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.
Framework allows organizations to collaborate on learning tasks securely.
Improves survey sampling with unbiased machine learning methods.
Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics. However, human experts have to construct proofs manually by en…
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they wa…
A green simulation-assisted reinforcement learning method for biomanufacturing.
GALA framework learns invariant graph representations via environment augmentation with minimal assumptions.
In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields. In the past, many genetic algorithms based methods have been successfully applied to training neural networks. In this paper, we extend previous work and pro…
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) methods typically implement Bayesian inference which faces several challenges given a large number of parameters, complex and multimodal posterio…
Recent variants improve knowledge distillation performance.
Develops a two-layer model to design mortgage assistance products.
New AI assistant for power grid operators simplifies complex decision-making.
We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which as…
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data for any ML system typically improves accuracy, but only if it provides examples not already adequately covered in the existing data. However, …
Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for cryptocurrencies for the period between N…
Helps visually impaired users make better decisions by adjusting their observations.
Generative AI boosts productivity and improves customer service quality.
INT benchmark tests theorem proving agents' ability to generalize to unseen theorems.
We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted …
MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio
New model improves prediction accuracy and interpretability for network-connected data.
Two-stage architecture helps learners collaborate on data with privacy and transmission constraints.
GAMA is a user-friendly AutoML system for machine learning pipeline optimization.
We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to …
Intelligent Personal Assistants (IPAs) have become widely popular in recent times. Most of the commercial IPAs today support a wide range of skills including Alarms, Reminders, Weather Updates, Music, News, Factual Questioning-Answering, etc. The list grows every day, making it difficult to remember the command structu…
SCIENCE improves prediction intervals for individual causal effects.
In this paper we propose a new method to assist in labeling data arriving from fast running processes using anomaly detection. A result is the possibility to manually classify data arriving at a high rates to train machine learning models. To circumvent the problem of not having a real ground truth we propose specific …
Improved training of RBMs using mode-assisted gradient updates.
This research simplifies verification of machine learning systems using reparameterization.
Paper proposes personalized climate control for driver comfort.
Computer-assisted method finds new Einstein metrics on spheres.
In this paper, we propose an AdaBoost-assisted extreme learning machine for efficient online sequential classification (AOS-ELM). In order to achieve better accuracy in online sequential learning scenarios, we utilize the cost-sensitive algorithm-AdaBoost, which diversifying the weak classifiers, and adding the forgett…
Access to food assistance programs such as food pantries and food banks needs focus in order to mitigate food insecurity. Accessibility to the food assistance programs is impacted by demographics of the population and geography of the location. It hence becomes imperative to define and identify food assistance deserts …
Despite the advantages of all-weather and all-day high-resolution imaging, SAR remote sensing images are much less viewed and used by general people because human vision is not adapted to microwave scattering phenomenon. However, expert interpreters can be trained by compare side-by-side SAR and optical images to learn…
Proposes a new method to control FDR using frequentist-assisted horseshoe for high-dimensional testing.
Designs algorithms to assist humans without affecting their decisions.
Task-oriented dialog presents a difficult challenge encompassing multiple problems including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction. Modern dialog systems typically begin by converting conversation history to a symbolic object referred to as belief sta…
New research shows machine-assisted decisions can still be unfair even when the algorithm is fair.
Novel framework for ML-assisted inference valid for any statistical task.
Study evaluates the impact of academic support center's face-to-face assistance on student performance.
RISA improves VFL by using imputed samples with low uncertainty.
We consider the learning of multi-agent Hawkes processes, a model containing multiple Hawkes processes with shared endogenous impact functions and different exogenous intensities. In the framework of stochastic maximum likelihood estimation, we explore the associated risk bound. Further, we consider the superposition o…
Proposes a new NMF method incorporating neighborhood structure for better anomaly detection.
Quantum-assisted Gaussian process speeds up data regression.
New method combines simulations and data for anomaly detection.
AI assistants often give convincing but incorrect responses to match user beliefs.
AACE learns treatment policies from EHRs using annotations to improve accuracy.
Survey examines challenges of ML in avionic systems certification.
We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions. It simulates the Nosé-Hoover dynamics of a continuously-tempered Hamiltonian system built on the distribution of interest. A significant advantag…