arXiv research

Online L2D algorithm for multiclass classification with varying experts.

Proposes a Varying-Coefficient MoE model for analyzing dynamic data.

In this note, we present a version of the Thompson sampling algorithm for the problem of online linear generalization with full information (i.e., the experts setting), studied by Kalai and Vempala, 2005. The algorithm uses a Gaussian prior and time-varying Gaussian likelihoods, and we show that it essentially reduces …

We consider the problem of learning to behave optimally in a Markov Decision Process when a reward function is not specified, but instead we have access to a set of demonstrators of varying performance. We assume the demonstrators are classified into one of k ranks, and use ideas from ordinal regression to find a rewar…

Improved time series forecasting with expert loss integration.

PCGS-TF uses a Transformer to adaptively control expert switching in non-stationary environments.

L2D-CD learns to defer expert recommendations in causal discovery.

A new method for multi-expert learning-to-defer avoids optimization issues.

With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often di…

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

Optimal investment strategy with expert opinions in uncertain conditions.

CoCoAFusE fuses expert predictions to model complex patterns with interpretability and uncertainty.

Reinforcement learning methods require careful design involving a reward function to obtain the desired action policy for a given task. In the absence of hand-crafted reward functions, prior work on the topic has proposed several methods for reward estimation by using expert state trajectories and action pairs. However…

The paper introduces a method to incorporate expert opinion on observable quantities into statistical models.

uMoE trains NNs with uncertain data by embedding uncertainty into training.

Adversarial MoE learns category-specific models for product search.

Manual segmentation of the Left Ventricle (LV) is a tedious and meticulous task that can vary depending on the patient, the Magnetic Resonance Images (MRI) cuts and the experts. Still today, we consider manual delineation done by experts as being the ground truth for cardiac diagnosticians. Thus, we are reviewing the p…

Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important…

Paper proposes a novel method to accurately determine the number of experts in Gaussian-gated Gaussian MoE models.

TFPS improves time series forecasting by learning pattern-specific experts.

LEARN-SAM improves RL from sub-optimal demonstrations by localizing expert policies and selectively using demonstrations.

Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…

New method improves forecast accuracy using CRPS for probabilistic predictions.

Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obt…

Proposes a new bankruptcy prediction model using Bayesian framework with expert knowledge.

PRISM-VQ combines financial priors with vector quantization for better stock prediction.

Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineer…

Moirai-MoE improves time series forecasting by automatically specializing tokens without human-defined frequency.

Data extracted from software repositories is used intensively in Software Engineering research, for example, to predict defects in source code. In our research in this area, with data from open source projects as well as an industrial partner, we noticed several shortcomings of conventional data mining approaches for c…

Model accurately gates ocean microbes from high-frequency flow cytometry data.

Proposes PE-GP-UCB for time-varying Bayesian optimisation.

To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between ru…

Heteroscedastic regression considering the varying noises among observations has many applications in the fields like machine learning and statistics. Here we focus on the heteroscedastic Gaussian process (HGP) regression which integrates the latent function and the noise function together in a unified non-parametric B…

In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and pre…

Active learning aims to reduce annotation cost by predicting which samples are useful for a human teacher to label. However it has become clear there is no best active learning algorithm. Inspired by various philosophies about what constitutes a good criteria, different algorithms perform well on different datasets. Th…

Multi-expert L2D underfits more severely, requiring new methods.

LR-Robot accelerates SLRs by combining expert oversight and AI, revealing trends and patterns in financial research.

TENP prunes experts and neurons in Mixture-of-Experts models for efficient deployment.

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

We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We p…

Homeownership boosts wealth and welfare compared to renting, according to new research.

HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.

NAMEx merges experts using Nash bargaining for improved performance.

Expert augmentation improves hybrid model generalization.

New method calibrates Gaussian product experts for better predictions.

This study compares 6 imitation learning algorithms using a common dataset and hyperparameter budget.

Meta-algorithm optimizes nonstochastic bandits with infinitely many experts.

New algorithm reduces expert prediction regret for two experts.