arXiv research

A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.

Paper studies fundamental limits of communication in distributed learning.

k-Rater reliability corrects under-reporting of aggregated data reliability.

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 …

The paper introduces a method to control false splits in tree-based data aggregation.

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

The paper addresses privacy in rank aggregation using randomized responses.

In this work, we study the problem of aggregating a finite number of predictors for nonstationary sub-linear processes. We provide oracle inequalities relying essentially on three ingredients: (1) a uniform bound of the $\ell^1$ norm of the time varying sub-linear coefficients, (2) a Lipschitz assumption on the predict…

New method improves CATE model selection with optimal regret rates.

We introduce an alternative to the notion of `fast rate' in Learning Theory, which coincides with the optimal error rate when the given class happens to be convex and regular in some sense. While it is well known that such a rate cannot always be attained by a learning procedure (i.e., a procedure that selects a functi…

Copula models for sovereign ratings improved by incorporating climate risk.

The incremental aggregated gradient algorithm is popular in network optimization and machine learning research. However, the current convergence results require the objective function to be strongly convex. And the existing convergence rates are also limited to linear convergence. Due to the mathematical techniques, th…

FedBuff improves federated learning scalability with asynchronous updates.

Model infers functions for attributes using multi-aggregate datasets with knowledge transfer.

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

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

New methods reduce private federated learning communication automatically.

The aim of this paper is to provide some theoretical understanding of quasi-Bayesian aggregation methods non-negative matrix factorization. We derive an oracle inequality for an aggregated estimator. This result holds for a very general class of prior distributions and shows how the prior affects the rate of convergenc…

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

Calibrated models can lead to miscalibrated aggregations in strategic interactions.

Crowdsourcing has become an effective and popular tool for human-powered computation to label large datasets. Since the workers can be unreliable, it is common in crowdsourcing to assign multiple workers to one task, and to aggregate the labels in order to obtain results of high quality. In this paper, we provide finit…

AKO improves stability and power of Knockoff inference.

Relational probabilistic models have the challenge of aggregation, where one variable depends on a population of other variables. Consider the problem of predicting gender from movie ratings; this is challenging because the number of movies per user and users per movie can vary greatly. Surprisingly, aggregation is not…

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 …

Adaptive Bayesian learning aggregates experts to improve performance.

Graph edges, along with their labels, can represent information of fundamental importance, such as links between web pages, friendship between users, the rating given by users to other users or items, and much more. We introduce LEAP, a trainable, general framework for predicting the presence and properties of edges on…

Robust VB framework handles contamination using min-max median aggregation.

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

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 …

FedNNNN improves FL by adjusting model update vector norms.

Study on convergence of graph neural networks on random graphs.

We consider Feller mean-reverting square-root diffusion, which has been applied to model a wide variety of processes with linearly state-dependent diffusion, such as stochastic volatility and interest rates in finance, and neuronal and populations dynamics in natural sciences. We focus on the statistical mixing (or sup…

Algorithm for online decision making with unknown dynamics and aggregate feedback.

Study benchmarks label noise detection methods, identifying best practices.

New aggregation methods improve robustness and efficiency in distributed learning.

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-…

We review the production function and the hypothesis of equilibrium in the neoclassical framework. We notify that in a soup of sectors in economy, while capital and labor resemble extensive variables, wage and rate of return on capital act as intensive variables. As a result, Baumol and Bowen's statement of equal wages…

LASG improves communication efficiency in distributed learning.

Federated Learning speeds up speech recognition training by 7x and reduces error rate by 6%.

The paper optimizes k-NN for distributed learning with minimax optimal performance.

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

The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication. The key idea is to first quantize the computed gradients, and then skip less informative quantized gradient communications by reusing outdated gradients. Quantizing and…

New bounds on generalization error for distributed learning using rate-distortion theory.

Italian banks use swaps to hedge against rising interest rates, offsetting losses on debt securities.

Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting, which assigns a sample to the class that most base classifiers vote for. However, improved performance can be obtained by assigning weights…

We propose a communication-efficient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size $N$ into $m$ machines, and estimates a local sparse LDA estimator on each machine using the data subset of size $N/m$. After the distri…

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

Siegel's paradox is a fundamental question in international finance about exchange rates for futures contracts and has puzzled many scholars for over forty years. The unorthodox approach presented in this article leads to an arbitrage-free solution which is invariant under currency re-denominations and is symmetric, as…