Instructional Material
An Information Theoretic Perspective on Conformal Prediction
More precisely, we prove three different ways to upper bound the intrinsic uncertainty, as described by the conditional entropy of the target variable given the inputs, by combining CP with information theoretical inequalities.
Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale
LLMs can now act as autonomous agents that interact with digital environments and complete specific objectives ( e.g., arranging an online meeting).
Causal Imitation for Markov Decision Processes: a Partial Identification Approach
Children often learn how to behave in an unfamiliar environment by imitating adults. Imitation learning (IL) enables a learning agent to behave in an unknown environment by observing expert demonstrations.
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
LLMs' flexibility is largely due to an "in-context learning" effect that emerges when
Optimization Algorithm Design via Electric Circuits
Lead authors (author list ordered alphabetically) 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
While humans accomplish 72.4% of the tasks, the best
HEMM: Holistic Evaluation of Multimodal Foundation Models Paul Pu Liang
To address this need, we contribute Holistic Evaluation of Multimodal Models ( HEMM), visualized in Figure 1. HEMM, as an evaluation framework, goes beyond conventional lists of datasets to emphasize holistic benchmarking at three levels.