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.
Alpha-GPT 2.0 integrates human insights into AI-driven investment research.
DeepDrummer generates drum loops with human preferences via active learning.
Improved NTL detection using human-in-the-loop approach with explainability.
A human-in-the-loop ML framework for precision dosing reduces expert workload and removes bias.
Proposes HEX for human-in-the-loop explainability in ML models.
Study finds visual explanations do not significantly improve human accuracy or trust in model predictions.
ShapleyBO explains BO's decisions, enhancing human-AI collaboration in robotics.
We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify proxies for interpretability, such as sparsity or the number of operations required. In this work, we optimize for interpretability by directly including humans in the …
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow d…
Mixed likelihood GPs improve model performance in human-in-the-loop experiments.
Framework for multi-agent RL with human feedback in a Snake game.
A Human-in-the-Loop Bayesian Optimization framework for constraint-aware bioprocess development.
The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressive success. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par w…
Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors. In this paper, we propose a novel human-in-the-loop learning algorithm called GLAD (GLocalized Anomaly Detection) that supports global anomaly detectors. GLAD automatically learns the…
Paper stabilizes generative model training with synthetic data.
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy. Here we propose a framework to directly support human decision-making, in which the role of machines is to reframe pr…
MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.
Conformal prediction sets improve human decision making by quantifying model uncertainty.
This paper argues for more realistic human models in RL.
This research integrates human interaction into reinforcement learning to improve sample efficiency and real-time learning.
Developers of text-to-speech synthesizers (TTS) often make use of human raters to assess the quality of synthesized speech. We demonstrate that we can model human raters' mean opinion scores (MOS) of synthesized speech using a deep recurrent neural network whose inputs consist solely of a raw waveform. Our best models …
LR-Robot accelerates SLRs by combining expert oversight and AI, revealing trends and patterns in financial research.
Many circumstances of practical importance have performance or success metrics which exist implicitly---in the eye of the beholder, so to speak. Tuning aspects of such problems requires working without defined metrics and only considering pairwise comparisons or rankings. In this paper, we review an existing Bayesian o…
Researchers use human-in-the-loop to create counterfactually augmented data, improving model performance.
A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…
In an attempt to gather a deeper understanding of how convolutional neural networks (CNNs) reason about human-understandable concepts, we present a method to infer labeled concept data from hidden layer activations and interpret the concepts through a shallow decision tree. The decision tree can provide information abo…
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…
We present a scalable, black box, perception-in-the-loop technique to find adversarial examples for deep neural network classifiers. Black box means that our procedure only has input-output access to the classifier, and not to the internal structure, parameters, or intermediate confidence values. Perception-in-the-loop…
Efficient method for training deep learning models with human validation and statistical analysis.
ACS is an interactive framework for model-free selection with guaranteed error control.
A new RL approach learns near-equivalent actions for healthcare decisions.
Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the matting problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. This user-in-the-loop nature makes th…
Variational Proximal Policy Optimization improves reinforcement learning from human feedback.
What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approa…
CoExBO optimizes lithium-ion batteries with user input, enhancing trust and efficiency.
Meta-learning curiosity algorithms improves exploration across various tasks.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
This research improves interpretability in sequential explanations using mental models.
Predictive Q-learning algorithm for IoT networks with human operators.
Introduces Motion Programs for better video analysis of human motion.
A new AI framework optimizes expert labeling of unlabeled data.
Many recommendation algorithms rely on user data to generate recommendations. However, these recommendations also affect the data obtained from future users. This work aims to understand the effects of this dynamic interaction. We propose a simple model where users with heterogeneous preferences arrive over time. Based…
Simplifying machine learning (ML) application development, including distributed computation, programming interface, resource management, model selection, etc, has attracted intensive interests recently. These research efforts have significantly improved the efficiency and the degree of automation of developing ML mode…
End-to-end learnable network for safer self-driving with interpretable intermediate representations.
Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the…
SOM-VQ tokenizes discrete models with semantic structure and navigable topology.
LR-Robot automates SLRs with AI, expert oversight, and multidimensional analysis.
A promising approach for teaching artificial agents to use natural language involves using human-in-the-loop training. However, recent work suggests that current machine learning methods are too data inefficient to be trained in this way from scratch. In this paper, we investigate the relationship between two categorie…