-uniform since different inputs from search space may incur different costs for …

This paper proposes CSADA to make DNNs cost-sensitive.

The paper improves competitive and dynamic regret bounds for smoothed online learning.

This paper tackles cost-sensitive portfolio optimization under ambiguous return distributions.

A new cost-frugal HPO method controls training cost during optimization.

We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified {\it average} feature acquisition b…

Investigates how trading boundaries change with transaction costs in portfolio selection.

New RL algorithms reduce costs for single-agent and federated learning.

New method optimizes costly evaluations in Bayesian optimization.

New method reduces total cost constraints in CBwK to sqrt(T) with fairness application.

Study how transaction costs impact stock returns and holdings in equilibrium.

The multimodal web elements such as text and images are associated with inherent memory costs to store and transfer over the Internet. With the limited network connectivity in developing countries, webpage rendering gets delayed in the presence of high-memory demanding elements such as images (relative to text). To ove…

Estimates optimal transport maps with known cost functions.

Proposes an angle-based framework for multicategory cost-sensitive classification.

The paper optimizes portfolios with transaction costs in a large asset universe.

We study the high-frequency limits of strategies and costs in a Nash equilibrium for two agents that are competing to minimize liquidation costs in a discrete-time market impact model with exponentially decaying price impact and quadratic transaction costs of size $θ\ge0$. We show that, for $θ=0$, equilibrium strategie…

Study optimal investment and consumption strategies with various transaction costs.

Cost-benefit analysis often assumes accurate estimates, but this study finds significant inaccuracies.

Introduces a Cost function to measure Legendrian knot obstructions.