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Found 6 papers, 2 papers with code
Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations
Existing solutions either introduce a regularization term to improve the smoothness of the trained policy against perturbations or alternatively train the agent's policy and the attacker's policy.
Enhancing LLM Safety via Constrained Direct Preference Optimization
Empirically, our approach provides a safety guarantee to LLMs that is missing in DPO while achieving significantly higher rewards under the same safety constraint compared to a recently proposed safe RLHF approach.
Pandering in a Flexible Representative Democracy
We introduce a novel formal model of \emph{pandering}, or strategic preference reporting by candidates seeking to be elected, and examine the resilience of two democratic voting systems to pandering within a single round and across multiple rounds.
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach
We propose a new estimator for heterogeneous treatment effects in a partially linear model (PLM) with many exogenous covariates and a possibly endogenous treatment variable.
An exact solution in Markov decision process with multiplicative rewards as a general framework
We first review the exact solution of conventional linear quadratic regulation with a linear transition and a Gaussian noise, whose optimal policy does not depend on the Gaussian noise, which is an undesired feature in the presence of significant noises.
Leveraging Legacy Data to Accelerate Materials Design via Preference Learning
Machine learning applications in materials science are often hampered by shortage of experimental data.
