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Found 18 papers, 6 papers with code
Hybrid Square Neural ODE Causal Modeling
We encode this information in a causal loss that we combine with the standard predictive loss to arrive at a hybrid loss that biases our learning towards causally valid hybrid models.
Experimenting under Stochastic Congestion
We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply and/or demand.
Online Learning for Traffic Routing under Unknown Preferences
at each period, we show that our approach obtains an expected regret and road capacity violation of $O(\sqrt{T})$, where $T$ is the number of periods over which tolls are updated.
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
We study the structure of regret-minimizing policies in the {\em many-armed} Bayesian multi-armed bandit problem: in particular, with $k$ the number of arms and $T$ the time horizon, we consider the case where $k \geq \sqrt{T}$.
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
This finding diverges from the notion of free exploration, which relates to covariate variation, as recently discussed in contextual bandit literature.
Quality Selection in Two-Sided Markets: A Constrained Price Discrimination Approach
In this paper we study the following information disclosure problem in two-sided markets: If a platform wants to maximize revenue, which sellers should the platform allow to participate, and how much of its available information about participating sellers' quality should the platform share with buyers?
Semi-parametric dynamic contextual pricing
Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product.
Exploration vs. Exploitation in Team Formation
An online labor platform faces an online learning problem in matching workers with jobs and using the performance on these jobs to create better future matches.
Designing Optimal Binary Rating Systems
Modern online platforms rely on effective rating systems to learn about items.
Optimal Testing in the Experiment-rich Regime
Motivated by the widespread adoption of large-scale A/B testing in industry, we propose a new experimentation framework for the setting where potential experiments are abundant (i. e., many hypotheses are available to test), and observations are costly; we refer to this as the experiment-rich regime.
Learning with Abandonment
Consider a platform that wants to learn a personalized policy for each user, but the platform faces the risk of a user abandoning the platform if she is dissatisfied with the actions of the platform.
Bandit Learning with Positive Externalities
In other words, arrivals exhibit positive externalities.
Regret of Queueing Bandits
A naive view of this problem would suggest that queue-regret should grow logarithmically: since queue-regret cannot be larger than classical regret, results for the standard MAB problem give algorithms that ensure queue-regret increases no more than logarithmically in time.
Matching while Learning
We introduce a benchmark model with heterogeneous "workers" (demand) and a limited supply of "jobs" that arrive over time.
Online Active Linear Regression via Thresholding
We consider the problem of online active learning to collect data for regression modeling.
Pricing and Referrals in Diffusion on Networks
When a new product or technology is introduced, potential consumers can learn its quality by trying the product, at a risk, or by letting others try it and free-riding on the information that they generate.
Economics Computer Science and Game Theory Social and Information Networks Physics and Society 91D30, 05C82, 91A43
Committing Bandits
In the second phase the decision maker has to commit to one of the arms and stick with it.
