Search Results for author:
Found 8 papers, 1 papers with code
Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model
We take the shadow price of each resource as prediction, which can be obtained by predictions on future requests.
Modeling Choice via Self-Attention
Naturally, accurate estimation of these models from data is a critical step in the application of these optimization problems in practice.
Markovian Interference in Experiments
We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited inventory).
Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise
The problem of low-rank matrix completion with heterogeneous and sub-exponential (as opposed to homogeneous and Gaussian) noise is particularly relevant to a number of applications in modern commerce.
Learning Treatment Effects in Panels with General Intervention Patterns
The problem of causal inference with panel data is a central econometric question.
Optimizing Offer Sets in Sub-Linear Time
Personalization and recommendations are now accepted as core competencies in just about every online setting, ranging from media platforms to e-commerce to social networks.
Fixing Inventory Inaccuracies At Scale
Using synthetic data and real data from a consumer goods retailer, we show that our approach provides up to a 10x cost reduction over incumbent approaches to anomaly detection.
Causal Inference With Selectively Deconfounded Data
In this paper, we consider the benefit of incorporating a large confounded observational dataset (confounder unobserved) alongside a small deconfounded observational dataset (confounder revealed) when estimating the ATE.
