A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper shows how expert knowledge can improve treatment effect estimation.
We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optim…
Many efficient algorithms with strong theoretical guarantees have been proposed for the contextual multi-armed bandit problem. However, applying these algorithms in practice can be difficult because they require domain expertise to build appropriate features and to tune their parameters. We propose a new method for the…
Paper proposes learnable topological features for efficient phylogenetic inference.
Improved power arbitrage through domain-adapted reinforcement learning.
DPBD simplifies labeling functions through interactive demonstrations.
Neural Architecture Search (NAS) represents a class of methods to generate the optimal neural network architecture and typically iterate over candidate architectures till convergence over some particular metric like validation loss. They are constrained by the available computation resources, especially in enterprise e…
Machine learning on graph structured data has attracted much research interest due to its ubiquity in real world data. However, how to efficiently represent graph data in a general way is still an open problem. Traditional methods use handcraft graph features in a tabular form but suffer from the defects of domain expe…
UCFE benchmarks LLMs in financial tasks with human feedback.
Competency questions help experts select best clustering for energy data.
Paper tackles unobserved confounding in human-AI collaborations.
Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide research attentions. However, the current transfer learning methods are mostly uninterpretable, especially to people without ML expertise. In thi…
Study shows LLM-advisors match human performance in eliciting preferences but struggle with conflicting needs and trust.
CliMB-DC combines human guidance and data-centric tools to improve ML for non-technical experts.
Deep neural networks have shown promising results for various clinical prediction tasks. However, training deep networks such as those based on Recurrent Neural Networks (RNNs) requires large labeled data, significant hyper-parameter tuning effort and expertise, and high computational resources. In this work, we invest…
A new method calibrates forecasts without sacrificing expertise.
Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify…
mNARX+ creates accurate surrogate models for complex systems without requiring domain expertise.
New framework assesses LLMs' expertise using nonparametric ranking and confidence diagrams.
Shapley values explain financial language models, aligning with domain knowledge.
Bayesian model transfers knowledge across different engineering fleets.
We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumi…
KEEL improves causal discovery with fuzzy knowledge and complex data.
A new framework pretrains a single GNN model for diverse graphs, overcoming domain-specific challenges.
Study shows group structures are crucial for financial model explanations.
FinHEAR combines LLMs with human expertise for better financial decision-making.
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly…
Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-question pairs could enable questions to be answered more effectively. While many research efforts have focused on the problem of general questio…
MuVI models multi-view data with structured sparsity, integrating domain knowledge.
We explore the behavior of a standard convolutional neural net in a continual-learning setting that introduces visual classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks. This setting corresponds to that which human learners face as they acquir…
DreamerV3 learns diverse tasks with a single configuration.
Study shows human advisors use context to improve student outcomes in algorithm-assisted advising.
This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expert…
Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To dis…
Failures are challenging for learning to control physical systems since they risk damage, time-consuming resets, and often provide little gradient information. Adding safety constraints to exploration typically requires a lot of prior knowledge and domain expertise. We present a safety measure which implicitly captures…
Novel framework for Bayesian neural networks incorporating task-specific constraints.
Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications. Thus a critical task in material design is establishing the processing-structure relationship, which requires domain expertise and …
Novel CNN-based gaze scanpath comparison distinguishes experts from novices in dental radiograph interpretation.
Synapse arbitrates TSFMs to improve time series forecasting performance.
PTBCC improves accuracy in multi-class annotation aggregation by learning from prototype confusion matrices.
We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion. Our approach jointly models a size purchased by a customer, and its possible return event: 1. no return, 2. returned too small 3. returned too big. Those events are drawn following a multinomial…
Sound synthesis is a complex field that requires domain expertise. Manual tuning of synthesizer parameters to match a specific sound can be an exhaustive task, even for experienced sound engineers. In this paper, we introduce InverSynth - an automatic method for synthesizer parameters tuning to match a given input soun…
Enhanced AI analysis predicts S&P 500 stock dynamics using various financial metrics.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.
Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of recovering uncertainty, they need large data volumes to be trained, which in imaging applications implicates prohibitively expensive collectio…
CNNs predict spatial fields from sparse data.
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…