arXiv research

Partial fusion combines neural networks to balance accuracy and efficiency.

This paper compares rank aggregation methods for partial label ranking.

Develops GNNs for incomplete graphs, improving learning from missing node attributes.

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…

Aggregation distorts causal discovery results but recovery is possible with partial linearity or prior.

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

This paper addresses privacy in federated learning with wireless clients and base stations.

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…

FedAMD framework improves federated learning with partial client participation.

Study optimal policy regret in partially observable Markov games with adaptive opponents.

Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available, namely, the individual observed at one time may not be observed at the next time point, or the identity of individual is unavailable. This is in sharp contrast to learning dynamics w…

Study market efficiency under partial information using SDEs and optimization.

Framework benchmarks optimizers on multiple criteria.

Hierarchical Partial-Order Models for Ranking

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…

New method reconstructs data subsets from limited published statistics.

This paper introduces glocal explanations for expected goal models in soccer.

New method improves training stochastic neural networks with tighter guarantees.

Paper proposes a new method to aggregate multiple sources with different label distributions.

Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.

We report on results concerning a partially aggregated Stock Flow Consistent (SFC) macroeconomic model in the stationary state where the sectors of banks and firms are aggregated, the sector of households is dis-aggregated, and the probability density function (pdf) of the wealth of households is exogenous, constrained…

The paper addresses privacy in rank aggregation using randomized responses.

Paper tackles learning win-win solutions in aggregation systems.

We consider a large, homogeneous portfolio of life or disability annuity policies. The policies are assumed to be independent conditional on an external stochastic process representing the economic-demographic environment. Using a conditional law of large numbers, we establish the connection between claims reserving an…

Unified approach for federated learning using MM optimization.

Novel convex risk measures aggregate multiple uncertain sources for insurance firms.

FedAVOT improves federated learning by aligning user distributions.

New method uses geometric mean to avoid non-collapsibility in case-control studies.

Transforms ensemble predictions to maintain interpretability.

Enhanced GNN with expanded attention window and partially random embeddings.

Artemis framework improves distributed learning with bidirectional compression and partial participation.

Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random de…

CT-OT Flow estimates continuous-time dynamics from discrete snapshots.

PRISM-FCP improves federated prediction robustness against Byzantine attacks.

Model shows partial compliance can lead to less fair outcomes than expected.

The paper presents a framework for estimating treatment effects using partial network data.

Graph smoothing can improve learning performance by restoring lost information.

In recent years rank aggregation has received significant attention from the machine learning community. The goal of such a problem is to combine the (partially revealed) preferences over objects of a large population into a single, relatively consistent ordering of those objects. However, in many cases, we might not w…

Securely analyzes survival data across multiple institutions without revealing individual patient records.

Model optimizes trading strategy with unobservable toxicity.

New spectral tests assess network model fits efficiently.

HS-FNO models non-Markovian PDEs by learning history and future states.

Paper tackles group robustness with partially labeled data.

We consider the predictive problem of supervised ranking, where the task is to rank sets of candidate items returned in response to queries. Although there exist statistical procedures that come with guarantees of consistency in this setting, these procedures require that individuals provide a complete ranking of all i…

Solvency II Directive 2009/138/EC requires an insurance and reinsurance undertakings assessment of a Solvency Capital Requirement by means of the so-called "Standard Formula" or by means of partial or full internal models. Focusing on the first approach, the bottom-up aggregation formula proposed by the regulator permi…

A popular approach of achieving fairness in optimization problems is by constraining the solution space to "fair" solutions, which unfortunately typically reduces solution quality. In practice, the ultimate goal is often an aggregate of sub-goals without a unique or best way of combining them or which is otherwise only…

Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which …

Study Nash equilibrium between broker and informed trader in dealer and lit markets.