Search Results for author:
Found 18 papers, 5 papers with code
Federated $\mathcal{X}$-armed Bandit with Flexible Personalisation
This paper introduces a novel approach to personalised federated learning within the $\mathcal{X}$-armed bandit framework, addressing the challenge of optimising both local and global objectives in a highly heterogeneous environment.
FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, allViewed Items and Click Responses/No-Click for Recommender Systems Research
Although the usage of exposure data in recommender systems is growing, to our knowledge there is no open large-scale recommender systems dataset that includes the slates of items presented to the users at each interaction.
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification
We consider a variant of online binary classification where a learner sequentially assigns labels ($0$ or $1$) to items with unknown true class.
Decentralized Q-Learning in Zero-sum Markov Games
The key challenge in this decentralized setting is the non-stationarity of the environment from an agent's perspective, since both her own payoffs and the system evolution depend on the actions of other agents, and each agent adapts her policies simultaneously and independently.
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling
We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations.
GIBBON: General-purpose Information-Based Bayesian OptimisatioN
This paper describes a general-purpose extension of max-value entropy search, a popular approach for Bayesian Optimisation (BO).
BOSS: Bayesian Optimization over String Spaces
This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops.
Learning to Rank under Multinomial Logit Choice
The learning to rank (LTR) framework models this problem as a sequential problem of selecting lists of content and observing where users decide to click.
BOSH: Bayesian Optimization by Sampling Hierarchically
Deployments of Bayesian Optimization (BO) for functions with stochastic evaluations, such as parameter tuning via cross validation and simulation optimization, typically optimize an average of a fixed set of noisy realizations of the objective function.
MUMBO: MUlti-task Max-value Bayesian Optimization
MUMBO is scalable and efficient, allowing multi-task Bayesian optimization to be deployed in problems with rich parameter and fidelity spaces.
On Thompson Sampling for Smoother-than-Lipschitz Bandits
Thompson Sampling is a well established approach to bandit and reinforcement learning problems.
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms
We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models.
Adaptive Sensor Placement for Continuous Spaces
We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events.
Adaptive Policies for Perimeter Surveillance Problems
Maximising the detection of intrusions is a fundamental and often critical aim of perimeter surveillance.
Bandit learning in concave $N$-person games
This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games.
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models
K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning.
Combinatorial Multi-Armed Bandits with Filtered Feedback
Motivated by problems in search and detection we present a solution to a Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward distributions and a new class of feedback, filtered semibandit feedback.
Game-theoretical control with continuous action sets
To do so, we extend the theory of finite-dimensional two-timescale stochastic approximation to an infinite-dimensional, Banach space setting, and we prove that the continuous dynamics of the process converge to equilibrium in the case of potential games.
