South America
Adversarial Learning for Feature Shift Detection and Correction
Distribution shifts in multi-dimensional data, caused by one or more "corrupted" dimensions, are
MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering Yizhen Luo
Studying protein mutations within amino acid sequences holds tremendous significance in life sciences.
Mixture of In-Context Experts Enhance LLMs' Long Context Awareness
Hongzhan Lin and Ang Lv proposed the idea of MoICE.
Better with Less
The proposed predictive uncertainty, as feedback from the pre-training model, measures the confidence level of the model in the data. When fed with the chosen data, on the other hand, the pre-training model grasps an initial understanding of the new, unseen data, and at the same time attempts to remember the knowledge learned from previous data.
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL Andrew Wagenmaker
Such direct sim2real transfer is not guaranteed to succeed, however, and in cases where it fails, it is unclear how to best utilize the simulator. In this work, we show that in many regimes, while direct sim2real transfer may fail, we can utilize the simulator to learn a set of exploratory policies which enable efficient exploration in the real world.
Achieving O (1 /ε) Sample Complexity for Constrained Markov Decision Process
We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making.
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds Jiayi Huang 1 Han Zhong 1 Liwei Wang
Our contributions are summarized as follows.