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AEA 2023, NABE 20232023In order to improve prices at Amazon, we created Pricing Labs, a price experimentation platform. Since we do not price discriminate, we must run product-randomized experiments. We discuss how we randomize to prevent spillovers, run different experimental designs (i.e., crossovers) to improve precision, and control for demand trends and differences in treatment groups to get more precise treatment effect
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NABE 20222022We propose a novel architecture for time series models built upon state-space methods. We jointly estimate many, potentially multivariate, distributions defined using state-space models by partially pooling their parameters across the cross-section. These joint distributions define a novel recurrent neural network. By combining state-space methods and neural networks, we leverage the interpretability of
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ICML 20222022The fundamental challenge of drawing causal inference is that counterfactual outcomes are not fully observed for any unit. Furthermore, in observational studies, treatment assignment is likely to be confounded. Many statistical methods have emerged for causal inference under unconfoundedness conditions given pre-treatment covariates, including: propensity score-based methods, prognostic score-based methods
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GCPR 20222022Generation of photo-realistic images, semantic editing and representation learning are only a few of many applications of high- resolution generative models. Recent progress in GANs have established them as an excellent choice for such tasks. However, since they do not provide an inference model, downstream tasks such as classification cannot be easily applied on real images using the GAN latent space.
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Interspeech 20222022Generating expressive and contextually appropriate prosody remains a challenge for modern text-to-speech (TTS) systems. This is particularly evident for long, multi-sentence inputs. In this paper, we examine simple extensions to a Transformer-based FastSpeech-like system, with the goal of improving prosody for multi-sentence TTS. We find that long context, powerful text features, and training on multi-speaker
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