arXiv research

New algorithm for aggregate inference in HMMs with continuous observations.

A new algorithm estimates aggregate marginals from noisy data in an online manner.

Federated learning is protected against adversarial attacks with residual-based reweighting.

We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data. We apply the algorithm to common m…

New federated learning algorithms improve model aggregation robustness.

New algorithm for recommending best arms with aggregated feedback.

BOA improves financial forecasting by combining expert models.

The incremental aggregated gradient algorithm is popular in network optimization and machine learning research. However, the current convergence results require the objective function to be strongly convex. And the existing convergence rates are also limited to linear convergence. Due to the mathematical techniques, th…

The article is devoted to investigating the application of aggregating algorithms to the problem of the long-term forecasting. We examine the classic aggregating algorithms based on the exponential reweighing. For the general Vovk's aggregating algorithm we provide its generalization for the long-term forecasting. For …

In this paper, we consider the problem of "hyper-sparse aggregation". Namely, given a dictionary $F = \{f_1, ..., f_M \}$ of functions, we look for an optimal aggregation algorithm that writes $\tilde f = \sum_{j=1}^M θ_j f_j$ with as many zero coefficients $θ_j$ as possible. This problem is of particular interest when…

New algorithm for XMC from aggregated labels.

New algorithm for collective Gaussian hidden Markov models inference.

Unified framework for Byzantine robust gossip algorithms with guaranteed performance.

Many real world problems can now be effectively solved using supervised machine learning. A major roadblock is often the lack of an adequate quantity of labeled data for training. A possible solution is to assign the task of labeling data to a crowd, and then infer the true label using aggregation methods. A well-known…

The ability to adequately model risks is crucial for insurance companies. The method of "Copula-based hierarchical risk aggregation" by Arbenz et al. offers a flexible way in doing so and has attracted much attention recently. We briefly introduce the aggregation tree model as well as the sampling algorithm proposed by…

New algorithm predicts piecewise regular functions online.

We prove a lower bound for feature dimension in linear MDPs and propose a novel dynamics aggregation framework.

Proposes MamBO for efficient high-dimensional large-scale optimization.

Data aggregation improves HAC for resource-constrained systems.

New algorithm for efficient inference over tree-structured graphs.

We introduce a new family of minmax rank aggregation problems under two distance measures, the Kendall τ and the Spearman footrule. As the problems are NP-hard, we proceed to describe a number of constant-approximation algorithms for solving them. We conclude with illustrative applications of the aggregation methods on…

State aggregation is a popular model reduction method rooted in optimal control. It reduces the complexity of engineering systems by mapping the system's states into a small number of meta-states. The choice of aggregation map often depends on the data analysts' knowledge and is largely ad hoc. In this paper, we propos…

The article improves prediction by aggregating Kalman recursions online.

Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation. We study the problem of unsupervised rank aggregation where no ground tr…

The paper develops a method for forecasting power consumption at various levels of aggregation.

Unified approach for federated learning using MM optimization.

We consider the problem of learning convex aggregation of models, that is as good as the best convex aggregation, for the binary classification problem. Working in the stream based active learning setting, where the active learner has to make a decision on-the-fly, if it wants to query for the label of the point curren…

Non-affine aggregation rules cannot preserve monotonicity in convex learning.

FedBuff improves federated learning scalability with asynchronous updates.

Estimating statistical uncertainties allows autonomous agents to communicate their confidence during task execution and is important for applications in safety-critical domains such as autonomous driving. In this work, we present the uncertainty-aware imitation learning (UAIL) algorithm for improving end-to-end control…

Proposes a novel MTL approach based on bias-variance analysis.

Paper presents a novel approach for global feature aggregation in Graph Neural Networks.

Study online learning in MDPs with aggregate bandit feedback, achieving low regret in both stochastic and adversarial settings.

Two new algorithms reduce feature space while preserving non-linear relationships.

The paper introduces a method to control false splits in tree-based data aggregation.

Improved local explainer aggregation for interpretable machine learning models.

FedBE aggregates local models into a robust global model via Bayesian inference.

MEVA aggregates model predictions to improve accuracy without needing model details.

We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold $θ$, with a fixed budget of $T$ trials. We introduce LSA, a new, simple and anytime algorithm that aims to minimize the aggregate regret (or the expected number of mis-classified arms). We prove that our algo…

A simple algorithm improves model generalization in expert advice settings.

Federated edge learning improves with CSIT-free model aggregation using RIS.

Multidimensional data have become ubiquitous and are frequently encountered in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location. We often have access to multiple aggregated views of the same data, each aggregated i…

U-aggregation combines multiple models without labels for better risk prediction.

We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L1-norm error fitting model with an assumption of the fitting function. The proposed algorithm generalizes the recent algorithm in the literature, aggregate and iterative disaggregate (AID), whi…

We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Lu…

Algorithm for online decision making with unknown dynamics and aggregate feedback.

A general graph-structured neural network architecture operates on graphs through two core components: (1) complex enough message functions; (2) a fixed information aggregation process. In this paper, we present the Policy Message Passing algorithm, which takes a probabilistic perspective and reformulates the whole inf…

Improves predictive algorithm performance with domain adaptation.