Improves prediction accuracy for aggregated outputs using Gaussian processes and variational learning.
problem Difficulties in generalizing to new inputs when outputs are aggregated at a coarser level than inputs.
method Variational learning with a model of output aggregation and Gaussian processes, proposing new bounds and approximations.
result Improved prediction accuracy and scalability to large datasets, explicitly accounting for uncertainty.
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
Aggregates diverse zero-shot LLM outputs for better corporate disclosure classification.
problem Combining varied zero-shot LLM predictions for improved stock return prediction.
method Multi-prompt framework with three fixed zero-shot LLM classifiers, logistic meta-classifier aggregation.
result Aggregated model outperforms single classifiers and baseline models, increasing balanced accuracy from 0.566 to 0.606.
Proposes a method for evaluating multiple dimensions of organizational effectiveness using DEA.
problem Evaluating multiple dimensions of organizational effectiveness in large data sets.
method Introduces two regularized DEA models (SBM and GP-SBM) to estimate both dimension-specific and aggregate efficiency scores.
result Demonstrates improved efficiency and validity compared to conventional methods.
Unified approach to aggregating models and preferences.
problem Consistent aggregation of models and preferences.
method Formal definition and weighted averaging of models and preferences.
result All rational aggregation rules are weighted averages of highest-ranked models/experts.
Model infers functions for attributes using multi-aggregate datasets with knowledge transfer.
problem Modeling aggregate data with varying granularities and spatial supports.
method Multi-output Gaussian process (MoGP) with linear mixing of independent latent GPs, aggregation process, and prior distribution of mixing weights.
result Proposed model outperforms in refining coarse-grained aggregate data.
RESTA defends LLMs against jailbreaking attacks by adding random noise to embeddings.
problem Vulnerability of LLMs to jailbreaking attacks that generate harmful outputs.
method Adds random noise to embedding vectors and aggregates during token generation.
result RESTA achieves superior robustness versus utility tradeoffs compared to baseline defenses.
DNA improves graph neural networks by selectively aggregating node embeddings.
problem Static neighborhood aggregation limits graph neural networks' performance.
method Dynamic neighborhood aggregation guided by attention and controlled channel connections.
result DNA outperforms current methods in transductive node classification.
Convolutional model disaggregates electricity consumption data.
problem Disaggregating aggregate electricity consumption data into individual appliance usage.
method Gated linear unit convolutional layers and residual blocks refine neural network output. Partially overlapped sequences are averaged for final output.
result The proposed model outperforms existing models in disaggregating various appliance usage.
Researchers improve deep ensemble forecast aggregation methods.
problem Aggregating forecast distributions from deep ensembles for better predictive performance.
method Comprehensive analysis of twelve benchmark data sets, comparing probability- and quantile-based aggregation methods for three neural network-based approaches.
result A general quantile aggregation framework for deep ensembles improves predictive performance in various settings.
The paper proposes criteria and methods for evaluating and aggregating feature-based model explanations.
problem Lack of quantitative evaluation criteria for feature-based model explanations.
method Developed quantitative evaluation criteria (low sensitivity, high faithfulness, low complexity), devised a framework for aggregation, and derived a new aggregate Shapley value explanation function.
result A new aggregate Shapley value explanation function that minimizes sensitivity.
New aggregation method combining outputs and inputs for improved classifier performance.
problem Improving classifier performance by reducing the influence of bad initial estimators.
method Combining outputs of training examples with Euclidean distance on inputs, ensuring a majority of estimators agree.
result Consistency in classification and regression, superior performance compared to original method.
New method ranks sectors and countries using local and aggregate I-O data.
problem Ranking sectors and countries in global value chains using incomplete I-O tables.
method Rank-1 approximation to I-O tables using local and aggregate information. result Consistently good performance in reconstructing rankings of upstreamness and downstreamness.
We empirically test the effects of unanticipated fiscal policy shocks on the growth rate and the cyclical component of real private output and reveal different types of asymmetries in fiscal policy implementation. The data used are quarterly U.S. observati ons over the period 1967:1 to 2011:4. In doing so, we use both …
RDL-Net improves speech enhancement with fewer parameters and better performance.
problem Improving speech enhancement with fewer parameters and better performance.
method Proposes RDL-Net, a CNN combining residual and dense aggregations without over-allocating parameters.
result RDL-Net achieves higher speech enhancement performance with fewer parameters and lower computational requirements.
Study uses echo-sounder buoys to analyze tuna schools' association with dFADs globally.
problem Understanding temporal trends of tuna schools' association to drifting objects.
method Applied Machine Learning to examine binary and regression outputs of tuna schools' colonization and disaggregation times.
result Median colonization and disaggregation times varied by ocean, with Pacific having longest soak and colonization times.
Paper extends topic models using neighborhood aggregation for better performance.
problem Extending topic models with pre-trained word embeddings and nonlinear output functions.
method Network view of topic models, neighborhood aggregation algorithm.
result Approach outperforms state-of-the-art supervised Latent Dirichlet Allocation.
New method for decentralized learning from diverse clients.
problem Achieving high-performance global model from diverse client models.
method Decentralized Learning via Adaptive Distillation (DLAD).
result Demonstrates effectiveness of DLAD on multiple public datasets.
CAggNet improves medical image segmentation by fusing coarse and fine features.
problem Medical image segmentation accuracy and efficiency.
method Crossing Aggregation Network with nested skip connections and weighted aggregation.
result CAggNet achieves more accurate and efficient segmentation compared to existing methods.
MEVA aggregates model predictions to improve accuracy without needing model details.
problem Improving model accuracy by combining multiple models.
method Non-intrusive, data-driven framework that treats models as black boxes and optimizes aggregation methods.
result MVA outperforms MEA in estimating aggregated predictions, enhancing robustness and accuracy.
A fast aggregation technique boosts classifier performance without re-training.
problem Boosting algorithms require re-training, making deep learning impractical.
method Formulate aggregation problem using a mixture model fitted to classifier outputs.
result Boosted performance by 12% using less expensive training stage.
FABLE incorporates instance features into PWS label models for improved performance.
problem Lack of instance features in existing label models limits their performance.
method FABLE uses a mixture of Bayesian label models and a Gaussian Process classifier to incorporate instance features.
result FABLE achieves the highest averaged performance across nine baselines on benchmark datasets.
Paper proposes an algorithm to estimate state aggregation from Markov transition data.
problem Estimating probabilistic aggregation map from system's trajectory.
method Two-step algorithm: spectral decomposition and linear transformation of singular vectors.
result Sharp error bounds for estimating aggregation and disaggregation distributions.
Proposes a new method for deep ensembles that improves accuracy and calibration.
problem Improving accuracy and calibration of deep ensembles.
method Estimates confusion matrices of ensemble members and weighs them according to their inferred performance.
result Empirically shows superiority of soft Dawid Skene over ensemble averaging.
Improved aggregation methods learn from all ICU events without preprocessing for better patient risk analysis.
problem Lack of efficient methods to dynamically assess patient status in ICU.
method Improved aggregation methods for a deep learning architecture that learns from all events without preprocessing.
result Models achieve strong performance (AUROC 0.87) in patient mortality classification.
We study cross-country GDP losses due to financial crises in terms of frequency (number of loss events per period) and severity (loss per occurrence). We perform the Loss Distribution Approach (LDA) to estimate a multi-country aggregate GDP loss probability density function and the percentiles associated to extreme eve…
UAIL uses uncertainty estimation to improve control systems in safety-critical tasks.
problem Improving control systems in safety-critical domains like autonomous driving.
method UAIL applies Monte Carlo Dropout to estimate uncertainty in control output and selectively acquire new training data.
result UAIL can reliably predict infractions and outperforms existing algorithms.
Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation. We study the problem of unsupervised rank aggregation where no ground tr…
New games model strategic interactions in incomplete information settings.
problem Modeling strategic interactions in incomplete information settings.
method Introduced new games that map input to private player types, aggregate strategies, and converge to near-Nash equilibria.
result Games can recover meaningful strategic interactions from real data.
Study assesses consistency and reproducibility of LLMs in finance and accounting tasks.
problem Consistency and reproducibility of LLM outputs in finance and accounting research.
method Extensive experimentation with 50 independent runs across 5 tasks using 3 OpenAI models.
result Task-specific patterns of consistency and reproducibility, with binary classification and sentiment analysis achieving near-perfect reproducibility.
Proposes KDA to protect deep nets from adversarial attacks.
problem Machine learning system vulnerability to adversarial attacks.
method Key based diversified aggregation with pre-filtering.
result Demonstrates high robustness and universality against various attacks.
A method for learning rankings in non-stationary data streams.
problem Learning preferences in a population that changes over time.
method Generalized Borda algorithm for non-stationary ranking streams.
result Bounds on the minimum number of samples required to output the ground truth.
AANets balance stability and plasticity in CIL.
problem Stability-plasticity dilemma in class-incremental learning.
method Adaptive Aggregation Networks (AANets) with stable and plastic residual blocks.
result AANets improve performance on CIL benchmarks.
We study a large economy in which firms cannot compute exact solutions to the non-linear equations that characterize the equilibrium price at which they can sell future output. Instead, firms use polynomial expansions to approximate prices. The precision with which they can compute prices is endogenous and depends on t…
Simpler model outperforms state-of-the-art for disaggregating census data.
problem Disaggregating detailed census data into finer-grained, high-resolution mappings.
method Aggregate learning approach using ancillary data for an interpretable model.
result Simple model outperforms state-of-the-art on disaggregation metrics.
A new method for forming learning objectives using the sum of ranked range.
problem Forming learning objectives from aggregated values.
method Sum of ranked range (SoRR) minimization with DCA.
result The proposed method effectively forms learning objectives and is applicable to binary and multi-label/multi-class classification.
Model shows how past consumption affects household confidence, leading to varied economic outcomes.
problem Exploring how past consumption impacts current confidence and economic activity in a multi-household model.
method Developed a DSGE model where past consumption influences individual household confidence and consumption propensity.
result The model demonstrates a range of economic outcomes including high output with no crises, high output with increased volatility, and alternation of high and low output states.
Introduces SoRR for aggregating losses in supervised learning.
problem Aggregating individual losses into a single output for machine learning models.
method Sum of ranked range (SoRR) minimization using DCA.
result Demonstrates effectiveness of AoRR and TKML in improving robustness of multi-label learning.
The paper establishes principles for initializing and designing GNNs with ReLU activations to avoid oversmoothing and correlation collapse.
problem Oversmoothing and correlation collapse in deep ReLU GNNs.
method The paper derives and validates three principles for initialization and architecture selection in finite width graph neural networks with ReLU activations.
result Correct initialization, residual aggregation operators, and residual connections significantly improve early training dynamics in deep ReLU GNNs.
EPFGNN models graph connections for better node classification.
problem Graph node classification issues due to feature aggregation.
method EPFGNN models graph as a Markov Random Field with explicit pairwise factors and a GNN backbone.
result EPFGNN improves semi-supervised node classification performance.
New method reconstructs data subsets from limited published statistics.
problem Reconstructing tabular data from aggregate statistics when full datasets are not possible.
method Generates and verifies subsets of rows and columns that are guaranteed to be correct.
result Privacy violations can persist even with sparse published statistics.
Surrogate model construction for vector-valued outputs
problem Improving surrogate model accuracy and stability for complex engineering systems
method Adaptive sequential sampling for polynomial chaos expansion
result Improves surrogate accuracy and stability
AWARE improves graph prediction by aggregating walks with attention schemes.
problem Improving graph prediction accuracy using walk aggregation.
method Integrates attention schemes into walk-aggregating GNNs.
result AWARE outperforms existing methods in graph-level prediction tasks.
Continuous-time model shows insider trading constraints impact market dynamics.
problem Trading constraints faced by insiders in continuous-time models.
method Proved global existence of equilibrium with terminal trading constraint.
result Equilibrium model aligns with empirical market behaviors.
Enhances neural forecasting for hierarchically organized time series data.
problem Probabilistic coherent forecasting of time series data across different levels of aggregation.
method Proposes a coherent multivariate mixture output for neural forecasting architectures, optimizing with a composite likelihood objective.
result 13.2% average accuracy improvements on most datasets compared to state-of-the-art baselines.
MCSAE improves speaker embedding by focusing on both high- and low-level features.
problem Reduced effect of low-level features in speaker embedding encoding.
method Masked cross self-attentive encoding using ResNet with multi-layer aggregation and random masking regularization.
result Improved speaker embedding with equal error rate of 2.63% and minimum detection cost function of 0.1453.
New method improves training stochastic neural networks with tighter guarantees.
problem Training stochastic neural networks with provable guarantees.
method Developed partially-aggregated estimators and reformulated PAC-Bayesian bounds.
result Derives a differentiable objective leading to tighter generalisation guarantees.
Federated learning improves by training central model with client model outputs.
problem Direct averaging of client models is limited in FL.
method Ensemble distillation for model fusion.
result Central model trained faster with fewer communication rounds.