In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
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Question: Many processes can be concisely represented as a sequence of events leading from a starting state to an end state. Given raw ingredients, and a finished cake, an experienced chef can surmise the recipe. Building upon this intuition, we propose a new class of visual generative models: goal-conditioned predictors (GCP). Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video. GCP instead treats videos as start-goal transformations, making video generation easier by conditioning on the more informative context provided by the first and final frames. Not only do existing forward prediction approaches synthesize better and longer videos when modified to become goal-conditioned, but GCP models can also utilize structures that are not linear in time, to accomplish hierarchical prediction . To this end, we study both auto-regressive GCP models and novel tree-structured GCP models that generate frames recursively, splitting the video iteratively into finer and finer segments delineated by subgoals . In experiments across simulated and real datasets, our GCP methods generate high-quality sequences over long horizons . Tree-structured GCPs are also substantially easier to parallelize than auto-regressive GCPs, making training and inference very efficient, and allowing the model to train on sequences that are thousands of frames in length.Finally, we demonstrate the utility of GCP approaches for imitation learning in the setting without access to expert actions . Videos are on the supplementary website: https://sites.google.com/view/video-gcp

Answer: We propose a new class of visual generative models: goal-conditioned predictors. We show experimentally that conditioning on the goal allows to reduce uncertainty and produce predictions over much longer horizons.

Question: Sequential data often originates from diverse environments. Across them exist both shared regularities and environment specifics. To learn robust cross-environment descriptions of sequences we introduce disentangled state space models (DSSM). In the latent space of DSSM environment-invariant state dynamics is explicitly disentangled from environment-specific information governing that dynamics. We empirically show that such separation enables robust prediction, sequence manipulation and environment characterization. We also propose an unsupervised VAE-based training procedure to learn DSSM as Bayesian filters. In our experiments, we demonstrate state-of-the-art performance in controlled generation and prediction of bouncing ball video sequences across varying gravitational influences.

Answer: DISENTANGLED STATE SPACE MODELS

Question: We develop the Y-learner for estimating heterogeneous treatment effects in experimental and observational studies. The Y-learner is designed to leverage the abilities of neural networks to optimize multiple objectives and continually update, which allows for better pooling of underlying feature information between treatment and control groups. We evaluate the Y-learner on three test problems: (1) A set of six simulated data benchmarks from the literature. (2) A real-world large-scale experiment on voter persuasion. (3) A task from the literature that estimates artificially generated treatment effects on MNIST didgits. The Y-learner achieves state of the art results on two of the three tasks. On the MNIST task, it gets the second best results.

Answer:
We develop a CATE estimation strategy that takes advantage some of the intriguing properties of neural networks.