The paper shows how expert knowledge can improve treatment effect estimation.
problem Lack of leveraging expert knowledge in treatment effect estimation.
method Formally defining two types of expertise (predictive and prognostic) and demonstrating their influence on treatment effect estimation methods.
result Expertise type significantly influences treatment effect estimation methods, and can be predicted from a dataset.
PTBCC improves accuracy in multi-class annotation aggregation by learning from prototype confusion matrices.
problem Inaccurate and insufficient confusion matrices for annotators in multi-class classification tasks.
method PTBCC (ProtoType learning-driven Bayesian Classifier Combination) uses prototype confusion matrices to capture annotator expertise.
result PTBCC achieves up to 15% accuracy improvement and 3% higher average accuracy compared to existing methods.
Model aggregates answers with peer predictions, inferring world states.
problem Aggregating answers from multiple respondents without assuming consensus correctness.
method Probabilistic model incorporating respondent signals and predictions.
result Model infers world states and respondent expertise, outperforming other models.
The paper models crowdlearning dynamics and user expertise evolution.
problem Understanding the evolution of user expertise in crowdlearning platforms.
method Probabilistic modeling framework and scalable estimation method.
result High-value knowledge is rare, and user proficiency varies.
A new method calibrates forecasts without sacrificing expertise.
problem Forecasters' calibration scores can be manipulated to appear expert.
method Deterministic and stochastic online procedures to calibrate forecasts.
result Calibration can be achieved without losing expertise.
Study shows human advisors use context to improve student outcomes in algorithm-assisted advising.
problem How human advisors use context to guide interventions in algorithm-assisted advising.
method Mixed-methods approach combining quantitative and qualitative data from a randomized controlled trial.
result 2 out of 3 interventions by advisors were plausibly 'expertly targeted' to students using non-algorithmic context.
Unified framework for deep learning with crowdsourced data.
problem Learning true labels from noisy, sparse, and uncontrolled crowdsourced annotations.
method Bayesian deep learning framework that learns annotator expertise and optimizes model training.
result Framework reduces annotation and training time for deep learning models.
A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy ordinal labels obtained from multiple annotators of varying and unknown expertise levels. Annotation models for ordinal…
Pilot study shows multimodal signals improve chess expertise detection.
problem Detecting chess expertise through multimodal signals.
method Multimodal observation of chess players' eye-gaze, posture, emotion, and body language.
result Multimodal approach reaches up to 93% accuracy in detecting chess expertise compared to 86% with unimodal approach.
Paper proposes learnable topological features for efficient phylogenetic inference.
problem Finding appropriate topological structures for phylogenetic inference tasks requires significant design effort and domain expertise.
method Combines raw node features with graph neural networks to automatically adapt to different tasks.
result Demonstrates effectiveness and efficiency on simulated and real data phylogenetic inference tasks.
FinHEAR combines LLMs with human expertise for better financial decision-making.
problem Challenges in financial decision-making for language models.
method Multi-agent framework with specialized LLMs for historical analysis, event interpretation, and expert retrieval.
result FinHEAR outperforms baselines in financial tasks with higher accuracy and risk-adjusted returns.
Interactive learning of automata models with human input.
problem Learning finite state automata from noisy, incomplete data.
method Evidence-driven state-merging algorithm with human interaction.
result Human input significantly improves automata learning accuracy.
VILD learns policies from mixed-quality demonstrations by modeling expert levels.
problem Challenges in learning from diverse-quality demonstrations.
method Explicitly models expert levels with a probabilistic graphical model and variational approach.
result VILD outperforms state-of-the-art methods in continuous-control benchmarks.
Algorithm improves learning by integrating diverse agents' behaviors.
problem Lack of social learning in reinforcement learning algorithms.
method Free energy approach for social bandit learning.
result Algorithm converges to optimal policy and enhances learning.
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.
R2DE assesses new exam questions quickly and accurately.
problem Assessing newly generated exam questions efficiently and accurately.
method R2DE uses NLP to estimate difficulty and discrimination of questions based on their text.
result R2DE can estimate question difficulty and discrimination quickly and accurately.
Learning algorithms normally assume that there is at most one annotation or label per data point. However, in some scenarios, such as medical diagnosis and on-line collaboration,multiple annotations may be available. In either case, obtaining labels for data points can be expensive and time-consuming (in some circumsta…
Novel CNN-based gaze scanpath comparison distinguishes experts from novices in dental radiograph interpretation.
problem Distinguishing expertise in dental radiograph interpretation based on gaze behavior.
method Convolutional neural networks (CNN) process scene information at the fixation level, using image patches as input to compare gaze scanpaths.
result 93% accuracy in distinguishing experts from novices using image patch features.
UCFE benchmarks LLMs in financial tasks with human feedback.
problem Evaluating LLMs' financial task performance and user satisfaction.
method Hybrid approach combining human expert evaluations and dynamic interactions.
result Significant alignment between benchmark scores and human preferences (Pearson correlation coefficient of 0.78).
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.
problem Lack of consistent advisor expertise and difficulty in encoding it in LLM systems.
method Grounds financial advisor personas in fund disclosures, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and manager interpretation than generic baselines.
Develops a test to assess if human experts add value to predictions.
problem Detecting if human expertise adds value to predictions.
method Statistical framework and hypothesis test to assess independence of expert predictions from outcomes.
result Physicians' decisions for AGIB patients incorporate information not available to a screening tool.
Paper describes profiles of multivariate normal distributions and novel estimators for mutual information.
problem Estimating mutual information for complex distributions.
method Analytical description of profiles, introduction of Bend and Mix Models, Monte Carlo estimation.
result Bend and Mix Models accurately estimate mutual information profiles and provide Bayesian estimates.
Bayesian model transfers knowledge across different engineering fleets.
problem Data sparsity in predictive models for engineering infrastructure.
method Hierarchical Bayesian approach with multitask learning.
result Improves survival analysis and power prediction in truck fleets and wind farms.
New method optimizes hyperparameters in deep learning models efficiently.
problem Manual hyperparameter tuning in deep learning models is inefficient and requires expertise.
method Introduces lower bounds to the linearized Laplace approximation of the marginal likelihood using neural tangent kernels.
result Optimization of hyperparameters can be significantly accelerated using the method.
DPBD simplifies labeling functions through interactive demonstrations.
problem Difficulty in writing labeling functions for large-scale labeled training data.
method Data Programming by Demonstration (DPBD) framework using interactive demonstrations.
result Ruler system generates labeling rules more easily and with higher user satisfaction.
Advocates for user-friendly RL problem descriptions to improve usability and generalization.
problem Usability and generalization challenges in RL for non-engineers.
method Development of user-friendly description languages for RL problems.
result Improved ability of RL algorithms to generalize to new problems.
New approach to off-policy evaluation connects causal graph to policy effects.
problem Evaluating policies using observational data from different policies.
method Formalizes off-policy evaluation within a causal graph framework.
result Identifies specific causal estimands and highlights necessary experimental data.
CausalPFN automates causal effect estimation from observational data.
problem Manual selection of causal effect estimators is time-consuming and requires domain expertise.
method CausalPFN is a transformer that learns to infer causal effects from raw observations without task-specific adjustments.
result CausalPFN achieves superior performance on various benchmarks and real-world tasks.
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
A new method for contextual bandits using decision trees.
problem Applying efficient algorithms for contextual bandits in practice requires domain expertise.
method Uses decision trees to model context-reward relationships and a bootstrapping approach for exploration-exploitation.
result Demonstrates improved performance on various datasets compared to existing methods.
InverSynth automatically tunes synthesizer parameters from audio input.
problem Manual tuning of synthesizer parameters is time-consuming and requires expertise.
method Strided convolutional neural networks for inferring synthesizer parameters.
result InverSynth outperforms baselines in synthesizer parameter tuning.
Study identifies Bitcoin arbitrageurs and their trading strategies.
problem Detecting and understanding Bitcoin arbitrageurs on Mt. Gox.
method Analyzing historical trade data from Mt. Gox (2011-2014) to identify and categorize arbitrageurs.
result Expert arbitrageurs have a positive profit margin, while novice users do not.
New method learns robot skills from data, matching or outperforming existing methods.
problem Learning robot skills from fixed datasets.
method Offline Reinforcement Learning via Supervised Learning using implicit models.
result Implicit models can match or outperform explicit models in acquiring robotic skills.
Machine learning improves communications design without deep expertise.
problem Design high-performance communication systems with limited domain knowledge.
method Apply machine learning techniques to two communication problems.
result Deep learning discovered a simple and effective strategy for system design.
Paper tackles medical question similarity using domain-relevant embeddings.
problem Identifying same-question pairs in medical contexts.
method Semi-supervised pre-training of a neural network on medical question-answer pairs.
result Our model achieves 82.6% accuracy on medical question similarity task.
VORACE uses random classifiers to vote for the best class, saving time and expertise.
problem Finding the best classifier for a dataset is costly and requires domain expertise.
method Randomly generated classifiers vote to determine the best class ranking.
result VORACE outperforms state-of-the-art methods on various datasets.
Probabilistic models assess credibility in evolving online communities.
problem Widespread concern about quality and credibility of online content.
method Probabilistic graphical models for joint analysis of user interactions, community dynamics, and text content.
result Automatic assessment of credibility and user expertise with interpretable explanations.
New safety measure reduces failures in learning systems.
problem Challenges in learning to control physical systems due to failures.
method Implicitly captures system dynamics to failure states using Gaussian processes for model-free active sampling.
result Significant reduction in failures during learning with estimated safety measure.
Deep learning predicts AF recurrence from MRI images without pre-processing.
problem Estimating AF recurrence from MRI images efficiently and accurately.
method Deep learning approach to predict shape descriptors directly from MRI images.
result Deep learning method produces similar outcomes to state-of-the-art methods.
Paper tackles unobserved confounding in human-AI collaborations.
problem Unobserved confounding undermines human-AI collaboration effectiveness.
method Combines sensitivity analysis from causal inference with AI-driven statistical modeling.
result Enhances robustness and reliability of collaborative outcomes.
Graph representation learning improves with domain knowledge.
problem Efficiently learning graph representations from scarce labels.
method Multi-task knowledge distillation combining graph metrics.
result Improves prediction performance, especially with limited training data.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
problem Lack of consistent and specific financial advisor expertise in personalized investment advice.
method Grounds financial advisor personas in fund disclosures, holdings transitions, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and grounded manager interpretation than generic baselines.
Proposes a method for time-evolving and difficulty-level topic discovery.
problem Discovering evolving and advanced topics in dynamic corpora.
method Constrained Coupled Matrix-Tensor Factorization with expertise-level constraints.
result Identifies evolving and difficulty-level topics in community-contributed content.
System learns user preferences to synthesize materials quickly.
problem Slow material synthesis for novice and expert users.
method Gaussian Process Regression for user preferences, neural network for real-time image predictions.
result Real-time material synthesis enables novice users to generate hundreds of models.
3D dataset for intracranial aneurysms aids deep learning applications.
problem Lack of 3D medical datasets for deep learning.
method Developed an open-access 3D dataset, IntrA, for intracranial aneurysms.
result Demonstrated the challenges and performance of 3D medical datasets.
NASIB adapts NAS to varying computation resources efficiently.
problem Constrained computation resources in enterprise environments.
method Adapts exploration vs. exploitation trade-off and uses Superkernels.
result Searches over a larger space with similar accuracy in less time.
Enhances model compression with multi-teacher knowledge distillation.
problem Uncertainty evaluation and diverse teacher expertise in model deployment.
method Bayesian inference and teacher-informed prior with entropy-based weighting.
result Improved predictive accuracy and robust uncertainty quantification.
Novel framework uses few data for Bayesian inference in imaging.
problem Uncertainty estimation in machine learning for imaging requires large data volumes.
method Variational inference framework combining few data, domain expertise, and existing datasets.
result Bayesian models achieve state-of-the-art reconstructions with minimal data collection.