arXiv research

Adding uninformative labels improves tumor segmentation in low-data mammography.

Neural networks compress uninformative input directions, improving test error.

The paper sets bounds on how much regret is unavoidable in adaptive LQR with unknown B-matrix.

Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …

Modeling market dynamics with informed and uninformed traders and fads.

Detecting and recovering labels in binomial logistic mixtures is challenging due to an information gap.

The estimation of probabilities of default (PDs) for low default portfolios by means of upper confidence bounds is a well established procedure in many financial institutions. However, there are often discussions within the institutions or between institutions and supervisors about which confidence level to use for the…

Proposes a semi-supervised K-Means algorithm for better feature selection.

Study shows mutual funds add little value for uninformed investors.

Informed traders strategically reveal noisier signals, making prices less responsive to public information.

New approach for uninformed investors to optimize execution costs.

AutoElicit uses LLMs to quickly create expert priors for predictive models.

New geometric interpretation explains over-parameterized models and adversarial perturbations.

Study dynamic equilibrium with insider and general uninformed agent preferences.

New method speeds up Bayesian inference for complex simulators.

This paper uses reference priors to improve deep learning models with unlabeled and labeled data.

In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function. We show that GANs without restriction on the discriminative function space commonly suffer from the problem that the gradient produced by …

A simple strategy optimizes broker-client trading, reducing price discounts for informed traders.

A key obstacle in automated analytics and meta-learning is the inability to recognize when different datasets contain measurements of the same variable. Because provided attribute labels are often uninformative in practice, this task may be more robustly addressed by leveraging the data values themselves rather than ju…

Reconstruction-based learning produces uninformative features for perception tasks.

The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without re…

This paper provides new insight into maximizing F1 scores in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, F1 score is widely used to measure the success of a binary classifier when one class is rare. Micro average, macro average, a…

SaliencyMix augments images with salient patches to improve model generalization.

Adaptive coverage policies improve conformal prediction accuracy.

This paper studies how AMMs can minimize losses from arbitrage while retaining uninformed trading activity.

New saliency evaluations focus on completeness and soundness, improving explanations.

Study shows pre-event L2 liquidity state predicts crypto futures liquidity better than event labels.

We extend the theory of asymmetric information in mispricing models for stocks following geometric Brownian motion to constant relative risk averse investors. Mispricing follows a continuous mean--reverting Ornstein--Uhlenbeck process. Optimal portfolios and maximum expected log--linear utilities from terminal wealth f…

New method samples Jeffreys prior for objective Bayesian inference.

The paper discusses the impact of prior densities on Bayesian model selection.

PARIS reduces imbalanced regression datasets by pruning uninformative samples.

Paper identifies problematic baselines in Shapley value explanations and proposes a reweighting mechanism.

New algorithm reduces online learning regret in uninformed Markov games.

Advocates against over-smoothing and over-squashing in GNNs, suggesting they are less critical than previously thought.

We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recover the data distribution and learn latent features, and formally show that common issues such as blurry samples and uninformative latent feat…

Study shows overparametrization can shift and bend loss landscapes, affecting signal recovery.

Training-free source selection for LLM families with shared vocabularies

LOBSTUR-GNN adapts bootstrapping for unsupervised GNNs, improving node representation learning.

FAIR method uses adversarial training to learn fair instance weights.

MIM adds indicator variables to improve model performance on incomplete data.

This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an uncon…

The uninformative ordering of artificial neurons in Deep Neural Networks complicates visualizing activations in deeper layers. This is one reason why the internal structure of such models is very unintuitive. In neuroscience, activity of real brains can be visualized by highlighting active regions. Inspired by those te…

The study examines when large trades are considered news or liquidity shocks in a market model.

Traditional market makers are losing their importance as automated systems have largely assumed the role of liquidity provision in markets. We update the model of Glosten and Milgrom (1985) to analyze this new world: we add multiple securities and introduce an automated market maker who uses the relationships between s…

We implement a market microstructure model including informed, uninformed and heuristic-driven investors, which latter behave in line with loss-aversion and mental accounting. We show that the probability of informed trading (PIN) varies significantly during 2008. In contrast, the probability of heuristic-driven tradin…

Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other measures for variational inference is a promising area of research. This paper proposes a robust modification of evidence and a lower bound for…

The choice of sentence encoder architecture reflects assumptions about how a sentence's meaning is composed from its constituent words. We examine the contribution of these architectures by holding them randomly initialised and fixed, effectively treating them as as hand-crafted language priors, and evaluating the resu…

This paper studies an optimal trading problem that incorporates the trader's market view on the terminal asset price distribution and uninformative noise embedded in the asset price dynamics. We model the underlying asset price evolution by an exponential randomized Brownian bridge (rBb) and consider various prior dist…