Functional Retrofitting improves embedding of unstructured data into knowledge graphs.
problem Combining unstructured data with knowledge graphs that have diverse entities and relations.
method Explicitly models pairwise relations with a variety of penalty functions and allows encoding of relation semantics.
result Significantly outperforms existing retrofitting methods on complex knowledge graphs.
LLMs outperform strong tabular baselines on industrial car retrofit prediction.
problem Industrial retrofit planning on structured operational data
method Embedding features, direct prompted classification, and ML+LLM stacking
result LLMs outperform strong tabular baselines on industrial car retrofit prediction
Proposes models for learning latent representations of evolving network vertices over time.
problem Inadequate models for capturing temporal smoothness in evolving networks.
method Proposes two models: retrofitted and linear transformation, to capture temporal smoothness.
result Proposed models significantly outperform existing models in temporal link prediction tasks.
In this work we present a review of the state of the art of information theoretic feature selection methods. The concepts of feature relevance, redundance and complementarity (synergy) are clearly defined, as well as Markov blanket. The problem of optimal feature selection is defined. A unifying theoretical framework i…
New method learns vector fields from noisy time series data.
problem Learning vector fields from noisy time series data.
method Neural network architecture with tensor products of one-dimensional neural shape functions for vector field approximation, alternating minimization for noise handling.
result Neural shape function architecture robust to noise, learning accurate vector fields from data with up to 10% Gaussian noise.
HyperFair integrates fairness in recommender systems using probabilistic soft logic.
problem Ensuring fairness in recommender systems across diverse domains.
method Soft fairness constraints integrated as regularization in a joint inference objective function.
result HyperFair improves fairness of predictions from black-box models and hybrid systems.
Active sensing improves energy breakdown without installing sensors.
problem Reduce energy consumption by providing appliance-level energy breakdown.
method Active learning solution based on low-rank tensor completion.
result Our approach gives better performance with fewer sensors installed.
Paper introduces a method to interpret complex epidemiology simulators.
problem Difficult interpretation of stochastic epidemiology simulators.
method Hijacking internal random number generators with probabilistic programming.
result Restores trust between policymakers and simulators by providing insights.
Proposes a framework for creating custom surrogate explanations.
problem Misunderstanding of LIME as the solution for surrogate explanations.
method Decomposes surrogate explainers into algorithmically independent modules.
result Empowers researchers to create custom local surrogate explanations.
Training-free looped transformers improve model performance without additional training.
problem Improving model performance without additional training or fine-tuning.
method A lightweight inference-time wrapper loops a contiguous mid-stack block of layers of a frozen checkpoint without additional fine-tuning.
result Our method improves model performance across various model families.
Proposes PSCs for UQ in deep nets without retraining.
problem Estimating uncertainty in deep nets with a single pass.
method Identifies sensitive, smooth intermediate layer, fits probabilistic model.
result PSCs achieve UQ and OOD detection performance matching existing methods.
A new method for learning policies from demonstrations without reinforcement.
problem Learning policies from demonstrations without access to reinforcement signals.
method Energy-based distribution matching (EDM) to learn policy parameters and state marginals.
result EDM yields consistent performance gains over existing algorithms for strictly batch imitation learning.
Imputation-free method learns tabular data with missing values using transformer.
problem Machine learning on tabular data with missing values often leads to unreliable outcomes due to synthetic imputation.
method Incremental attention learning (IFIAL) using transformer with attention masks.
result IFIAL outperforms state-of-the-art methods in 17 diverse tabular data sets.