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Found 10 papers, 5 papers with code
The Geometry of Culture: Analyzing Meaning through Word Embeddings
We demonstrate the utility of a new methodological tool, neural-network word embedding models, for large-scale text analysis, revealing how these models produce richer insights into cultural associations and categories than possible with prior methods.
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
We consider dynamic pricing with many products under an evolving but low-dimensional demand model.
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence
The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the base treatment and take the cross-fitted residuals.
Accurate Inference for Adaptive Linear Models
Estimators computed from adaptively collected data do not behave like their non-adaptive brethren.
Deep IV: A Flexible Approach for Counterfactual Prediction
Counterfactual prediction requires understanding causal relationships between so-called treatment and outcome variables.
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
We present a multi-view Bayesian non-parametric algorithm which improves multi-sense word embeddings by (a) using multilingual (i. e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner.
Counterfactual Prediction with Deep Instrumental Variables Networks
We are in the middle of a remarkable rise in the use and capability of artificial intelligence.
Document Classification by Inversion of Distributed Language Representations
There have been many recent advances in the structure and measurement of distributed language models: those that map from words to a vector-space that is rich in information about word choice and composition.
Bayesian and empirical Bayesian forests
We derive ensembles of decision trees through a nonparametric Bayesian model, allowing us to view random forests as samples from a posterior distribution.
Applications
Multinomial Inverse Regression for Text Analysis
Multinomial inverse regression is introduced as a general tool for simplifying predictor sets that can be represented as draws from a multinomial distribution, and we show that logistic regression of phrase counts onto document annotations can be used to obtain low dimension document representations that are rich in sentiment information.
Methodology
