Flexible evidential deep learning improves uncertainty quantification in machine learning.
problem Overconfident predictions in machine learning models can lead to serious consequences.
method Proposes flexible evidential deep learning (F-EDL) to model uncertainty over class probabilities using a flexible Dirichlet distribution.
result Empirically demonstrates state-of-the-art uncertainty quantification performance across diverse scenarios.
Localized debiased machine learning simplifies estimating quantile treatment effects.
problem Estimating quantile treatment effects in causal inference with many covariates and flexible relationships.
method Localized debiased machine learning (LDML) avoids learning the full nuisance function by estimating only at a single initial guess.
result LDML enables practically-feasible and theoretically-grounded efficient estimation of quantile treatment effects.
DRMMs enable flexible conditional sampling for interactive machine learning.
problem Limited flexibility in conditional sampling for deep generative models.
method Proposes Deep Residual Mixture Models (DRMMs) that allow flexible conditional sampling.
result DRMMs enable sampling with arbitrary combinations of conditioning variables and priors.
We describe two applications of machine learning in the context of IP/Optical networks. The first one allows agile management of resources at a core IP/Optical network by using machine learning for short-term and long-term prediction of traffic flows and joint global optimization of IP and optical layers using colorles…
This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalabili…
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
New benchmark tests machine learning's ability to learn causal overhypotheses.
problem Machine learning's difficulty in understanding causal overhypotheses.
method Adapted blicket detector environment for machine learning agents to test causal overhypotheses.
result Many state-of-the-art methods struggle with causal overhypotheses in the new benchmark.
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learni…
LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
problem Combining flexibility and interpretability in personalized treatment rules.
method Uses variational autoencoders and a mixture of interpretable experts.
result Accurately recovers true local coefficients and optimal treatment strategies.
DML addresses biases in machine learning by estimating nuisance functions.
problem Bias in machine learning models due to nuisance functions.
method Double/Debiased Machine Learning (DML) approach to reduce biases.
result DML allows flexible estimation of nuisance functions without auxiliary assumptions.
Theory and methods to mitigate omitted variable bias in causal machine learning.
problem Mitigating omitted variable bias in causal machine learning models.
method Developed a general theory and flexible statistical inference methods for bounding and testing the magnitude of omitted variable bias.
result Simple plausibility judgments can bound the magnitude of omitted variable bias in complex, nonlinear models.
Double machine learning improves causal effect estimation by relaxing assumptions.
problem Estimating causal effects with observational data.
method Double/debiased machine learning (DML) framework.
result DML improves adjustment for nonlinear confounding relationships.
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
problem Flexible and composable nonlinear mixed-effects modeling
method Macro-based modeling language and unified interface
result Substantially expand the range of nonlinear mixed-effects models
Graph convolutional deep kernel machine learns representations for graph tasks.
problem Limited representation learning in infinite-width neural networks.
method Developed a graph convolutional deep kernel machine as an infinite-width limit.
result Representation learning improves performance for heterophilous node classification tasks.
tsflex speeds up time series processing and feature extraction.
problem Inefficient and inflexible time series processing tools.
method Flexible Python toolkit exttttsflex for multivariate, asynchronous time series. result Significantly faster and more memory-efficient than existing packages.
Flexible framework improves communication efficiency across various systems.
problem Reducing communication between nodes in machine learning tasks.
method Adapts compression level to true gradient at each iteration, optimizing per-bit improvement.
result Automatic tuning strategies significantly increase communication efficiency.
MLJ offers a Julia package for composing machine learning models.
problem Complex model composition in machine learning.
method Flexible model composition tools and meta-algorithms.
result Clear benefits of Julia over multi-language alternatives.
ddml aids causal inference in econometrics with machine learning.
problem Estimation of causal effects with endogenous variables and unknown functional forms.
method Double/Debiased Machine Learning (DDML) in Stata.
result Monte Carlo evidence supports using DDML with stacking for causal inference.
Optical ESNs enable flexible, efficient machine learning with reduced energy.
problem Implementing universal computational capabilities in machine learning.
method Optical implementation of ESNs leveraging stimulated Brillouin scattering.
result Efficient, scalable, and memory-capable optical reservoir computing.
Flexible framework integrates machine learning and DRO for uncertain parameter prediction.
problem Limited joint observations of uncertain parameters and covariates.
method Wasserstein, sample robust optimization, and phi-divergence-based ambiguity sets.
result Validation of theoretical and practical benefits in limited data scenarios.
We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process. In common with the original Helmholtz machine and later variational autoencoder algorithms (but …
A new method combines experts' opinions to train regression models with noisy labels.
problem Training regression models with noisy labels from multiple experts.
method Estimate each labeler's expertise and combine opinions using learned weights.
result Empirically outperforms existing techniques on simulated and real data.
Proposes ELASTICBSP for faster, more flexible distributed deep learning training.
problem Inefficient synchronization in classic BSP for distributed deep learning.
method Introduces ELASTICBSP model with ZIPLINE method for more flexible training.
result ELASTICBSP converges faster and more accurately than classic BSP.
Frengression models causal data flexibly and faithfully.
problem Challenges in robust benchmarking and evaluation of causal inference with real-world data.
method Introduces frengression, a deep generative model for joint distribution of covariates, treatments, and outcomes.
result Frengression provides accurate estimation and flexible simulation of multivariate, time-varying data.
Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we g…
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes framework and some of its main theoretical and algorithmic developments.
ANN with GA optimizes flexible disc design for lower mass and stress.
problem Design flexible disc elements for lower mass and stress without compromising torque transmission and misalignment.
method Artificial Neural Network (ANN) coupled with Genetic Algorithm (GA) for design exploration.
result Optimized designs meet specified criteria with minimum mass and stress.
We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in th…
A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and…
Flexible model for complex relationships using Bayesian nonparametrics.
problem Complex relationships between variables not well captured by simple models.
method Hierarchical generation of nonlinear features, Bayesian inference, variable selection.
result Find interpretable models with a small set of important features.
Paper proposes a method to estimate scientific parameters in hybrid models without relying on model architecture.
problem Estimating unknown parameters in hybrid models combining machine learning and scientific models.
method Sharpness-aware minimization adapted for hybrid modeling, focusing on model simplicity.
result Demonstrates effectiveness of SAM-based hybrid model learning for scientific parameter estimation.
Researchers develop flexible kernels for biological sequences with guaranteed reliability.
problem Challenges in applying machine learning to biological sequences, including unreliable methods.
method Theoretical analysis and development of modified kernels to ensure reliability and accuracy.
result Developed kernels that are universal, characteristic, and metrize the space of distributions for biological sequences.
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data s…
A heuristic minimizes tardy jobs' total weight on single-machine scheduling.
problem Minimizing tardy jobs' total weight on single-machine scheduling.
method Data-driven heuristic combining machine learning and problem-specific characteristics.
result Significantly outperforms state-of-the-art in optimality gap and adaptability.
Bayesian nonparametric machine learning improves instrumental variable inference.
problem Estimating causal effects with nonlinear relationships.
method Bayesian Additive Regression Trees (BART) for estimating functions and Dirichlet Process mixtures for error terms.
result Dramatic improvements in inference with nonlinear data, no manual tuning required.
Recent developments in machine-learning algorithms have led to impressive performance increases in many traditional application scenarios of artificial intelligence research. In the area of deep reinforcement learning, deep learning functional architectures are combined with incremental learning schemes for sequential …
Flexible framework compresses models using LC algorithm.
problem Efficiently compressing neural networks for resource constraints.
method Decouples learning and compression steps with alternating L and C phases.
result Compressed models maintain performance and accuracy.
Enhances demand models with deep learning for personalized pricing.
problem Capturing rich heterogeneity in demand models.
method Integrates deep neural networks into structural models with economic structure.
result Captures rich heterogeneity and creates personalized pricing.
SchNetPack 2.0 enhances atomistic machine learning with improved neural networks.
problem Improving atomistic machine learning methods and applications.
method Improved data pipeline, equivariant neural networks, PyTorch implementation, PyTorch Lightning, Hydra configuration framework.
result Easy extension and complex training tasks support.
Study improves machine learning for estimating survival treatment effects.
problem Estimating heterogeneous survival treatment effects in observational data.
method Flexible machine learning methods in the counterfactual framework, including AFT-BART-NP.
result AFT-BART-NP consistently yields best performance in terms of bias, precision, and frequentist coverage.
We propose some machine-learning-based algorithms to solve hedging problems in incomplete markets. Sources of incompleteness cover illiquidity, untradable risk factors, discrete hedging dates and transaction costs. The proposed algorithms resulting strategies are compared to classical stochastic control techniques on s…
MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio
problem Estimating ATT marginal hazard-ratio in externally controlled survival trials
method Machine-learning-assisted generalized entropy calibration for IPW Cox regression
result Reduces bias, increases efficiency, and improves coverage
Flexible nonstationary Gaussian process with neural network parameters.
problem Limited expressiveness of stationary Gaussian processes.
method Nonstationary kernels with neural network parameters trained jointly.
result Better accuracy and log-score compared to stationary and hierarchical models.
DoubleML is a Python library for causal inference using machine learning.
problem Estimating causal parameters in complex models with machine learning.
method Double machine learning framework for valid statistical inference.
result High flexibility and easy extension for various model specifications.
Pymc-learn is a Python package providing a variety of state-of-the-art probabilistic models for supervised and unsupervised machine learning. It is inspired by scikit-learn and focuses on bringing probabilistic machine learning to non-specialists. It uses a general-purpose high-level language that…
New method solves quantile crossing problem in econometrics.
problem Quantile crossing problem in quantile regression.
method Flexible check function approach.
result Eliminates or greatly reduces quantile crossing problem.
Two ML frameworks predict antibody properties using structural data.
problem Predicting antibody properties using sequence and structural data.
method ANTIPASTI and INFUSSE models using graph representations and neural networks.
result ANTIPASTI predicts binding affinity; INFUSSE predicts residue flexibility.
Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular pot…