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Found 12 papers, 3 papers with code
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Model Primitive Hierarchical Lifelong Reinforcement Learning
Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult.
Semiparametric discrete data regression with Monte Carlo inference and prediction
These data commonly exhibit complex distributional features such as zero-inflation, over-/under-dispersion, boundedness, and heaping, which render many parametric models inadequate.
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes
Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces.
MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning
When this methodology is used to realize grasps from coarse initial positions provided by a vision-only planner, the system is made dramatically more robust to calibration errors in the camera-robot transform.
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks
This paper aims to address this scalability challenge with a robust, sample-efficient, and general meta-IRL algorithm, SQUIRL, that performs a new but related long-horizon task robustly given only a single video demonstration.
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction
Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction.
Ranked #1 on
Video Prediction
on Cityscapes 128x128
Semiparametric count data regression for self-reported mental health
STAR is deployed to study the factors associated with self-reported mental health and demonstrates substantial improvements in goodness-of-fit compared to existing count data regression models.
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks
In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks.
Model-based Reinforcement Learning
reinforcement-learning
+1
Monte Carlo inference for semiparametric Bayesian regression
Data transformations are essential for broad applicability of parametric regression models.
Frequentist Guarantees of Distributed (Non)-Bayesian Inference
Motivated by the need to analyze large, decentralized datasets, distributed Bayesian inference has become a critical research area across multiple fields, including statistics, electrical engineering, and economics.
Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation
Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models.
