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Character-Aware Neural Language Models
TLDR
A simple neural language model that relies only on character-level inputs that is able to encode, from characters only, both semantic and orthographic information and suggests that on many languages, character inputs are sufficient for language modeling.
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Estimating individual treatment effect: generalization bounds and algorithms
TLDR
A novel, simple and intuitive generalization-error bound is given showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalized-error of that representation and the distance between the treated and control distributions induced by the representation.
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Recurrent Neural Networks for Multivariate Time Series with Missing Values
TLDR
Novel deep learning models are developed based on Gated Recurrent Unit, a state-of-the-art recurrent neural network that takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results.
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Learning Representations for Counterfactual Inference
TLDR
A new algorithmic framework for counterfactual inference is proposed which brings together ideas from domain adaptation and representation learning and significantly outperforms the previous state-of-the-art approaches.
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Causal Effect Inference with Deep Latent-Variable Models
TLDR
This work builds on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect and shows its method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.
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A Practical Algorithm for Topic Modeling with Provable Guarantees
TLDR
This paper presents an algorithm for topic model inference that is both provable and practical and produces results comparable to the best MCMC implementations while running orders of magnitude faster.
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BLOG: Probabilistic Models with Unknown Objects
TLDR
This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty, and introduces a probabilistic form of Skolemization for handling evidence.
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Tightening LP Relaxations for MAP using Message Passing
TLDR
This work proposes to solve the cluster selection problem monotonically in the dual LP, iteratively selecting clusters with guaranteed improvement, and quickly re-solving with the added clusters by reusing the existing solution.
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Structured Inference Networks for Nonlinear State Space Models
TLDR
A unified algorithm is introduced to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.
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Deep Kalman Filters
TLDR
A unified algorithm is introduced to efficiently learn a broad spectrum of Kalman filters and investigates the efficacy of temporal generative models for counterfactual inference, and introduces the "Healing MNIST" dataset where long-term structure, noise and actions are applied to sequences of digits.
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