arXiv research

Contextualized ML learns context-dependent effects using deep learning.

IDS improves reinforcement learning with contextual information.

The study explores whether model selection guarantees apply to contextual bandits.

New algorithm improves learning efficiency in multi-task contextual bandits.

New algorithm learns optimal exploration parameters for contextual bandits.

The paper tackles learning varying DAG structures based on contextual features.

SADCBO optimizes contextual variables by balancing relevance and cost.

GLCB uses Gated Linear Networks for online contextual bandits.

Novel approach to contextual bandits using self-supervised learning.

Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. However, the learning speed of the traditional contextual bandit algorithms is often slow due to the need for extensive exploration. This poses…

New method for contextual bandit with missing rewards.

New method for contextual bandits with corrupted context.

New approach combines semi-supervised learning and bandits for better predictions.

We describe MELEE, a meta-learning algorithm for learning a good exploration policy in the interactive contextual bandit setting. Here, an algorithm must take actions based on contexts, and learn based only on a reward signal from the action taken, thereby generating an exploration/exploitation trade-off. MELEE address…

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We study a consideration for the expl…

A framework for auto-tuning hyper-parameters in contextual bandit algorithms.

New findings on universal learning in contextual bandits with adversarial rewards.

LoCo-RLHF models diverse human feedback with contextual information.

New algorithm for context bandits with continuous actions.

Active learning method improves AI performance by balancing exploration and exploitation.

Paper tackles domain adaptation for contextual bandits with sub-linear regret.

Algorithm learns optimal arm selection in unsupervised sequential selection with contextual information.

A new method scores contextual Markov networks without assuming chordality.

We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of co…

Meta learning improves with contextualizers that adapt to examples.

Contextual bandit algorithms are essential for solving many real-world interactive machine learning problems. Despite multiple recent successes on statistically and computationally efficient methods, the practical behavior of these algorithms is still poorly understood. We leverage the availability of large numbers of …

New method tackles high-dimensional contextual bandits with flexible kernel models.

Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitly conditioning the policy on a parametric context space. In this paper…

The performance of machine learning model can be further improved if contextual cues are provided as input along with base features that are directly related to an inference task. In offline learning, one can inspect historical training data to identify contextual clusters either through feature clustering, or hand-cra…

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual …

This paper explores the complexity of learning representations in contextual linear bandits.

New algorithms improve contextual bandit performance by adapting to problem difficulty.

Contextual multi-armed bandit problems arise frequently in important industrial applications. Existing solutions model the context either linearly, which enables uncertainty driven (principled) exploration, or non-linearly, by using epsilon-greedy exploration policies. Here we present a deep learning framework for cont…

Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm. They use the reward as a learning signal and aim to maximize the total reward over the inputs. Contextual bandits are commonly used to solve recommendation or ranking problems. This paper considers a learning s…

Paper tackles transfer learning for contextual multi-armed bandits under covariate shift.

This study analyzes how RNNs process context in sentiment analysis.

Taxonomies are semantic hierarchies of concepts. One limitation of current taxonomy learning systems is that they define concepts as single words. This position paper argues that contextualized word representations, which recently achieved state-of-the-art results on many competitive NLP tasks, are a promising method t…

New approach for off-policy learning in contextual bandits with performance guarantees.

In this paper we propose the multi-objective contextual bandit problem with similarity information. This problem extends the classical contextual bandit problem with similarity information by introducing multiple and possibly conflicting objectives. Since the best arm in each objective can be different given the contex…

Paper solves stochastic contextual linear bandits using linear bandit algorithms.

Contextual policy search allows adapting robotic movement primitives to different situations. For instance, a locomotion primitive might be adapted to different terrain inclinations or desired walking speeds. Such an adaptation is often achievable by modifying a small number of hyperparameters. However, learning, when …

Study on adaptivity constraints in linear contextual bandits with optimal design.

The paper tackles misspecification in contextual bandits by incorporating arm-specific variables.

Paper improves Thompson Sampling for linear contextual bandits.

SLiCE learns contextual node embeddings for link prediction in heterogeneous networks.

Improved off-policy selection and learning in contextual bandits with better guarantees.

We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards i…

Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications. In this work, we propose a multi-task learning frame…