arXiv research

New method uses correlated auxiliary feedback to reduce regret in parameterized bandits.

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where us…

We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. Such success-failure rewards are often …

Autoregressive feedback is considered a necessity for successful unconditional text generation using stochastic sequence models. However, such feedback is known to introduce systematic biases into the training process and it obscures a principle of generation: committing to global information and forgetting local nuanc…

PABBO optimizes user utility learning from preferential feedback, significantly faster than traditional methods.

Sequential experiments are often characterized by an exploration-exploitation tradeoff that is captured by the multi-armed bandit (MAB) framework. This framework has been studied and applied, typically when at each time period feedback is received only on the action that was selected at that period. However, in many pr…

Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit. In this paper we introduce ImplicitCE, an algorithm for recommending items to new users during their sign-up flow. ImplicitCE works by trans…

Study optimal consumption for loss-averse agents considering past spending peaks.

Improved reinforcement learning algorithm with linear approximation for unknown dynamics.

The paper analyzes optimal consumption with past spending maximum as a reference.

Proposes a deep learning model for timely and accurate recommendations.

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based …

Generalizes adversarial learning for better latent variable inference in GANs.

Framework identifies population quantities from MNAR feedback using weak shadow variables from pretrained models.

The paper solves a control problem using reflections to track a benchmark process.

Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal m…

The paper extends Merton's problem by adding benchmark tracking, finding optimal strategies.

AuxiLearn combines auxiliary tasks into a single loss function.

In-N-Out improves model robustness to out-of-distribution data.

Aux-NAS uses auxiliary labels to improve primary task performance without extra inference cost.

Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method which automatically learns appropriate labels for an auxiliary task, such that any supervised learning task can be improved without requiri…

This paper studies the continuous time mean-variance portfolio selection problem with one kind of non-linear wealth dynamics. To deal the expectation constraint, an auxiliary stochastic control problem is firstly solved by two new generalized stochastic Riccati equations from which a candidate portfolio in feedback for…

Researchers identify valid auxiliary functions for extreme value distributions and their max-domains of attraction.

Auxiliary Tuning adapts pre-trained models for novel tasks efficiently.

The paper improves high-dimensional linear regression prediction and estimation using auxiliary samples.

Method reweights auxiliary tasks to reduce data need for main task.

This paper reviews dMTL and methods for selecting auxiliary tasks.

Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of prima…

Forward gradients improve neural network training without backpropagation issues.

MFAI uses gradient boosted trees to leverage auxiliary info for scalable Bayesian matrix factorization.

Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks w.r.t. the primary task is not known a priori, the success of MTL models depends on th…

Proves Bochner's identity on graphs using a new auxiliary graph.

ScoreFusion fuses multiple diffusion models to enhance generative modeling of a target population.

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…

Proposes a method to improve graph neural networks on heterogeneous graphs using meta-paths.

NeurT-FDR controls FDR by incorporating auxiliary covariates in deep learning.

We introduce a new family of MCMC samplers that combine auxiliary variables, Gibbs sampling and Taylor expansions of the target density. Our approach permits the marginalisation over the auxiliary variables yielding marginal samplers, or the augmentation of the auxiliary variables, yielding auxiliary samplers. The well…

Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged b…

REGAIN learns optimal auxiliary directions for forecast reconciliation.

LRSAO uses RL to dynamically select and unlearn auxiliary objectives for EA optimization.

We propose a technique for increasing the efficiency of gradient-based inference and learning in Bayesian networks with multiple layers of continuous latent vari- ables. We show that, in many cases, it is possible to express such models in an auxiliary form, where continuous latent variables are conditionally determini…

Enhances machine learning with background knowledge through feature generation.

Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal(i.e., no hierarchy) and exclusive of one another (i.e., no overlap). CNN-based image classifiers built on this assumption, therefore, cannot take into ac…

Learn dynamics of a system using auxiliary data from similar systems.

Detecting and aggregating sentiments toward people, organizations, and events expressed in unstructured social media have become critical text mining operations. Early systems detected sentiments over whole passages, whereas more recently, target-specific sentiments have been of greater interest. In this paper, we pres…

A framework for using auxiliary data to improve few-shot learning.

Proto-value networks improve deep reinforcement learning representations using auxiliary tasks.

Auxiliary equations improve bounds in symplectic geometry.