arXiv research

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

This study analyzes how RNNs process context in sentiment analysis.

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…

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…

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…

Paper tackles Arabic question similarity, outperforming state-of-the-art.

A new algorithm selects models for contextual bandits, reducing regret.

Study analyzes broker's gain from trade in repeated context-based trading.

A new affinity measure for spectral clustering using conformal prediction improves clustering performance.

Adapts to misspecification in contextual bandits using offline regression.

Active learning improves ordering of items with contextual attributes.

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

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

New interface explains contextual bandits to non-experts.

Proposes CLSM for better subsequence generation in music sequences.

Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches hav…

In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current use cases focus on single step einforcement learning (RL) and mostly in the domain of natural language proce…

Neural-$σ^2$-LinearUCB improves regret in neural contextual bandits.

Single model estimates ensemble uncertainty efficiently.

New algorithm offers costless model selection in contextual bandits.

We study contextual bandits with budget and time constraints, referred to as constrained contextual bandits.The time and budget constraints significantly complicate the exploration and exploitation tradeoff because they introduce complex coupling among contexts over time.Such coupling effects make it difficult to obtai…

CAMS selects best pre-trained model for unlabeled data points.

Efficiently identifies good policies by choosing contexts for human feedback.

A new algorithm tackles graph-based contextual bandits with efficient regret bounds.

SCNode improves node embeddings for GNNs in both homophilic and heterophilic graphs.

We tackle the problem of online reward maximisation over a large finite set of actions described by their contexts. We focus on the case when the number of actions is too big to sample all of them even once. However we assume that we have access to the similarities between actions' contexts and that the expected reward…

Consider a nonparametric contextual multi-arm bandit problem where each arm $a \in [K]$ is associated to a nonparametric reward function $f_a: [0,1] \to \mathbb{R}$ mapping from contexts to the expected reward. Suppose that there is a large set of arms, yet there is a simple but unknown structure amongst the arm reward…

A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid mode…

Contextual bandit framework improves revenue optimization in securities lending market.

BERTopic improves financial text analysis with FinTextSim's contextual embeddings.

Contextualized ML learns context-dependent effects using deep learning.

IDS improves reinforcement learning with contextual information.

This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action orga…

Estimates shared parameters across related learning problems using robust statistics and LASSO.

Thompson Sampling, one of the oldest heuristics for solving multi-armed bandits, has recently been shown to demonstrate state-of-the-art performance. The empirical success has led to great interests in theoretical understanding of this heuristic. In this paper, we approach this problem in a way very different from exis…

SADCBO optimizes contextual variables by balancing relevance and cost.

New method tracks significant shifts in nonparametric bandits.

Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine recommendations, e.g. when a user specifies a price range or product category filter. …

The bundle approach and n-contextuality reveal quantum model contextuality.

Risk-aware linear bandits optimize against adverse outcomes.

Proposes a neural network for contextual regression.

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

Improved regret bounds for contextual bandits considering variance sequences.

Transformers model contextual relations using probabilistic measures, revealing their expressive power.

Paper solves stochastic contextual linear bandits using linear bandit algorithms.

Faster algorithm reduces contextual bandit regret with fewer offline regression calls.

Efficiently handles contextual bandits with diffusion models.

New algorithm adapts to unknown smoothness in contextual bandits.