arXiv research

Proposes a new optimization-based method for aggregating sets in neural networks.

Optimal fuzzy classification aggregation functions are weighted means.

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…

We consider the forecast aggregation problem in repeated settings, where the forecasts are done on a binary event. At each period multiple experts provide forecasts about an event. The goal of the aggregator is to aggregate those forecasts into a subjective accurate forecast. We assume that experts are Bayesian; namely…

This work analyzes two methods for combining multiple binary labels in bipartite ranking.

Fishnets improve set and graph learning with scalable, robust aggregation.

This paper solves aggregation of Pareto optimal models by using Bayesian priors and weighted averaging.

Given a finite family of functions, the goal of model selection aggregation is to construct a procedure that mimics the function from this family that is the closest to an unknown regression function. More precisely, we consider a general regression model with fixed design and measure the distance between functions by …

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…

This paper optimizes MDP policies for efficient state aggregation.

Improved local explainer aggregation for interpretable machine learning models.

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

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

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

Policy-GNN optimizes GNN aggregation for diverse node iterations.

Optimal transport aggregation combines distributed MoE models efficiently.

Majority Vote is optimal for reliable data labeling under certain conditions.

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

Paper optimizes demand aggregation for low-level electricity markets.

We introduce a new recursive aggregation procedure called Bernstein Online Aggregation (BOA). The exponential weights include an accuracy term and a second order term that is a proxy of the quadratic variation as in Hazan and Kale (2010). This second term stabilizes the procedure that is optimal in different senses. We…

Let $\cF$ be a set of $M$ classification procedures with values in $[-1,1]$. Given a loss function, we want to construct a procedure which mimics at the best possible rate the best procedure in $\cF$. This fastest rate is called optimal rate of aggregation. Considering a continuous scale of loss functions with various …

Partial fusion combines neural networks to balance accuracy and efficiency.

Study optimal stopping for group with diverse discount rates using an attitude function.

A new method for faster prediction in distributed Gaussian processes.

Paper analyzes sparse aggregation in GLMs with Kullback-Leibler risk bounds.

We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-conv…

Sharp bounds found on expert error in binary advice aggregation.

New algorithm for efficient inference over tree-structured graphs.

Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks. Existing GCNs usually conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors which is biased by…

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 …

Optimal risk sharing without convex preferences using aggregate convexity.

New approach for prudent risk evaluation using model aggregation.

Calibrated models can lead to miscalibrated aggregations in strategic interactions.

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

AEW estimator achieves optimal risk in expectation for large enough temperatures.

New aggregation strategy handles unbounded losses with regret bounds.

Two approaches integrate qualitative views into portfolio optimization, showing aggregation methods outperform robust optimization.

The paper addresses privacy in rank aggregation using randomized responses.

Unified approach for federated learning using MM optimization.

New algorithm for recommending best arms with aggregated feedback.

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

Optimal Transport Graph Neural Networks (OT-GNN) improves graph embeddings by using optimal transport.

Study improves forecasting of aggregated curves in electricity markets.

Most real-world problems have huge state and/or action spaces. Therefore, a naive application of existing tabular solution methods is not tractable on such problems. Nonetheless, these solution methods are quite useful if an agent has access to a relatively small state-action space homomorphism of the true environment …

A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.

Paper tightens statistical aggregation results using local complexity.

FedAVOT improves federated learning by aligning user distributions.

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…