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.
Embeddings of lab test codes improve mortality prediction and preserve ordinality.
Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save costs on potentially redundant lab tests and inform physicians of a more…
Introduces an artificial cyber lab to test and identify cyber resilience measures.
Bayesian nonparametric LABS model adapts to function smoothness in Besov spaces.
The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according…
LLM embeddings improve adaptation to tabular -shifts with few labeled examples.
Proposes using entity embedding vectors to improve Gaussian Process models for knowledge transfer across cell lines.
Machine learning constructs problem-based medical records from electronic health records.
This study measures price risk aversion using indirect utility functions in a lab experiment.
Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability. Results: We present a seamless integration of domain knowledges and …
New model detects crying in real-world settings with improved accuracy.
SLM Lab is a framework for reproducible RL research with modular algorithms.
Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily learn deep learning, most existing tools focus on simpler models. In thi…
Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, quantitative error prediction can be helpful in improving the registration quality. The task of predicting registration error is demanding due…
Method scales up ML science by measuring multiple molecules at once.
This research shows how to learn shared representations from unpaired data.
Icebreaker tackles active information acquisition with low data, improving model performance.
Model predicts patient outcomes from EHR data by limiting feature interactions.
Robotic clothing manipulation improved with fashion image analysis techniques.
Current clinical practice to monitor patients' health follows either regular or heuristic-based lab test (e.g. blood test) scheduling. Such practice not only gives rise to redundant measurements accruing cost, but may even lead to unnecessary patient discomfort. From the computational perspective, heuristic-based test …
Traditionally, psychophysical experiments are conducted by repeated measurements on a few well-trained participants under well-controlled conditions, often resulting in, if done properly, high quality data. In recent years, however, crowdsourcing platforms are becoming increasingly popular means of data collection, mea…
This paper defines and quantifies transferability in domain generalization.
The paper revisits classical competition theory to explain speculative asset price dynamics.
The paper tests if LLMs' capabilities are executed by small subnetworks (circuits).
In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand …
Research proposes a risk-free machine learning model for COVID screening from routine blood tests.
New test for conditional independence using kernel embeddings.
Financial markets provide a natural quantitative lab for understanding some of the most advanced human behaviours. Among them is the use of mathematical tools known as financial instruments. Besides money, the two most fundamental financial instruments are bonds and equities. More than 30 years ago Mehra and Prescott f…
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. DeepMind Lab or VizDoom), but emulates physical properties of the real world, such as continuity, multi-modal partially-observable states with…
The Madry Lab recently hosted a competition designed to test the robustness of their adversarially trained MNIST model. Attacks were constrained to perturb each pixel of the input image by a scaled maximal distortion = 0.3. This discourages the use of attacks which are not optimized on the dis…
The reproducing kernel Hilbert space (RKHS) embedding of distributions offers a general and flexible framework for testing problems in arbitrary domains and has attracted considerable amount of attention in recent years. To gain insights into their operating characteristics, we study here the statistical performance of…
Graph-Relational Domain Adaptation (GRDA) adapts domains based on their graph structure.
Study shows gMPNNs struggle with OOD link prediction in larger test graphs.
A novel kernel-based test detects equality versus singularity of two probability measures.
In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn…
Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints. Often, identifying weak (but scientifically interesting) associations between a set of predictors and a response necessitates pooling datasets from multiple diverse labs or groups. While there is a rich l…
Kernel embeddings separate distinct probability distributions, simplifying testing.
A statistical test of independence may be constructed using the Hilbert-Schmidt Independence Criterion (HSIC) as a test statistic. The HSIC is defined as the distance between the embedding of the joint distribution, and the embedding of the product of the marginals, in a Reproducing Kernel Hilbert Space (RKHS). It has …
Trieste optimizes black-box functions using TensorFlow for efficient decision-making.
In the present work we are going to give a formal exposition of the ribbon graphs topic based on notes of Labourie \cite{Lab}, since is difficult to find as such in the literature.
This paper introduces an approach for detecting differences in the first-order structures of spatial point patterns. The proposed approach leverages the kernel mean embedding in a novel way by introducing its approximate version tailored to spatial point processes. While the original embedding is infinite-dimensional a…
AECF improves multimodal inference robustness and calibration.
New tests for binary classification regression functions without distribution assumptions.
Enhanced kernel ridgeless regression improves performance with LAB RBF kernels.
Metric learning methods for dimensionality reduction in combination with k-Nearest Neighbors (kNN) have been extensively deployed in many classification, data embedding, and information retrieval applications. However, most of these approaches involve pairwise training data comparisons, and thus have quadratic computat…
This study improves sentence embeddings from BERT models.
LSTM-based speaker verification usually uses a fixed-length local segment randomly truncated from an utterance to learn the utterance-level speaker embedding, while using the average embedding of all segments of a test utterance to verify the speaker, which results in a critical mismatch between testing and training. T…
Algorithm recovers causal graphs in presence of latent confounders and selection bias.