Search Results for author:
Found 30 papers, 4 papers with code
Risk-Averse Multi-Armed Bandit Problems under Mean-Variance Measure
We show that the model-specific regret and the model-independent regret in terms of the mean-variance of the reward process are lower bounded by $\Omega(\log T)$ and $\Omega(T^{2/3})$, respectively.
Anomaly Detection in Hierarchical Data Streams under Unknown Models
We consider the problem of detecting a few targets among a large number of hierarchical data streams.
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs
Two settings of complete and partial side information based on whether the UIG is fully revealed are studied and a general two-step learning structure consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is proposed to fully exploit the topological structure of the side information.
Decision Variance in Online Learning
In this paper, a risk-averse online learning problem under the performance measure of the mean-variance of the rewards is studied.
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization
Online minimization of an unknown convex function over the interval $[0, 1]$ is considered under first-order stochastic bandit feedback, which returns a random realization of the gradient of the function at each query point.
Adaptive Sensor Placement for Continuous Spaces
We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events.
Ordinal Bayesian Optimisation
Bayesian optimisation is a powerful tool to solve expensive black-box problems, but fails when the stationary assumption made on the objective function is strongly violated, which is the case in particular for ill-conditioned or discontinuous objectives.
Regret Bounds for Noise-Free Kernel-Based Bandits
Kernel-based bandit is an extensively studied black-box optimization problem, in which the objective function is assumed to live in a known reproducing kernel Hilbert space.
Amortized variance reduction for doubly stochastic objectives
Approximate inference in complex probabilistic models such as deep Gaussian processes requires the optimisation of doubly stochastic objective functions.
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
A framework based on iterative coordinate minimization (CM) is developed for stochastic convex optimization.
Scalable Thompson Sampling using Sparse Gaussian Process Models
We provide theoretical guarantees and show that the drastic reduction in computational complexity of scalable TS can be enjoyed without loss in the regret performance over the standard TS.
On Information Gain and Regret Bounds in Gaussian Process Bandits
For the Mat\'ern family of kernels, where the lower bounds on $\gamma_T$, and regret under the frequentist setting, are known, our results close a huge polynomial in $T$ gap between the upper and lower bounds (up to logarithmic in $T$ factors).
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space.
Optimal Order Simple Regret for Gaussian Process Bandits
Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$.
Uniform Generalization Bounds for Overparameterized Neural Networks
As a byproduct of our results, we show the equivalence between the RKHS corresponding to the NT kernel and its counterpart corresponding to the Mat\'ern family of kernels, showing the NT kernels induce a very general class of models.
Open Problem: Tight Online Confidence Intervals for RKHS Elements
Confidence intervals are a crucial building block in the analysis of various online learning problems.
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization.
Near-Optimal Collaborative Learning in Bandits
In this paper, we provide new lower bounds on the sample complexity of pure exploration and on the regret.
Provably and Practically Efficient Neural Contextual Bandits
The non-asymptotic error bounds may be of broader interest as a tool to establish the relation between the smoothness of the activation functions in neural contextual bandits and the smoothness of the kernels in kernel bandits.
Collaborative Learning in Kernel-based Bandits for Distributed Users
We study collaborative learning among distributed clients facilitated by a central server.
Sample Complexity of Kernel-Based Q-Learning
To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model.
Delayed Feedback in Kernel Bandits
An abstraction of the problem can be formulated as a kernel based bandit problem (also known as Bayesian optimisation), where a learner aims at optimising a kernelized function through sequential noisy observations.
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow.
Image generation with shortest path diffusion
We compute the shortest path according to this metric, and we show that it corresponds to a combination of image sharpening, rather than blurring, and noise deblurring.
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
In particular, with highly non-smooth kernels (such as Neural Tangent kernel or some Mat\'ern kernels) the existing results lead to trivial (superlinear in the number of episodes) regret bounds.
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling
In this paper, we propose a diffusion model based channel sampling approach for rapidly synthesizing channel realizations from limited data.
Adversarial Contextual Bandits Go Kernelized
We study a generalization of the problem of online learning in adversarial linear contextual bandits by incorporating loss functions that belong to a reproducing kernel Hilbert space, which allows for a more flexible modeling of complex decision-making scenarios.
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization.
Robust Best-arm Identification in Linear Bandits
To this end, we present an instance-dependent lower bound for the robust best-arm identification problem with linear rewards.
Optimal Regret Bounds for Collaborative Learning in Bandits
We consider regret minimization in a general collaborative multi-agent multi-armed bandit model, in which each agent faces a finite set of arms and may communicate with other agents through a central controller.
