Search Results for author:
Found 33 papers, 13 papers with code
Bandit Profit-maximization for Targeted Marketing
Specifically, we aim to maximize profit over an arbitrary sequence of multiple demand curves, each dependent on a distinct ancillary variable, but sharing the same price.
Active Cost-aware Labeling of Streaming Data
When the labeling cost is $B$, our algorithm, which chooses to label a point if the uncertainty is larger than a time and cost dependent threshold, achieves a worst-case upper bound of $\widetilde{O}(B^{\frac{1}{3}} K^{\frac{1}{3}} T^{\frac{2}{3}})$ on the loss after $T$ rounds.
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
Customers with few relevant reviews may hesitate to make a purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews so that buyers can confidently estimate their values.
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback
The allocations at a CE are Pareto efficient and fair.
PAC Best Arm Identification Under a Deadline
In this work, the decision-maker is given a deadline of $T$ rounds, where, on each round, it can adaptively choose which arms to pull and how many times to pull them; this distinguishes the number of decisions made (i. e., time or number of rounds) from the number of samples acquired (cost).
Online Learning Demands in Max-min Fairness
We describe mechanisms for the allocation of a scarce resource among multiple users in a way that is efficient, fair, and strategy-proof, but when users do not know their resource requirements.
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
Second, we present an algorithm for a fixed deadline setting, where we are given a time deadline and need to maximize the probability of finding the best arm.
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
To that end, we first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism.
Offline Contextual Bayesian Optimization for Nuclear Fusion
One obstacle in utilizing fusion as a feasible energy source is the stability of the reaction.
Offline Contextual Bayesian Optimization
In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find an optimal configuration.
Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning
Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases.
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
In applications such as molecule design or drug discovery, it is desirable to have an algorithm which recommends new candidate molecules based on the results of past tests.
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
We compare Dragonfly to a suite of other packages and algorithms for global optimisation and demonstrate that when the above methods are integrated, they enable significant improvements in the performance of BO.
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language
Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum.
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
We study the problem of black-box optimization of a noisy function in the presence of low-cost approximations or fidelities, which is motivated by problems like hyper-parameter tuning.
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
Motivated by settings such as hyper-parameter tuning and physical simulations, we consider the problem of black-box optimization of a function.
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
We also study a notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret.
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal.
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be performed in parallel.
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design.
Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain.
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
In this paper, we study active posterior estimation in a Bayesian setting when the likelihood is expensive to evaluate.
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
However, in many cases, cheap approximations to $\func$ may be obtainable.
The Multi-fidelity Multi-armed Bandit
We study a variant of the classical stochastic $K$-armed bandit where observing the outcome of each arm is expensive, but cheap approximations to this outcome are available.
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Recently, there has been a surge of interest in using spectral methods for estimating latent variable models.
Multi-fidelity Gaussian Process Bandit Optimisation
However, in many cases, cheap approximations to $f$ may be obtainable.
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
Between non-additive models which often have large variance and first order additive models which have large bias, there has been little work to exploit the trade-off in the middle via additive models of intermediate order.
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations
We propose and analyse estimators for statistical functionals of one or moredistributions under nonparametric assumptions. Our estimators are derived from the von Mises expansion andare based on the theory of influence functions, which appearin the semiparametric statistics literature. We show that estimators based either on data-splitting or a leave-one-out techniqueenjoy fast rates of convergence and other favorable theoretical properties. We apply this framework to derive estimators for several popular informationtheoretic quantities, and via empirical evaluation, show the advantage of thisapproach over existing estimators.
High Dimensional Bayesian Optimisation and Bandits via Additive Models
We prove that, for additive functions the regret has only linear dependence on $D$ even though the function depends on all $D$ dimensions.
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations
We propose and analyze estimators for statistical functionals of one or more distributions under nonparametric assumptions.
On Estimating $L_2^2$ Divergence
We give a comprehensive theoretical characterization of a nonparametric estimator for the $L_2^2$ divergence between two continuous distributions.
Nonparametric Estimation of Renyi Divergence and Friends
We consider nonparametric estimation of $L_2$, Renyi-$\alpha$ and Tsallis-$\alpha$ divergences between continuous distributions.
