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Found 8 papers, 2 papers with code
Optimal Design for Human Feedback
Learning of preference models from human feedback has been central to recent advances in artificial intelligence.
Experimental Design for Active Transductive Inference in Large Language Models
We design the LLM prompt by adaptively choosing few-shot examples for a given inference query.
Pessimistic Off-Policy Multi-Objective Optimization
The pessimistic estimator can be optimized by policy gradients and performs well in all of our experiments.
Bridging Recommendation and Marketing via Recurrent Intensity Modeling
RIM allows us to rethink recommendation in a Matching (Mtch) scenario, where the benefits of the users (e. g., ItemRec relevance) and item providers (e. g., item-exposure guarantees) are considered at the same time.
Maximum n-times Coverage for Vaccine Design
We introduce the maximum $n$-times coverage problem that selects $k$ overlays to maximize the summed coverage of weighted elements, where each element must be covered at least $n$ times.
Information Condensing Active Learning
We introduce Information Condensing Active Learning (ICAL), a batch mode model agnostic Active Learning (AL) method targeted at Deep Bayesian Active Learning that focuses on acquiring labels for points which have as much information as possible about the still unacquired points.
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments.
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles
The inaccuracy of neural network models on inputs that do not stem from the training data distribution is both problematic and at times unrecognized.
