Patrick Jaillet

research

∙ 02/14/2023

Non-stationary Contextual Bandits and Universal Learning

We study the fundamental limits of learning in contextual bandits, where...

0 Moise Blanchard, et al. ∙

research

∙ 02/14/2023

Effective Dimension in Bandit Problems under Censorship

In this paper, we study both multi-armed and contextual bandit problems ...

0 Gauthier Guinet, et al. ∙

research

∙ 02/09/2023

Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal

We give query complexity lower bounds for convex optimization and the re...

0 Moise Blanchard, et al. ∙

research

∙ 02/03/2023

Multi-channel Autobidding with Budget and ROI Constraints

In digital online advertising, advertisers procure ad impressions simult...

0 Yuan Deng, et al. ∙

research

∙ 12/31/2022

Contextual Bandits and Optimistically Universal Learning

We consider the contextual bandit problem on general action and context ...

0 Moise Blanchard, et al. ∙

research

∙ 11/20/2022

Additional Results and Extensions for the paper "Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem”

This technical report provides additional results for the main paper “Pr...

0 Moise Blanchard, et al. ∙

research

∙ 11/20/2022

Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem

The k-traveling salesman problem (k-TSP) seeks a tour of minimal length ...

0 Moise Blanchard, et al. ∙

research

∙ 10/13/2022

Sample-Then-Optimize Batch Neural Thompson Sampling

Bayesian optimization (BO), which uses a Gaussian process (GP) as a surr...

0 Zhongxiang Dai, et al. ∙

research

∙ 09/10/2022

Fairness in the Autobidding World with Machine-learned Advice

The increasing availability of real-time data has fueled the prevalence ...

0 Yuan Deng, et al. ∙

research

∙ 08/20/2022

Weighted Maximum Entropy Inverse Reinforcement Learning

We study inverse reinforcement learning (IRL) and imitation learning (IM...

11 The Viet Bui, et al. ∙

research

∙ 06/14/2022

On Provably Robust Meta-Bayesian Optimization

Bayesian optimization (BO) has become popular for sequential optimizatio...

0 Zhongxiang Dai, et al. ∙

research

∙ 05/28/2022

Federated Neural Bandit

Recent works on neural contextual bandit have achieved compelling perfor...

0 Zhongxiang Dai, et al. ∙

research

∙ 05/05/2022

Optimal Information Provision for Strategic Hybrid Workers

We study the problem of information provision by a strategic central pla...

0 Sohil Shah, et al. ∙

research

∙ 03/09/2022

Universal Regression with Adversarial Responses

We provide algorithms for regression with adversarial responses under la...

15 Moise Blanchard, et al. ∙

research

∙ 02/28/2022

Rectified Max-Value Entropy Search for Bayesian Optimization

Although the existing max-value entropy search (MES) is based on the wid...

10 Quoc Phong Nguyen, et al. ∙

research

∙ 12/31/2021

Robust Entropy-regularized Markov Decision Processes

Stochastic and soft optimal policies resulting from entropy-regularized ...

8 Tien Mai, et al. ∙

research

∙ 10/27/2021

Differentially Private Federated Bayesian Optimization with Distributed Exploration

Bayesian optimization (BO) has recently been extended to the federated l...

14 Zhongxiang Dai, et al. ∙

research

∙ 07/30/2021

Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization

Information-based Bayesian optimization (BO) algorithms have achieved st...

9 Quoc Phong Nguyen, et al. ∙

research

∙ 07/16/2021

Bidding and Pricing in Budget and ROI Constrained Markets

In online advertising markets, setting budget and return on investment (...

0 Negin Golrezaei, et al. ∙

research

∙ 05/13/2021

Value-at-Risk Optimization with Gaussian Processes

Value-at-risk (VaR) is an established measure to assess risks in critica...

29 Quoc Phong Nguyen, et al. ∙

research

∙ 04/17/2021

Convolutional Normalizing Flows for Deep Gaussian Processes

Deep Gaussian processes (DGPs), a hierarchical composition of GP models,...

0 Haibin Yu, et al. ∙

research

∙ 02/18/2021

Efficient Carpooling and Toll Pricing for Autonomous Transportation

In this paper, we address the existence and computation of competitive e...

0 Saurabh Amin, et al. ∙

research

∙ 12/19/2020

An Information-Theoretic Framework for Unifying Active Learning Problems

This paper presents an information-theoretic framework for unifying acti...

11 Quoc Phong Nguyen, et al. ∙

research

∙ 12/19/2020

Top-k Ranking Bayesian Optimization

This paper presents a novel approach to top-k ranking Bayesian optimizat...

19 Quoc Phong Nguyen, et al. ∙

research

∙ 11/07/2020

No-regret Learning in Price Competitions under Consumer Reference Effects

We study long-run market stability for repeated price competitions betwe...

0 Negin Golrezaei, et al. ∙

research

∙ 10/24/2020

Variational Bayesian Unlearning

This paper studies the problem of approximately unlearning a Bayesian mo...

3 Quoc Phong Nguyen, et al. ∙

research

∙ 10/20/2020

Federated Bayesian Optimization via Thompson Sampling

Bayesian optimization (BO) is a prominent approach to optimizing expensi...

17 Zhongxiang Dai, et al. ∙

research

∙ 09/16/2020

Competitive Ratios for Online Multi-capacity Ridesharing

In multi-capacity ridesharing, multiple requests (e.g., customers, food ...

1 Meghna Lowalekar, et al. ∙

research

∙ 09/13/2020

Zone pAth Construction (ZAC) based Approaches for Effective Real-Time Ridesharing

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare hav...

4 Meghna Lowalekar, et al. ∙

research

∙ 08/18/2020

Learning Structure in Nested Logit Models

This paper introduces a new data-driven methodology for nested logit str...

0 Youssef M Aboutaleb, et al. ∙

research

∙ 08/18/2020

A Relation Analysis of Markov Decision Process Frameworks

We study the relation between different Markov Decision Process (MDP) fr...

0 Tien Mai, et al. ∙

research

∙ 06/30/2020

R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games

This paper presents a recursive reasoning formalism of Bayesian optimiza...

11 Zhongxiang Dai, et al. ∙

research

∙ 12/19/2019

Robust Multi-product Pricing under General Extreme Value Models

We study robust versions of pricing problems where customers choose prod...

0 Tien Mai, et al. ∙

research

∙ 11/24/2019

An Iterative Security Game for Computing Robust and Adaptive Network Flows

The recent advancement in cyberphysical systems has led to an exponentia...

0 Supriyo Ghosh, et al. ∙

research

∙ 11/16/2019

Inverse Reinforcement Learning with Missing Data

We consider the problem of recovering an expert's reward function with i...

26 Tien Mai, et al. ∙

research

∙ 11/16/2019

Generalized Maximum Causal Entropy for Inverse Reinforcement Learning

We consider the problem of learning from demonstrated trajectories with ...

44 Tien Mai, et al. ∙

research

∙ 11/08/2019

Incentive-aware Contextual Pricing with Non-parametric Market Noise

We consider a dynamic pricing problem for repeated contextual second-pri...

7 Negin Golrezaei, et al. ∙

research

∙ 10/26/2019

Implicit Posterior Variational Inference for Deep Gaussian Processes

A multi-layer deep Gaussian process (DGP) model is a hierarchical compos...

16 Haibin Yu, et al. ∙

research

∙ 07/08/2019

Optimal Explanations of Linear Models

When predictive models are used to support complex and important decisio...

2 Dimitris Bertsimas, et al. ∙

research

∙ 07/08/2019

The Price of Interpretability

When quantitative models are used to support decision-making on complex ...

4 Dimitris Bertsimas, et al. ∙

research

∙ 11/20/2018

Probability Distributions on Partially Ordered Sets and Network Security Games

We consider the following problem: Does there exist a probability distri...

0 Mathieu Dahan, et al. ∙

research

∙ 09/30/2018

Online Resource Allocation under Partially Predictable Demand

For online resource allocation problems, we propose a new demand arrival...

0 Dawsen Hwang, et al. ∙

research

∙ 08/09/2018

Maximum Weight Online Matching with Deadlines

We study the problem of matching agents who arrive at a marketplace over...

0 Itai Ashlagi, et al. ∙

research

∙ 03/22/2018

Signaling Game-based Misbehavior Inspection in V2I-enabled Highway Operations

Vehicle-to-Infrastructure (V2I) communications are increasingly supporti...

0 Manxi Wu, et al. ∙

research

∙ 03/04/2018

Maximizing Efficiency in Dynamic Matching Markets

We study the problem of matching agents who arrive at a marketplace over...

0 Itai Ashlagi, et al. ∙

research

∙ 11/01/2017

Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models

This paper presents a novel variational inference framework for deriving...

0 Haibin Yu, et al. ∙

research

∙ 11/21/2015

Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond

This paper presents a novel nonmyopic adaptive Gaussian process planning...

0 Chun Kai Ling, et al. ∙

research

∙ 11/17/2014

Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation

The expressive power of a Gaussian process (GP) model comes at a cost of...

0 Kian Hsiang Low, et al. ∙

research

∙ 08/09/2014

Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations

Gaussian processes (GP) are Bayesian non-parametric models that are wide...

0 Jie Chen, et al. ∙

research

∙ 08/09/2014

Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena

The problem of modeling and predicting spatiotemporal traffic phenomena ...

0 Jie Chen, et al. ∙

Patrick Jaillet

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