arXiv research

A new pricing strategy learns customer valuations without noise distribution knowledge.

Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of ef…

A simple algorithm reduces federated contextual linear bandits' regret efficiently.

The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question answering (QA) has been recently released. ARC only contains natural science questions authored for human exams, which are hard to answer and require advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art QA systems fail to s…

LLM-Lasso uses LLMs to improve feature selection in Lasso regression.

Improved Thompson Sampling for high-dimensional sparse bandits.

We introduce the problem of model selection for contextual bandits, where a learner must adapt to the complexity of the optimal policy while balancing exploration and exploitation. Our main result is a new model selection guarantee for linear contextual bandits. We work in the stochastic realizable setting with a seque…

Method learns evolving policies in healthcare contexts.

Study on selecting between base algorithms in stochastic bandit problems.

This paper investigates how transformers can learn to generalize to unseen examples in context.

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

Bayesian framework reduces online optimization regret.

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…

New method for estimating and optimizing MDPs without stationarity.

Master algorithm selects best contextual bandit from a collection.

New algorithms minimize regret in both adversarial and stochastic contexts.

We study the stochastic contextual bandit problem, where the reward is generated from an unknown function with additive noise. No assumption is made about the reward function other than boundedness. We propose a new algorithm, NeuralUCB, which leverages the representation power of deep neural networks and uses a neural…

New algorithms adapt to model misspecification in contextual bandits.

Algorithm reduces regret in misspecified linear contextual bandits.

Study improves vehicle motion prediction by incorporating traffic density.

RKT model improves knowledge tracing by considering exercise relations and student forget behavior.

Proposes a new model for more accurate demand forecasting considering dynamic contextual information.

New algorithms reduce regret in adversarial linear contextual bandits.

INFERS PDEs from data samples using learned context.

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…

The paper addresses contextual optimization problems with feedback, aiming to minimize regret.

The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are cru…

SPARKLE handles high-dimensional covariates for online decision-making.

The paper achieves nearly optimal regret bounds for contextual multinomial logit bandits.

Optimizes decision-making in dueling bandits with contextual features.

Paper develops robust OPF method using contextual information.

This paper improves context-aware recommender systems by selecting and incorporating relevant low-dimensional contextual information.

Study dynamic batch learning in high-dimensional sparse linear bandits.

New algorithm tackles non-linear utility in MNL bandits with $ilde{O}(\sqrt{T})$ regret.

New algorithm resists corruption in linear contextual bandits.

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

Survey of methods to incorporate external knowledge into stock price prediction.

Building performance discrepancies between building design and operation are one of the causes that lead many new designs fail to achieve their goals and objectives. One of main factors contributing to the discrepancy is occupant behaviors. Occupants responding to a new design are influenced by several factors. Existin…

Contextual bandit learning is an increasingly popular approach to optimizing recommender systems via user feedback, but can be slow to converge in practice due to the need for exploring a large feature space. In this paper, we propose a coarse-to-fine hierarchical approach for encoding prior knowledge that drastically …

Bayesian algorithms perform well even with misspecified priors, especially in meta-learning.

For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too often, the litany of proposed explainable deep learning methods stop at the first step, providing practitioners with insight into a model, but…

We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place for the other three. Our basic systems are built on Transformer, back translation and knowledge distillation. We integrate several of our rece…

A novel bandit problem with context-dependent rewards and blocking.

New method tracks significant shifts in nonparametric bandits.

New algorithm learns from sparse data without knowing sparsity index.

New algorithms for neural bandits learn from context and arm features.

FinDKG uses LLMs to detect financial trends from news articles.

Contextualized ML learns context-dependent effects using deep learning.