40 citations · 113 across the 19 of their papers we have counts for
21 papers
Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation
Qining Zhang, Tanner Fiez, Yi Liu +1
A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inf…
Segment Discovery: Enhancing E-commerce Targeting
Qiqi Li, Roopali Singh, Charin Polpanumas +3
Modern e-commerce services frequently target customers with incentives or interventions to engage them in their products such as games, shopping, video streaming, etc. This custome…
Adaptive Experimentation When You Can't Experiment
Yao Zhao, Kwang-Sung Jun, Tanner Fiez +1
This paper introduces the \emph{confounded pure exploration transductive linear bandit} (\texttt{CPET-LB}) problem. As a motivating example, often online services cannot directly a…
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
Tanner Fiez, Houssam Nassif, Yu-Cheng Chen +2
Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/…
Experimental Designs for Heteroskedastic Variance
Justin Weltz, Tanner Fiez, Alexander Volfovsky +4
Most linear experimental design problems assume homogeneous variance although heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set…
Neural Insights for Digital Marketing Content Design
Fanjie Kong, Yuan Li, Houssam Nassif +3
In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement. However, creating successful marketing content is a manual and…
Adaptive Experimental Design and Counterfactual Inference
Tanner Fiez, Sergio Gamez, Arick Chen +2
Adaptive experimental design methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N test…
Online Learning in Periodic Zero-Sum Games
Tanner Fiez, Ryann Sim, Stratis Skoulakis +2
A seminal result in game theory is von Neumann's minmax theorem, which states that zero-sum games admit an essentially unique equilibrium solution. Classical learning results build…
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms
Liyuan Zheng, Tanner Fiez, Zane Alumbaugh +2
The hierarchical interaction between the actor and critic in actor-critic based reinforcement learning algorithms naturally lends itself to a game-theoretic interpretation. We adop…
Minimax Optimization with Smooth Algorithmic Adversaries
Tanner Fiez, Chi Jin, Praneeth Netrapalli +1
This paper considers minimax optimization in the challenging setting where can be both nonconvex in and nonconcave in . Though such optimization…
Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games
Stratis Skoulakis, Tanner Fiez, Ryann Sim +2
The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interac…
Gradient Descent-Ascent Provably Converges to Strict Local Minmax Equilibria with a Finite Timescale Separation
Tanner Fiez, Lillian Ratliff
We study the role that a finite timescale separation parameter has on gradient descent-ascent in two-player non-convex, non-concave zero-sum games where the learning rate of pl…
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review
Tanner Fiez, Nihar B. Shah, Lillian Ratliff
A number of applications involve sequential arrival of users, and require showing each user an ordering of items. A prime example (which forms the focus of this paper) is the biddi…
Sequential Experimental Design for Transductive Linear Bandits
Tanner Fiez, Lalit Jain, Kevin Jamieson +1
In this paper we introduce the transductive linear bandit problem: given a set of measurement vectors , a set of items $\mathcal{Z}\subset \mathbb{…
Convergence of Learning Dynamics in Stackelberg Games
Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff
This paper investigates the convergence of learning dynamics in Stackelberg games. In the class of games we consider, there is a hierarchical game being played between a leader and…
Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences
Tanner Fiez, Shreyas Sekar, Liyuan Zheng +1
The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge. We propose a multi-armed…
Adaptive Incentive Design
Lillian J. Ratliff, Tanner Fiez
We apply control theoretic and optimization techniques to adaptively design incentives. In particular, we consider the problem of a planner with an objective that depends on data f…
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback
Tanner Fiez, Shreyas Sekar, Lillian J. Ratliff
We study a multi-armed bandit problem in a dynamic environment where arm rewards evolve in a correlated fashion according to a Markov chain. Different than much of the work on rela…
Data-Driven Spatio-Temporal Analysis of Curbside Parking Demand: A Case-Study in Seattle
Tanner Fiez, Lillian Ratliff
Due to rapid expansion of urban areas in recent years, management of curbside parking has become increasingly important. To mitigate congestion, while meeting a city's diverse need…
Optimizing Curbside Parking Resources Subject to Congestion Constraints
Chase Dowling, Tanner Fiez, Lillian Ratliff +1
To gain theoretical insight into the relationship between parking scarcity and congestion, we describe block-faces of curbside parking as a network of queues. Due to the nature of…
How Much Urban Traffic is Searching for Parking? Simulating Curbside Parking as a Network of Finite Capacity Queues
Chase Dowling, Tanner Fiez, Lillian Ratliff +1
With the increasing availability of transaction data collected by digital parking meters, paid curbside parking can be advantageously modeled as a network of interdependent queues.…