arXiv research

Ensembles of neural networks learn better by sharing information.

We describe and extract time-ordered multibody interactions from complex systems.

CREIMBO models diverse brain activity by identifying hidden neural sub-circuits and their non-stationary interactions.

Enhanced ensemble filters use machine learning to improve accuracy in filtering models.

A new machine learning framework called machine collaboration improves prediction accuracy.

Bayesian inference for neural networks improves uncertainty quantification.

Unified theory linking Bayesian and ensemble methods in deep learning.

Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks. Many efforts have been devoted to enhancing the robustness of individual networks and then constructing a straightforward ensemble…

This paper describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble, produced by bagging multiple trees. The process of bagging and combining results from multiple trees, produces numerous diagnostics which, with interactiv…

GER learns particle dynamics from unpaired snapshots using physics-informed GANs.

iLOCO measures feature interactions without assumptions, providing statistical inference.

New method models aptamer libraries as Boltzmann-weighted graph ensembles for better affinity predictions.

New method reduces ensemble size for linear bandits, achieving near optimal regret.

Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model)…

Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods fo…

Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based le…

We propose a method to extract interpretable rules from tree ensembles.

Renormalization in neural networks linked to quantum field theory.

The paper addresses interpretability issues in EBM models by improving feature selection and reducing spurious interactions.

A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.

The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functi…

New ensemble models classify mouse movement trajectories to assess survey question difficulty.

Paper tackles noisy and expensive likelihoods in complex models.

Paper proposes an ensemble-based AIS for multimodal sampling.

In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this prob…

Fibonacci Ensembles use Fibonacci weights to improve ensemble learning, inspired by natural growth patterns.

Study symmetry breaking in quantum mechanics to understand many-body physics.

Existing information-theoretic frameworks based on maximum entropy network ensembles are not able to explain the emergence of heterogeneity in complex networks. Here, we fill this gap of knowledge by developing a classical framework for networks based on finding an optimal trade-off between the information content of a…

Estimates interactions between modalities for multimodal data.

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural langu…

Ensemble decoders to capture latent space topology in deep generative models.

We build a statistical ensemble representation of two economic models describing respectively, in simplified terms, a payment system and a credit market. To this purpose we adopt the Boltzmann-Gibbs distribution where the role of the Hamiltonian is taken by the total money supply (i.e. including money created from debt…

Proposes an autonomous IDS using multiple learning techniques.

It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task. We present a novel method, Selective Bayesian Forest Classifie…

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for obtaining tree ensembles are based on versions of boosting or Random Forest. Previous work showed that boosting algorithms exhibit a cyclic b…

Estimates network structure and interaction rules from multiple agent trajectories.

MetaStackVis aids in choosing better metamodels for stacking ensembles.

Unsupervised ensemble learning has long been an interesting yet challenging problem that comes to prominence in recent years with the increasing demand of crowdsourcing in various applications. In this paper, we propose a novel method-- unsupervised ensemble learning via Ising model approximation (unElisa) that combine…

ProxySHAP approximates Shapley and Banzhaf interactions efficiently.

TreeHFD algorithm explains tree ensemble models through hierarchical orthogonality.

We present an extension of the ergodic, mixing, and Bernoulli levels of the ergodic hierarchy for statistical models on curved manifolds, making use of elements of the information geometry. This extension focuses on the notion of statistical independence between the microscopical variables of the system. Moreover, we e…

Unbiased wealth exchanges always lead to inequality.

This work uses decision trees to encode relevant features and their interactions into neural networks, improving model performance.

New distributed EnKF method for non-sequential assimilation of large datasets.

AdaEnsemble learns adaptive feature interactions for CTR prediction.

Study explores cyberbullying datasets and classifier generalization.

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order in…

Interpreting predictions from tree ensemble methods such as gradient boosting machines and random forests is important, yet feature attribution for trees is often heuristic and not individualized for each prediction. Here we show that popular feature attribution methods are inconsistent, meaning they can lower a featur…