arXiv research

BOA improves financial forecasting by combining expert models.

Adaptive Bayesian learning aggregates experts to improve performance.

A new algorithm estimates aggregate marginals from noisy data in an online manner.

New algorithm predicts piecewise regular functions online.

The article improves prediction by aggregating Kalman recursions online.

MELO predicts electricity loads by adapting to shifts without external indicators.

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

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

Paper improves probabilistic forecasts of electricity prices.

Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregation results from crowd workers. To ensure the time relevance and overcome slow responses of workers, online label aggregation is increasingly requested, calling for solut…

Meta-strategy learns tuning parameters for online learning methods.

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

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

New method improves forecast accuracy using CRPS for probabilistic predictions.

New aggregation strategy handles unbounded losses with regret bounds.

A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.

PriceAggregator optimizes hotel price fetching to increase Agoda's bookings.

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…

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…

We introduce a new recursive aggregation procedure called Bernstein Online Aggregation (BOA). The exponential weights include an accuracy term and a second order term that is a proxy of the quadratic variation as in Hazan and Kale (2010). This second term stabilizes the procedure that is optimal in different senses. We…

New method forecasts time series with changing variances.

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

Extends boosting to multiclass online agnostic classification.

Boosts weak online learners to strong ones with sublinear regret.

Distillation is an effective knowledge-transfer technique that uses predicted distributions of a powerful teacher model as soft targets to train a less-parameterized student model. A pre-trained high capacity teacher, however, is not always available. Recently proposed online variants use the aggregated intermediate pr…

Bounds on Littlestone dimension for private learning and online prediction.

The paper addresses fairness in online learning by extending auditing schemes and presenting efficient algorithms.

Algorithm aggregates rewards from multiple players to learn related tasks in online bandit learning.

Value aggregation is a general framework for solving imitation learning problems. Based on the idea of data aggregation, it generates a policy sequence by iteratively interleaving policy optimization and evaluation in an online learning setting. While the existence of a good policy in the policy sequence can be guarant…

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…

Random Forests (RF) is one of the algorithms of choice in many supervised learning applications, be it classification or regression. The appeal of such tree-ensemble methods comes from a combination of several characteristics: a remarkable accuracy in a variety of tasks, a small number of parameters to tune, robustness…

We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes, and the transition function is not known to the learner. We show $\tilde{O}(L|X|\sqrt{|A|T})$ regret bound, where $T$ is the number of episodes, $X$ is the state space, $A…

An online learning framework for survival analysis with real-time adaptation.

Improved multiclass logistic regression with lower computational complexity.

The paper develops a method for forecasting power consumption at various levels of aggregation.

Algorithm learns expert weights to minimize regret in adversarial setting.

Paper proposes a faster method for fuzzy neural networks by removing unsuitable hyperboxes.

Recommenders have become widely popular in recent years because of their broader applicability in many e-commerce applications. These applications rely on recommenders for generating advertisements for various offers or providing content recommendations. However, the quality of the generated recommendations depends on …

Many real world problems can now be effectively solved using supervised machine learning. A major roadblock is often the lack of an adequate quantity of labeled data for training. A possible solution is to assign the task of labeling data to a crowd, and then infer the true label using aggregation methods. A well-known…

New method for online learning IC models with node-level feedback.

Method fuses low and high-resolution data for better health estimates.

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SWAG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weig…

We consider the problem of learning over non-stationary ranking streams. The rankings can be interpreted as the preferences of a population and the non-stationarity means that the distribution of preferences changes over time. Our goal is to learn, in an online manner, the current distribution of rankings. The bottlene…

Improved Thompson Sampling reduces regret in contextual bandits and reinforcement learning.

The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of existing batch and online ensemble classifiers. There are only a limited number of studies on the ense…

We study the question of how to aggregate controllers for dynamical systems in order to improve their performance. To this end, we propose a framework of boosting for online control. Our main result is an efficient boosting algorithm that combines weak controllers into a provably more accurate one. Empirical evaluation…

Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.

Efficient strategies for online learning against bandit algorithms solve minimax problems.