arXiv research

This work expands state-action aggregation methods for non-Markovian environments.

This work tackles large action spaces in RL by binarizing actions.

Early-stopped aggregation improves computational efficiency in adaptive statistical inference.

New aesthetic curves in equiaffine geometry include the quadratic and logarithmic spiral.

Objective: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. …

The paper calculates VaR and CTE for extreme and aggregate risks using FGM copula.

Paper introduces weak comonotonicity for more realistic extreme dependence scenarios.

The purpose of this note is to present several criteria for essential self-adjointness. The method is based on ideas due to Shubin. This note is divided into two parts. The first part deals with symmetric first order systems on the line in the most general setting. Such a symmetric first order system of differential eq…

New algorithm for XMC from aggregated labels.

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks by the average value and generalise Spearman's ρto extreme ranks. Our main …

Paper tackles learning win-win solutions in aggregation systems.

Paper proposes an algorithm to estimate state aggregation from Markov transition data.

New bounds for quantile aggregation unify and clarify existing methods.

Optimized Q-learning reduces regret in state-aggregated MDPs.

New aggregation method improves GNN robustness to structural perturbations.

Paper finds robust $Λ$-quantiles equal to extremal distributions.

FLANDERS detects and blocks extreme model poisoning in federated learning.

Aggregated variables can mask causal effects, turning unconfounded into confounded relations.

In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate …

We study the topological properties of the multinetwork of commodity-specific trade relations among world countries over the 1992-2003 period, comparing them with those of the aggregate-trade network, known in the literature as the international-trade network (ITN). We show that link-weight distributions of commodity-s…

Policy gradient methods with aggregated states can achieve better performance than approximate policy iteration.

Turbo-Aggregate reduces secure aggregation time from quadratic to nearly linear.

Semi-supervised node classification in attributed graphs, i.e., graphs with node features, involves learning to classify unlabeled nodes given a partially labeled graph. Label predictions are made by jointly modeling the node and its' neighborhood features. State-of-the-art models for node classification on such attrib…

Improves PU learning for imbalanced data with practical AUL estimation and new training method.

The article improves prediction by aggregating Kalman recursions online.

New model improves inference on asset market durations.

Model reduction of Markov processes is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a discrete-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we study…

We study cross-country GDP losses due to financial crises in terms of frequency (number of loss events per period) and severity (loss per occurrence). We perform the Loss Distribution Approach (LDA) to estimate a multi-country aggregate GDP loss probability density function and the percentiles associated to extreme eve…

This paper optimizes MDP policies for efficient state aggregation.

In this article we show the relationship between the Pareto distribution and the gamma distribution. This shows that the second one, appropriately extended, explains some anomalies that arise in the practical use of extreme value theory. The results are useful to certain phenomena that are fitted by the Pareto distribu…

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

Method learns CTMC models from steady-state data, predicting unseen states.

Compact satellite tech detects flooded areas quickly.

Extends effect variable concept to finite states for web search evaluation.

In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregatio…

Paper proposes a federated XGBoost for anomaly detection balancing privacy and accuracy.

A mechanism to share risks and costs with guarantees against extreme outcomes.

UAIL uses uncertainty estimation to improve control systems in safety-critical tasks.

Study on existence of ground states for free energy on hyperbolic space.

We study minority games in efficient regime. By incorporating the utility function and aggregating agents with similar strategies we develop an effective mesoscale notion of state of the game. Using this approach, the game can be represented as a Markov process with substantially reduced number of states with explicitl…

This work improves privacy in the shuffle model for summing real numbers.

HAXMLNet tackles extreme multi-label text classification with hierarchical attention.

Enhances DenseNets with multi-scale convolutions and stochastic feature reuse.

Framework for Granger causality in extreme events.

Paper develops a method to approximate Markov chains with fewer states.

New algorithm for aggregate inference in HMMs with continuous observations.

We investigate the properties of a modulus of a foliation on a Riemannian manifold. We give necessary and sufficient conditions for the existence of an extremal function and state some of its properties. We obtain the integral formula which, in a sense, combines the integral over the manifold with integral over the lea…

This paper tackles hidden state inference for HMMs using particle filtering.