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.
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.
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.
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.
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.
A graph-based evolutionary algorithm automates machine learning workflows.
problem Automated machine learning to reduce manual operations.
method Graph-based architecture for flexible model combinations, evolutionary algorithm with mutation and heredity operators, Bayesian hyper-parameter optimization.
result State-of-the-art performance compared to other AutoML systems.
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.
Study shows how high-budget agents can manipulate prediction markets.
problem Manipulation of prediction markets by high-budget agents.
method Agent-based simulations and analytic characterization of price dynamics.
result High-budget agents can temporarily shift prediction market prices.
Electrical engineer's AI journey due to deep learning convergence.
problem Separate development of AI and pattern recognition.
method Exploration of AI's historical trajectory and intersection with electrical engineering.
result Convergence of AI and electrical engineering due to deep learning.
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.
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.
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.
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.
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.
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…
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.
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.
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.
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.
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.
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.
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.
Study examines machine learning competitions' impact on AI development.
problem Fostering innovation and skill development in AI.
method Analysis of major competition platforms, workflows, and participant demographics.
result MLCs promote collaboration, reproducibility, and continuous innovation in AI.
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.
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.
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…
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.
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.
The calculus correspondence has been known to exist between generic pedal evolutions and generic wave front evolutions. In this paper, we first extend the known results on the calculus correspondence to evolutions with multi-parameters, and then give applications of calculus correspondence. Moreover, we discuss the pos…
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.
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.
Study material evolution using groupoids to track intrinsic properties.
problem Tracking material evolution without considering the whole body.
method Construct a groupoid encoding intrinsic properties and characteristic foliations.
result Define the evolution equation for material points.
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.
The paper studies circular evolutes and involutes of framed curves in Euclidean space.
problem Investigating properties of framed curves and their evolutes and involutes.
method Definition and analysis of circular evolutes and involutes of framed curves, properties of normal surfaces, and their relations.
result Circular evolutes and involutes of framed curves are opposite operations under suitable assumptions, similar to fronts in the Euclidean plane.
The paper studies curve evolution using the PLR equation and its solutions.
problem Investigating the evolution of space curves governed by the PLR equation.
method Examined the Lund-Regge evolution and derived its representation in the Frenet frame, aligning with the Lax system of the PLR equation. Developed a construction method for curve families via the Sym formula.
result Described the Lund-Regge evolution corresponding to Date multi-soliton solutions to the PLR equation.
Study of evolutes of polygons and curves in higher dimensions.
problem Understanding evolutes of spatial polygons and curves in higher dimensions.
method Analyzing iterations of evolute transformations and studying properties of evolutes for polygons and curves.
result Eigenvalues of the second evolute map have double multiplicity, and evolutes of certain curves are homothetic to the curves themselves.
Survey on two non-Kähler geometry conjectures.
problem Constant holomorphic sectional curvature and Fino-Vezzoni conjectures in non-Kähler geometry.
method Survey and discussion of historical and recent developments.
result Discussion of conjectures without new results.
This paper reflects on teaching Data Science to diverse students.
problem Adapting learning practices to diverse backgrounds in Data Science.
method Summarizes experiences from teaching a postgraduate Data Science module.
result Draws lessons relevant to teaching Data Science.