Education
Smoothed Online Classification can be Harder than Batch Classification
We study online classification under smoothed adversaries.
Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning David Y unis TTI-Chicago Chicago, IL
Exploration in sparse-reward reinforcement learning (RL) is difficult due to the need for long, coordinated sequences of actions in order to achieve any reward.
Generating a Diversity of Challenging Programming Puzzles with Autotelic Generative Models
Chen et al., 2021], and come up with new problems that drive innovation in art and science [Gromov,
Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting
In detail, we utilize foundation visual perception models to obtain each generated object from the image and leverage pre-trained depth estimation models to lift the generated 2D image to 3D space.
Knowledge Composition using Task Vectors with Learned Anisotropic Scaling Frederic Z. Zhang Paul Albert
Listed order was determined by a coin toss.
OneBit: Towards Extremely Low-bit Large Language Models
LLMs difficult beyond mid-to-high-end GPUs like the A100, let alone on mobile devices. The high demand for resources not only drives up usage costs, but also restricts their wider application.
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
As a prominent category of imitation learning methods, adversarial imitation learning (AIL) has garnered significant practical success powered by neural network approximation. However, existing theoretical studies on AIL are primarily limited to simplified scenarios such as tabular and linear function approximation and involve complex algorithmic designs that hinder practical implementation, highlighting a gap between theory and practice.