arXiv research

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

Recently, it has been shown that many functions on sets can be represented by sum decompositions. These decompositons easily lend themselves to neural approximations, extending the applicability of neural nets to set-valued inputs---Deep Set learning. This work investigates a core component of Deep Set architecture: ag…

SACP aggregates nonconformity scores from multiple predictors to create more efficient uncertainty sets.

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

The paper examines risk aggregation under mixtures of marginals, finding that more homogeneous distributions lead to larger uncertainty.

We improve prediction set coverage by assigning weights to individual sets.

Aggregation challenges causal interpretation of IV estimators.

Active learning method reduces labeling cost for regression models with aggregated data.

SCHA-VAE generates novel data from limited examples using hierarchical context aggregation.

New algorithm for recommending best arms with aggregated feedback.

This work introduces COLA, a strategy to aggregate conformal prediction sets efficiently.

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

Improved defense against data poisoning attacks by aggregating smaller subsets.

The paper compares aggregated data labels in curated and random bags for machine learning models.

Improved local explainer aggregation for interpretable machine learning models.

This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Traditional rank aggregation methods are deterministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized. Surpri…

New LCM aggregator improves GNN performance and efficiency.

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

Estimates variance function using aggregation methods in regression models.

BOA improves financial forecasting by combining expert models.

CAOS aggregates multiple one-shot predictors for efficient uncertainty quantification.

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…

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…

Probabilistic graphical models are graphical representations of probability distributions. Graphical models have applications in many fields including biology, social sciences, linguistic, neuroscience. In this paper, we propose directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed procedure i…

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

New method aggregates bootstrapped DAGs for causal discovery.

We develop a secure aggregation protocol for federated learning that reduces communication and computation costs.

A conformal procedure improves CoT reasoning by aggregating reasoning paths and calibrating abstention rules.

COMA combines prediction sets from multiple models for online, adaptive prediction.

Unified approach to aggregating models and preferences.

Researchers improve deep ensemble forecast aggregation methods.

Calibrated models can lead to miscalibrated aggregations in strategic interactions.

Data aggregation improves HAC for resource-constrained systems.

Federated learning is the centralized training of statistical models from decentralized data on mobile devices while preserving the privacy of each device. We present a robust aggregation approach to make federated learning robust to settings when a fraction of the devices may be sending corrupted updates to the server…

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

Paper tightens statistical aggregation results using local complexity.

We establish dual representations for systemic risk measures based on acceptance sets in a general setting. We deal with systemic risk measures of both "first allocate, then aggregate" and "first aggregate, then allocate" type. In both cases, we provide a detailed analysis of the corresponding systemic acceptance sets …

The financial crisis showed the importance of measuring, allocating and regulating systemic risk. Recently, the systemic risk measures that can be decomposed into an aggregation function and a scalar measure of risk, received a lot of attention. In this framework, capital allocations are added after aggregation and can…

Optimal transport aggregation combines distributed MoE models efficiently.

In classification, the de facto method for aggregating individual losses is the average loss. When the actual metric of interest is 0-1 loss, it is common to minimize the average surrogate loss for some well-behaved (e.g. convex) surrogate. Recently, several other aggregate losses such as the maximal loss and average t…

CARE improves LLM aggregation by accounting for shared confounders.

Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how …

Unified approach for federated learning using MM optimization.

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

New algorithm predicts piecewise regular functions online.

Aggregation defenses improve deep learning models' robustness against data poisoning attacks.

We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggregate probability distribution over these events. Researchers have proposed many aggregation methods, although on the question of which is be…

Proposes a method for evaluating multiple dimensions of organizational effectiveness using DEA.