Flexible evidential deep learning improves uncertainty quantification in machine learning.
problem Overconfident predictions in machine learning models can lead to serious consequences.
method Proposes flexible evidential deep learning (F-EDL) to model uncertainty over class probabilities using a flexible Dirichlet distribution.
result Empirically demonstrates state-of-the-art uncertainty quantification performance across diverse scenarios.
RegFlow models future states with flexible probability distributions.
problem Predicting future states under complex, non-deterministic scenarios.
method Hypernetwork architecture and continuous normalizing flow model.
result RegFlow achieves state-of-the-art results on benchmark datasets.
LEMs extend transformer-based architectures for complex execution problems.
problem Handling flexible time boundaries and multiple execution constraints in deep learning.
method Decouples market information processing from execution allocation decisions using TKANs, VSNs, and multi-head attention mechanisms.
result LEMs achieve superior execution performance compared to traditional benchmarks.
New method for robust financial portfolio analysis.
problem Challenges in modeling financial portfolio dependence structure.
method Nonparametric Angles-based Correlation (NAbC) method.
result Valid inferences and flexible scenarios for portfolio analysis.
Recent developments in machine-learning algorithms have led to impressive performance increases in many traditional application scenarios of artificial intelligence research. In the area of deep reinforcement learning, deep learning functional architectures are combined with incremental learning schemes for sequential …
New method detects changes online with bounds on delay.
problem Detecting changes in data streams efficiently.
method Maximizes discrepancy between pre-change and post-change distributions.
result Non-asymptotic bounds on average running length and detection delay.
Estimates outcomes under hypothetical scenarios using a flexible framework.
problem Adapting to sudden shifts in treatment patterns.
method Doubly robust estimator using incremental interventions.
result Achieves n \sqrt{n} n -consistency and asymptotic normality. This study provides benchmarks for different implementations of LSTM units between the deep learning frameworks PyTorch, TensorFlow, Lasagne and Keras. The comparison includes cuDNN LSTMs, fused LSTM variants and less optimized, but more flexible LSTM implementations. The benchmarks reflect two typical scenarios for au…
DINo forecasts PDEs with flexible extrapolation and adaptability.
problem Fixed discretizations limit real-world PDE forecasting.
method DINo uses implicit neural representations for continuous-time dynamics.
result DINo outperforms other neural PDE forecasters.
This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs,…
Agent-based models have demonstrated their power and flexibility in Econophysics. However their major challenge is still to devise more realistic simulation scenarios. The complexity of Economy makes appealing the idea of introducing chaotic number generators as simulation engines in these models. Chaos based number ge…
Transformer model handles causal inference with DAG integration.
problem Complex causal structures and adaptability across various scenarios.
method Integrates DAGs into transformer's attention mechanism.
result Surpasses existing methods in estimating causal effects.
Generative Networks outperform traditional methods in PiT ESG generation.
problem Generating economic scenarios quickly and flexibly for sudden changes.
method Comparison of nonparametric, parametric, and generative models.
result Conditional Variational Autoencoder (CVAE) performs best.
By interpreting a traffic scene as a graph of interacting vehicles, we gain a flexible abstract representation which allows us to apply Graph Neural Network (GNN) models for traffic prediction. These naturally take interaction between traffic participants into account while being computationally efficient and providing…
Simformer uses transformer models to perform flexible Bayesian inference.
problem Current simulation-based inference methods are inflexible and require fixed priors.
method Trains a probabilistic diffusion model with transformer architectures.
result Outperforms state-of-the-art methods on various benchmarks.
Novel framework for reliable long-tailed classification.
problem Challenges of long-tailed imbalance and specific error risks.
method Bayesian Decision Theory and variational optimization.
result Demonstrates reliability and flexibility in diverse tasks.
A new method combines experts' opinions to train regression models with noisy labels.
problem Training regression models with noisy labels from multiple experts.
method Estimate each labeler's expertise and combine opinions using learned weights.
result Empirically outperforms existing techniques on simulated and real data.
Flexible empirical Bayes for large-scale multiple linear regression.
problem Large-scale multiple linear regression with flexible priors and efficient computation.
method Adaptive shrinkage priors combined with variational approximations for hyperparameter estimation.
result The posterior mean from the empirical Bayes method solves a penalized regression problem.
In this paper, we present a two-stage stochastic international portfolio optimisation model to find an optimal allocation for the combination of both assets and currency hedging positions. Our optimisation model allows a "currency overlay", or a deviation of currency exposure from asset exposure, to provide flexibility…
New method tackles high-dimensional contextual bandits with flexible kernel models.
problem Maximizing rewards in decision-making scenarios with many features.
method Introduces stochastic assumptions and no-regret learning for Gaussian kernels.
result Achieves no-regret learning even with feature dimensions growing with samples.
Method enhances anomaly detection using contrastive learning and out-of-distribution data.
problem Improving anomaly detection in datasets with limited out-of-distribution data.
method Proposes a contrastive learning method that incorporates out-of-distribution data to enhance anomaly detection performance.
result The method significantly improves anomaly detection performance, even with limited out-of-distribution data.
Gaussian processes (GPs) are commonplace in spatial statistics. Although many non-stationary models have been developed, there is arguably a lack of flexibility compared to equipping each location with its own parameters. However, the latter suffers from intractable computation and can lead to overfitting. Taking the i…
Proposes a framework for modeling RTB auctions using point processes.
problem Modeling and optimizing repeated auctions in the RTB ecosystem.
method Develops a stochastic framework using point processes to model and optimize RTB auctions.
result The proposed framework can be approximated to a Poisson point process, enabling the use of established properties.
Unified framework for N-tuples learning improves weakly supervised tasks.
problem Reducing annotation burden in supervised learning.
method Empirical risk minimization framework integrating pointwise unlabeled data.
result Framework improves generalization across various N-tuples learning tasks.
Proposes a flexible method for learning latent causal representations.
problem Limited applicability of existing causal representation learning methods.
method Imposes constraints on function classes and relaxes identifiability conditions.
result Establishes partial identifiability results under weaker conditions.
Humans and animals show remarkable flexibility in adjusting their behaviour when their goals, or rewards in the environment change. While such flexibility is a hallmark of intelligent behaviour, these multi-task scenarios remain an important challenge for machine learning algorithms and neurobiological models alike. We…
Two conformal methods create flexible conditional predictive bands without strong assumptions.
problem Achieving asymptotic conditional coverage without strong dependence assumptions.
method Flexible distribution-free conditional predictive bands using density estimators.
result Methods achieve better control of conditional coverage and smaller length than previous methods.
New methods generalize nonlinear ICA beyond structural sparsity.
problem Identify true latent sources from nonlinear mixtures without structural sparsity assumptions.
method Propose identifiability results for undercomplete, partial sparsity, and flexible grouping structures.
result Prove identifiability in general settings of undercompleteness, partial sparsity, and flexible grouping structures.
Proposes a new allocation method for distributionally robust ranking and selection.
problem Inaccurate simulation input modeling due to limited data.
method Introduces a simple additive allocation (AA) procedure and a general additive allocation (GAA) framework.
result Proves that the proposed AA procedure is consistent and achieves additivity in the strongest sense.
New framework using Jensen-Shannon divergence improves domain adaptation theory.
problem Incoherence between empirical domain adversarial training and theoretical H \mathcal{H} H -divergence. method Established new theoretical framework based on Jensen-Shannon divergence, derived bi-directional upper bounds.
result Framework exhibits flexibilities for various transfer learning problems.
CDST improves ensemble prediction by adjusting model weights based on covariates.
problem Improving ensemble prediction accuracy in complex scenarios.
method Covariate-dependent stacking (CDST) with flexible model weights estimated via cross-validation.
result CDST consistently outperforms conventional model averaging methods in complex datasets.
In this paper, we introduce a novel, non-recursive, maximal matching algorithm for double auctions, which aims to maximize the amount of commodities to be traded. It differs from the usual equilibrium matching, which clears a market at the equilibrium price. We compare the two algorithms through experimental analyses, …
We propose a unified methodology to input non-linear views from any number of users in fully general non-normal markets, and perform, among others, stress-testing, scenario analysis, and ranking allocation. We walk the reader through the theory and we detail an extremely efficient algorithm to easily implement this met…
Proposes a flexible deep learning model for complex distributions.
problem Complex shapes, strong skews, and multiple modes in output variable distributions.
method Uncountable Mixture of Asymmetric Laplacians (UMAL) deep learning framework.
result UMAL can estimate heterogeneous distributions without strong assumptions.
Bayesian Gaussian Process ODEs enhanced with normalizing flows for improved flexibility and accuracy.
problem Limitations of standard Gaussian Process ODEs in modeling complex scenarios.
method Introducing normalizing flows to reparameterize the ODE vector field, developing a data-driven variational learning algorithm.
result Improved accuracy and uncertainty estimates for Bayesian Gaussian Process ODEs.
Paper proposes a robust hypothesis testing method using Sinkhorn distance.
problem Hypothesis testing for small samples.
method Data-driven approach using Sinkhorn uncertainty sets.
result The method provides a more flexible detector compared to Wasserstein robust test.
Improves robustness of information bottleneck framework with sparsity-inducing prior.
problem Fixed-dimensional priors restrict flexibility and restrict robustness.
method Sparsity-inducing spike-slab categorical prior that learns dimension distribution per data point.
result Improves accuracy and robustness compared to traditional priors and other methods.
Tensor models improve joint EEG and fMRI analysis.
problem Jointly analyzing EEG and fMRI for brain function studies.
method Soft and flexible coupling of tensor decompositions for EEG and fMRI.
result Tensorial methods outperform ICA in multi-modal analysis.
In large scale systems, approximate nearest neighbour search is a crucial algorithm to enable efficient data retrievals. Recently, deep learning-based hashing algorithms have been proposed as a promising paradigm to enable data dependent schemes. Often their efficacy is only demonstrated on data sets with fixed, limite…
Proposes a flexible feature allocation model for sparse factor analysis.
problem Sparse data and rigid assumptions in traditional exploratory tools.
method Adaptive latent feature sharing with control over feature sparsity.
result Derives a novel adaptive Factor analysis (aFA) and aPPCA for flexible dimensionality reduction.
CausalCompass evaluates TSCD robustness under violations of modeling assumptions.
problem Widespread adoption of TSCD is hindered by untestable causal assumptions and lack of robustness evaluation.
method CausalCompass is a flexible benchmark framework for assessing TSCD robustness under violations of modeling assumptions.
result No single method consistently attains optimal performance across all settings, but deep learning-based methods perform well.
Unified approach for non-stationary and clustered bandits.
problem Solving non-stationary and clustered bandits with overlapping solutions.
method Test of homogeneity for seamless integration of non-stationary and clustered bandits.
result Unified solution framework for change detection and cluster identification.
Secure Multiparty Computation protects data privacy in Symbolic Regression.
problem Data privacy in Symbolic Regression models.
method Secure Multiparty Computation for vertical partitioning.
result Comparable performance to centralized model while preserving privacy.
pmsims R package uses Gaussian process for flexible sample size estimation in clinical models.
problem Determining adequate sample size for clinical prediction models.
method Simulation-based Gaussian process search for flexible sample size estimation.
result Gaussian process-based method produces more stable sample size estimates, especially in challenging settings.
Improved nested simulation for financial risk measurement.
problem Efficiently estimating nested risk measures in financial engineering.
method Reusing inner simulation outputs to improve efficiency and accuracy.
result The proposed approach outperforms standard nested simulation and regression methods.
Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts
problem Representing clinical concepts and patients in electronic health records
method Spectral-based unsupervised learning with flexible knowledge transfer
result Outperforms competing approaches in challenging scenarios
Adaptive batch sizes improve active learning efficiency and flexibility.
problem Fixed batch sizes in active learning are inefficient due to dynamic cost-speed trade-offs.
method Probabilistic Numerics framework that adaptively changes batch sizes based on integration error and precision objectives.
result Significant enhancement in learning efficiency and flexibility across various applications.
Proposes a flexible neural recommendation framework for better prediction performance.
problem Data sparsity, cold start problem, and long-tail distribution in recommendations.
method A modular neural recommendation framework that includes a neural collaborative filtering part and a text processing part as a regularizer.
result Achieves better prediction performance than state-of-the-art text-aware methods using a simple text processing approach.