24 papers · ranked by Valyu relevance
Nitai Fingerhut, Matteo Sesia, Yaniv Romano
Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with highdimensional covariates, under the assumption of a partially linear model. The idea is to first fit on a subset of the samples two…
Yiqun Jiang, Wenli Zhang, Yu-Li Huang, Cameron MacKenzie + 2 more
The increased utilization of echocardiography in clinical practice has witnessed a substantial rise, underscoring its pivotal role as a diagnostic tool for various cardiovascular conditions. However, due to the relative scarcity of echocardiography tests, challenges persist in efficiently prioritizing patients for…
Jiacai Ma, Fuzhu Zou
Serving performance is widely recognized as a critical factor influencing match outcomes in professional tennis. To evaluate its contribution to winning probability, this study analyzes ATP men’s singles matches (2013-2024) and estimates the causal effects of four serve-related indicators: ace rate, first serve win…
Daqian Shao, Ashkan Soleymani, Francesco Quinzan, Marta Kwiatkowska
Machine Learning Authors: ['Daqian Shao' 'Ashkan Soleymani' 'Francesco Quinzan' 'Marta Kwiatkowska'] A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises…
Saco, Gabriel
Standard Double Machine Learning (DML; Chernozhukov et al., 2018) confidence intervals can exhibit substantial finite-sample coverage distortions when the underlying score equations are ill-conditioned, even if nuisance functions are estimated with state-of-the-art methods. Focusing on the partially linear regression…
Guodu Xiang, Kui Yu, Yujie Wang, Richang Hong + 2 more
Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuisance functions, constructing treatment and outcome residuals, and estimating causal effects from the residuals. However, DML often produces…
Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaaßen + 1 more
A Simulation Study Authors: ['Philipp Bach' 'Oliver Schacht' 'Victor Chernozhukov' 'Sven Klaaßen' 'Martin Spindler'] Proper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks. While there is an extensive literature on tuning ML learners for…
Ming Chen, Tanya T. Nguyen, Jinyuan Liu
In causal mediation analyses, of interest are the direct or indirect pathways from exposure to an outcome variable. For observation studies, massive baseline characteristics are collected as potential confounders to mitigate selection bias, possibly approaching or exceeding the sample size. Accordingly, flexible…
Achim Ahrens, Christian Hansen, Mark E. Schaffer, Thomas Wiemann
This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: short-stacking exploits the cross-fitting…
Marie Kempkes, Aroosa Ijaz, Elies Gil-Fuster, Carlos Bravo-Prieto + 3 more
'Jakob Spiegelberg' 'Evert van Nieuwenburg' 'Vedran Dunjko'] The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen data. While this counterintuitive behavior has been observed in a variety of…
Xuran Meng, Jianfeng Yao, Yuan Cao
Recent works have demonstrated a double descent phenomenon in over-parameterized learning. Although this phenomenon has been investigated by recent works, it has not been fully understood in theory. In this paper, we investigate the multiple descent phenomenon in a class of multi-component prediction models. We first…
Christoph Küng, Olena Protsenko, Rosario Vanella, Michael A. Nash
Understanding the linkage between protein sequence and phenotypic expression level is crucial in biotechnology. Machine learning algorithms trained with deep mutational scanning (DMS) data have significant potential to improve this understanding and accelerate protein engineering campaigns. However, most machine…
Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang + 1 more
'M. Natália D.S. Cordeiro'] The search for stable, lead-free perovskite materials is critical for developing efficient and environmentally friendly energy solutions. In this study, machine learning methods were applied to predict the bandgap and formation energy of double perovskites, aiming to identify promising…
Seungjun Yu, Haneol Lee, Changyoung Ju, Haewook Han + 1 more
Modern optical systems are important components of contemporary electronics and communication technologies, and the design of new systems has led to many innovative breakthroughs. This paper introduces a novel application based on deep reinforcement learning, D3QN, which is a combination of the Dueling Architecture and…
Ali Lashkaripour, David P. McIntyre, Suzanne G.K. Calhoun, Karl Krauth + 2 more
Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to…
Amit Kumar Gope, Yu-Shu Liao, Chung-Feng Jeffrey Kuo, Zina Vuluga + 1 more
'Mihai Cosmin Corobea'] Melt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality.…
Alex Lee, Joshua Rackers, William Bricker
One of the fundamental limitations of accurately modeling biomolecules like DNA is the inability to perform quantum chemistry calculations on large molecular structures. We present a machine learning model based on an equivariant Euclidean Neural Network framework to obtain quantum-accurate electron densities for…
Leif Jacobson, James Stevenson, Farhad Ramezanghorbani, Steven Dajnowicz + 1 more
Transferable neural network potentials have shown great promise as an avenue to increase the accuracy and applicability of existing atomistic force fields for organic molecules and inorganic materials. Training sets used to develop transferable potentials are very large, typically millions of examples, and as such, are…
Authors not listed
We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into…
Tengxiao Wang, Karen Chang, Matteo Tomasi, Chen-Yuan Lee + 3 more
The light/dark box test can be used to assess visual function in rodents based on their spontaneous behavior in response to light. Commonly used assay relies on a single behavioral metric, dwell time in the light or dark compartment, which may be influenced by factors other than vision, leading to unreliable assessment…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Alex Lee, Joshua Rackers, Shivesh Pathak, William Bricker
Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
Lopez Rene, Makita Mario, Ortega Laura, Lal Avantika + 1 more
Machine learning is a complex but essential technology in genomics data analysis and its popularity has increased the rate of new methodological approaches published but this raises the question of how models should be benchmarked and validated. Bench-ML is a generalizable and easy to use web interface for benchmarking…