arXiv research

Unified approach to aggregating models and preferences.

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…

BOA improves financial forecasting by combining expert models.

In order to improve forecasts, a decisionmaker often combines probabilities given by various sources, such as human experts and machine learning classifiers. When few training data are available, aggregation can be improved by incorporating prior knowledge about the event being forecasted and about salient properties o…

In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions. We restrict ourselves to the case where expert predictions come from Kalman recursions, fitting state-space models. By using exponential weights, we construct diffe…

New method aggregates Gaussian experts by detecting conditional independence violations.

A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.

A simple algorithm improves model generalization in expert advice settings.

Sharp bounds found on expert error in binary advice aggregation.

A new framework uses deep RL to aggregate expert advice for better portfolio management.

New algorithm predicts piecewise regular functions online.

We develop the setting of sequential prediction based on shifting experts and on a "smooth" version of the method of specialized experts. To aggregate experts predictions, we use the AdaHedge algorithm, which is a version of the Hedge algorithm with adaptive learning rate, and extend it by the meta-algorithm Fixed Shar…

A new method for faster prediction in distributed Gaussian processes.

Optimal transport aggregation combines distributed MoE models efficiently.

New method forecasts time series with changing variances.

Synthesizes robust estimators for domain adaptation.

A new method for sparse Gaussian process regression using correlated experts.

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…

In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregatio…

Algorithm learns expert weights to minimize regret in adversarial setting.

This work improves multi-modal generative models by using permutation-invariant neural networks.

Study online monotone density estimation with expert aggregation and log-optimal calibration.

We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (1997) and an adaptation of fixed-share rules of He…

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

The policy objective of safeguarding financial stability has stimulated a wave of research on systemic risk analytics, yet it still faces challenges in measurability. This paper models systemic risk by tapping into expert knowledge of financial supervisors. We decompose systemic risk into a number of interconnected seg…

A new method combines Gaussian graphical models for better distributed Gaussian process predictions.

Adaptive Conformal Inference improves time series forecasting uncertainty.

New method improves forecast accuracy using CRPS for probabilistic predictions.

New method reduces labeler costs by aggregating predictions from local classifiers.

In this paper we extend the setting of the online prediction with expert advice to function-valued forecasts. At each step of the online game several experts predict a function, and the learner has to efficiently aggregate these functional forecasts into a single forecast. We adapt basic mixable (and exponentially conc…

New approach detects cyber-attacks in real-time.

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 …

The paper explores trade-offs between regret and variance in online learning algorithms.

Investigates least squares estimation in deterministic MoE models.

In real-world scenarios, data tends to exhibit a long-tailed distribution, which increases the difficulty of training deep networks. In this paper, we propose a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME). Our method is inspired by the observation that networks traine…

EBBS integrates expert assessments into MIO best-subsets problem.

New method aggregates bootstrapped DAGs for causal discovery.

AGFN improves causal discovery by integrating expert feedback and handling latent confounding.

New method for disaggregate electricity demand forecasting at household level.

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…

Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm. We provide a new interpretation of mixability via convex analysis that highlights the role of the Kullback-Leibler divergence in its definit…

New method improves conformal prediction for ambiguous labels.

The paper proposes methods to identify and sample from mixtures of Mallows models for top-k rankings.

We study online aggregation of the predictions of experts, and first show new second-order regret bounds in the standard setting, which are obtained via a version of the Prod algorithm (and also a version of the polynomially weighted average algorithm) with multiple learning rates. These bounds are in terms of excess l…

The goal of online prediction with expert advice is to find a decision strategy which will perform almost as well as the best expert in a given pool of experts, on any sequence of outcomes. This problem has been widely studied and $O(\sqrt{T})$ and $O(\log{T})$ regret bounds can be achieved for convex losses (\cite{zin…

New method accounts for uncertainty in medical AI evaluations.

We consider a variation on the problem of prediction with expert advice, where new forecasters that were unknown until then may appear at each round. As often in prediction with expert advice, designing an algorithm that achieves near-optimal regret guarantees is straightforward, using aggregation of experts. However, …

MoE-F combines LLMs online for better time-series prediction.