Search Results for author:
Found 7 papers, 2 papers with code
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective.
Message-Enhanced DeGroot Model
The Message-Enhanced DeGroot model, combining the Bounded Brownian Message model with the traditional DeGroot model, quantitatively describes the evolution of agents' opinions under the influence of messages.
Herd Behavior in Optimal Investment: A Dual-Agent Approach with Investment Opinion and Rational Decision Decomposition
Furthermore, to quantify the preference for their rational decision over that of the other agent, we introduce the agent's investment opinion.
From Trojan Horses to Castle Walls: Unveiling Bilateral Backdoor Effects in Diffusion Models
We show that this latter insight can be used to enhance the detection of backdoor-poisoned training data.
Spread Control Method on Unknown Networks Based on Hierarchical Reinforcement Learning
Epidemics such as COVID-19 pose serious threats to public health and our society, and it is critical to investigate effective methods to control the spread of epidemics over networks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Pacos: Modeling Users' Interpretable and Context-Dependent Choices in Preference Reversals
Choice problems refer to selecting the best choices from several items, and learning users' preferences in choice problems is of great significance in understanding the decision making mechanisms and providing personalized services.
Probe: Learning Users' Personalized Projection Bias in Intertemporal Choices
In this work, we specifically focus on two commonly observed biases: projection bias and the reference-point effect.
