arXiv research

Proposes MEDM to balance entropy minimization and diversity maximization for better domain adaptation.

New technique prevents Q-learning collapse by maximizing diversity among ensembles.

Kernel sparsity ("dying ReLUs") and lack of diversity are commonly observed in CNN kernels, which decreases model capacity. Drawing inspiration from information theory and wireless communications, we demonstrate the intersection of coding theory and deep learning through the Grassmannian subspace packing problem in CNN…

The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method for maximizing the u…

New method learns diverse solutions in reinforcement learning without gradient bias.

The inaccuracy of neural network models on inputs that do not stem from the training data distribution is both problematic and at times unrecognized. Model uncertainty estimation can address this issue, where uncertainty estimates are often based on the variation in predictions produced by a diverse ensemble of models …

A method to improve image synthesis diversity using mutual information.

iRDM selects unlabeled samples for regression without labels, improving model accuracy.

ODS improves adversarial attacks by maximizing output diversity.

Maximizes coding rate difference for robust, discriminative features.

SEMF predicts prediction intervals for ML models using latent variables.

Proposes method to discover diverse near-optimal policies in reinforcement learning.

A new method for diverse Pareto solutions in multi-objective learning.

Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples in the multi-c…

DiwE uses regional distribution changes to create diverse ensemble classifiers for concept drift.

We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such algorithms in terms of the `total curvature'. We also show that by adj…

Quality-Diversity algorithms explore multiple high-performing solutions in a search space.

DMNL bandits optimize assortment choices balancing relevance and diversity.

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…

To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposals. In structured prediction problems, this becomes a daunting task, as the solution space (image label…

Proposes qPO, a new acquisition strategy for batched Bayesian optimization that maximizes the probability of including the optimum.

Designs efficient algorithms to maximize the expectation of Gaussian random variables.

Paper uses Bayesian optimization to find best Supertrend indicator settings.

The paper explores multidimensional critic output in GANs, improving convergence and diversity.

There are many problems in machine learning and data mining which are equivalent to selecting a non-redundant, high "quality" set of objects. Recommender systems, feature selection, and data summarization are among many applications of this. In this paper, we consider this problem as an optimization problem that seeks …

Improved uncertainty estimation through diverse sampling in neural networks.

Scalable methods for maximizing regularized submodular functions with improved memory and communication complexity.

Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties. In order to protect organizations database DAM systems have two main roles, monitoring (documenting activity) and alerting to anomalous activity. Due to high-velocity …

This paper presents a methodology and workflow that overcome the limitations of the conventional Generative Adversarial Networks (GANs) for geological facies modeling. It attempts to improve the training stability and guarantee the diversity of the generated geology through interpretable latent vectors. The resulting s…

We address the problem of maximizing an unknown submodular function that can only be accessed via noisy evaluations. Our work is motivated by the task of summarizing content, e.g., image collections, by leveraging users' feedback in form of clicks or ratings. For summarization tasks with the goal of maximizing coverage…

DIGEN benchmark provides synthetic datasets for ML algorithm evaluation.

DAC enhances exploration in reinforcement learning with entropy regularization.

Several fundamental problems that arise in optimization and computer science can be cast as follows: Given vectors $v_1,\ldots,v_m \in \mathbb{R}^d$ and a constraint family ${\cal B}\subseteq 2^{[m]}$, find a set $S \in \cal{B}$ that maximizes the squared volume of the simplex spanned by the vectors in $S$. A motivatin…

Social learning can make financial markets inefficient, but individual learning can fix this.

The paper tackles budget allocation for multiple campaigns using a novel combinatorial bandit approach.

In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To ta…

We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In prior works, such information is used to compute influence probabilities, which is utilized by stochastic diffusion models in influence maximization. Motivated by the recent criticism on the effectiveness o…

Novel methods generate diverse policies in reinforcement learning.

Framework improves agent's ability to learn from noisy images.

Fine-tunes diffusion models to generate diverse samples with high genuine rewards.

Proposes a meta-learning method for robust portfolio optimization.

Algorithm learns diverse rankings for search engines.

A determinantal point process (DPP) is a probabilistic model of set diversity compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we would like to learn the entries of its kernel matrix by maximizing the log-likelihood of the available data. However, log-likelihood is non-co…

Relevance ranking and result diversification are two core areas in modern recommender systems. Relevance ranking aims at building a ranked list sorted in decreasing order of item relevance, while result diversification focuses on generating a ranked list of items that covers a broad range of topics. In this paper, we s…

Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically unsupervised. To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network…

MetaTrader combines diverse expert strategies to optimize portfolio performance.

A novel method integrates feature and topology views for unsupervised graph representation learning.

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning ap…