A new method for forming learning objectives using the sum of ranked range.
problem Forming learning objectives from aggregated values.
method Sum of ranked range (SoRR) minimization with DCA.
result The proposed method effectively forms learning objectives and is applicable to binary and multi-label/multi-class classification.
New LCM aggregator improves GNN performance and efficiency.
problem Graph neural networks' sensitivity to aggregation function choice.
method Learnable commutative monoid for graph aggregation.
result LCM aggregator achieves performance competitive with recurrent aggregators.
This paper studies convergence properties of multivariate distributions constructed by endowing empirical margins with a copula. This setting includes Latin Hypercube Sampling with dependence, also known as the Iman--Conover method. The primary question addressed here is the convergence of the component sum, which is r…
A system for federated learning with private data, adding discrete Gaussian noise and secure aggregation.
problem Training models on private data distributed across devices while ensuring privacy.
method Discretizes data, adds discrete Gaussian noise, and uses secure aggregation to protect privacy.
result Matches the accuracy of central differential privacy with less than 16 bits of precision per value.
New method preserves privacy by aggregating feature-vectors with weighted sums, ensuring label differential privacy.
problem Ensuring privacy in training data aggregation for sensitive labels.
method Learning from bag aggregates (LBA) with weighted Gaussian sums, preserving label differential privacy (label-DP).
result Weighted LBA using iid Gaussian weights with m randomly sampled disjoint k-sized bags provides (ε,δ)-label-DP. New insights into tail behavior of heavy-tailed random vectors and processes.
problem Understanding tail behavior of aggregates of heavy-tailed random vectors.
method Analyzing multivariate regularly varying random vectors and Lévy processes.
result More than one large jump can determine tail behavior of aggregates.
Introduces SoRR for aggregating losses in supervised learning.
problem Aggregating individual losses into a single output for machine learning models.
method Sum of ranked range (SoRR) minimization using DCA.
result Demonstrates effectiveness of AoRR and TKML in improving robustness of multi-label learning.
This work examines aggregation functions in Deep Set learning.
problem The sensitivity of Deep Set networks to aggregation function choices.
method Investigation of alternative aggregation functions, including learnable recurrent ones.
result Learnable aggregations improve performance, reduce hyper-parameter sensitivity, and generalize better.
Paper studies fundamental limits of communication in distributed learning.
problem Communication efficiency in model aggregation for distributed learning.
method Rate-Distortion approach to model aggregation as a vector Gaussian CEO problem.
result Derives rate region bound and sum-rate-distortion function for model aggregation.
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…
Proposes a new optimization-based method for aggregating sets in neural networks.
problem Limited representational power of existing aggregation methods.
method Equilibrium Aggregation: an optimization-based approach.
result Equilibrium Aggregation outperforms existing methods in various tasks.
The paper introduces a tensor-based approach to improve neural models' aggregation of structural context.
problem Sub-optimal use of simple aggregation functions in neural models for structured data.
method Tensor-based formulation and Tucker tensor decomposition to control parameter space size.
result Effective regulation of trade-off between expressivity, computational complexity, and generalisation.
Study tail behavior of sum of heavy-tailed risks with copulas.
problem Analyzing the tail behavior of sums of heavy-tailed risks with dependence modeled by copulas.
method Modeling dependence with copulas and analyzing tail asymptotics of sums of heavy-tailed risks.
result Obtained asymptotic expansions for Value-at-Risk of aggregate risk.
New bounds for quantile aggregation unify and clarify existing methods.
problem Analytical bounds for quantile aggregation with dependence uncertainty.
method Using inf-convolution of quantile-based risk measures, establish new analytical bounds called convolution bounds.
result Convolution bounds are the best available and provide sharp results in many cases.
Optimal Transport Graph Neural Networks (OT-GNN) improves graph embeddings by using optimal transport.
problem Graph Neural Networks (GNN) often lose structural or semantic information when aggregating node embeddings.
method Combines optimal transport (OT) with parametric graph models to compute graph embeddings from Wasserstein distances between node embeddings and prototype point clouds.
result OT-GNN outperforms popular methods on molecular property prediction tasks and produces smoother graph representations.
Paper proves Pareto efficient insurance for multiple entities.
problem Optimizing insurance for multiple policyholders and insurers.
method Sum-minimization characterization and pairwise implementability analysis.
result Characterization of Pareto efficient insurance arrangements.
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descen…
PNA improves GNNs for graph data with multiple aggregators.
problem Capturing continuous features in graph neural networks.
method Combines multiple aggregators with degree-scalers.
result PNA outperforms existing models on graph theory and real-world tasks.
Fishnets improve set and graph learning with scalable, robust aggregation.
problem Learning informative embeddings for sets and graphs with scalable and robust aggregation.
method Proposes Fishnets, a new aggregation strategy for set-based learning.
result Fishnets achieve state-of-the-art performance on graph datasets with fewer parameters and faster training.
In this paper, we consider the problem of "hyper-sparse aggregation". Namely, given a dictionary F={f1,...,fM} of functions, we look for an optimal aggregation algorithm that writes f~=∑j=1Mθjfj with as many zero coefficients θj as possible. This problem is of particular interest when…
New aggregation method improves GNN robustness to structural perturbations.
problem Graph Neural Networks (GNNs) are vulnerable to adversarial attacks that manipulate graph structure.
method Proposes a robust aggregation function with a breakdown point of 0.5, inspired by robust statistics.
result Improves GNN robustness by a factor of 3 on Cora ML and 5.5 on Citeseer, and 8 for low-degree nodes.
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
Optimizes non-linear outcomes from summed contributions.
problem Maximizing a non-linear function of summed small contributions.
method Derives a scalable descent algorithm leveraging concentration properties.
result Directly optimizes for stated objective, e.g., A/B test success criterion.
Introduces a new price measure and a second-order economic theory for volatility forecasting.
problem Forecasting price volatility in financial markets.
method Develops a new price measure and a second-order economic theory to model price volatility.
result Shows that second-order economic theory improves forecasting of price volatility.
It is well known that a random vector with given marginal distributions is comonotonic if and only if it has the largest sum with respect to the convex order [ Kaas, Dhaene, Vyncke, Goovaerts, Denuit (2002), A simple geometric proof that comonotonic risks have the convex-largest sum, ASTIN Bulletin 32, 71-80. Cheung (2…
We propose an approach to the aggregation of risks which is based on estimation of simple quantities (such as covariances) associated to a vector of dependent random variables, and which avoids the use of parametric families of copulae. Our main result demonstrates that the method leads to bounds on the worst case Valu…
Unified framework for Byzantine robust gossip algorithms with guaranteed performance.
problem Vulnerability of decentralized machine learning to misbehaving devices.
method Introduces F-RG framework and CS+ robust aggregation rule for Byzantine resilience.
result CS+-RG has near-optimal breakdown tolerance and outperforms existing methods.
LPF provides formal guarantees for aggregating multi-evidence in probabilistic tasks.
problem Lack of formal guarantees for multi-evidence reasoning in AI.
method LPF uses variational autoencoders and Sum-Product Networks to aggregate evidence items.
result Proves multiple formal guarantees including calibration preservation and error decay.
GEM detects malicious accounts using adaptive embeddings from heterogeneous graphs.
problem Detecting malicious accounts on a leading mobile payment platform.
method Adaptive learning of discriminative embeddings from heterogeneous account-device graphs with attention mechanism for node importance.
result GEM consistently outperforms competitive methods in detecting malicious accounts.
Paper optimizes graph neural networks for better structural graph classification.
problem Improving graph neural networks for structural graph classification.
method Focus on aggregation functions, specifically sum and histogram-based functions, to enhance discrimination.
result Design of a graph neural network that learns discriminative graph representations.
We study a variant of the stochastic K-armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed. Instead, at the end of each round the player observes only the sum of a number…
We analyze a fast incremental aggregated gradient method for optimizing nonconvex problems of the form minx∑ifi(x). Specifically, we analyze the SAGA algorithm within an Incremental First-order Oracle framework, and show that it converges to a stationary point provably faster than both gradient descent and s…
The paper examines how small positive dependence can lead to correlated tail risks.
problem Understanding the impact of dependence uncertainty on tail risk measures.
method Introducing a regular dependence measure and analyzing the aggregation of risks.
result Small positive dependence can result in perfectly correlated tail risks.
Proposes a new risk model using stable laws to manage company-wide losses.
problem Managing aggregate risks and pricing policies in the presence of systematic risk.
method Develops a modified risk model using multivariate stable distributions to account for various risk phenomena.
result Computes the Tail Conditional Expectation of aggregate risks and corresponding allocations.
The paper compares aggregated data labels in curated and random bags for machine learning models.
problem Protecting user privacy in machine learning systems with aggregated data.
method Examined curated and random bags for training machine learning models and compared their performance.
result Gradient-based learning can be performed on aggregated data without performance degradation.
We empirically test the effects of unanticipated fiscal policy shocks on the growth rate and the cyclical component of real private output and reveal different types of asymmetries in fiscal policy implementation. The data used are quarterly U.S. observati ons over the period 1967:1 to 2011:4. In doing so, we use both …
New protocol makes federated learning more scalable and private.
problem Securely aggregate data from distributed, private datasets.
method Proposes a new protocol for aggregation in the shuffled model that is more efficient in terms of communication and error.
result Achieves differential privacy guarantees with polylogarithmic scaling in the number of users.
Bayesian model uses simple functions to forecast macroeconomic data.
problem Forecasting large datasets in macroeconomics with complex nonlinear relationships.
method Sum of simple two-component location mixtures, logistic function threshold, conjugate priors.
result Accurate point and density forecasts in US macroeconomic aggregates.
Paper studies second order tail probabilities in risk models.
problem Analyzing tail probabilities in risk models with constant interest force.
method Asymptotic expansion and weighted Kesten-type inequality for second order subexponential random variables.
result Second order asymptotic formulae for continuous-time renewal risk models are derived.
Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data. Based on the recent DeepSets architecture proposed by Zaheer et al. (NIPS 2017), we introduce a Set Aggregation Network (SAN) as an alternative…
In this paper, we study the accuracy of values aggregated over classes predicted by a classification algorithm. The problem is that the resulting aggregates (e.g., sums of a variable) are known to be biased. The bias can be large even for highly accurate classification algorithms, in particular when dealing with class-…
This paper tackles hidden state inference for HMMs using particle filtering.
problem Inference for hidden states under HMMs is challenging due to unavailable true labels.
method Adaptive conformal inference framework using particle filtering.
result The framework produces prediction sets with specific aggregated coverage levels.
pFedGame uses game theory for decentralized federated learning in dynamic networks.
problem Performance bottlenecks, data bias, model convergence issues, and model poisoning attacks in federated learning.
method pFedGame employs game theory to decentralize federated learning, avoiding a central aggregation server and addressing dynamic network challenges.
result pFedGame achieves higher accuracy (over 70%) in heterogeneous data compared to existing methods.
The benefits of diversifying risks are difficult to estimate quantitatively because of the uncertainties in the dependence structure between the risks. Also, the modelling of multidimensional dependencies is a non-trivial task. This paper focuses on one such technique for portfolio aggregation, namely the aggregation o…
Our work specifies the fundamental cost of using secure aggregation in federated learning.
problem Training a distributed model with differential privacy constraints.
method Characterized the communication cost required for optimal accuracy under differential privacy, achieved by a linear scheme.
result The fundamental communication cost is $ ilde{O}\left( \min(n^2\varepsilon^2, d)
ight)$ bits per client, both sufficient and necessary.
Method learns CTMC models from steady-state data, predicting unseen states.
problem Learning CTMC models from aggregate steady-state statistics without sequence examples.
method ∞-SGD, a stochastic gradient descent method that avoids infinite sums.
result Successfully learns CTMC models and predicts unseen states.
Sharp bounds found for various risk measures using generalized FGM copulas.
problem Finding sharp bounds for risk measures in high dimensions.
method Proved that generalized FGM copulas form a convex polytope, used this structure to find bounds for risk measures.
result Sharp analytical bounds for convex risk measures in the class of generalized FGM copulas.
Paper introduces REED for noncoherent OTA-FL, reducing latency without phase alignment.
problem Noncoherent OTA-FL requires signed model updates without phase alignment.
method Introduces REED for continuous signed aggregation using resource-element energy difference.
result Exact variance laws for REED and chip-diverse extension in Rayleigh fading.