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Found 9 papers, 1 papers with code
Estimating Class Separability of Datasets Using Persistent Homology with Application to LLM Fine-Tuning
This paper proposes a method to estimate the class separability of an unlabeled text dataset by inspecting the topological characteristics of sentence-transformer embeddings of the text.
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning
A key challenge for a reinforcement learning (RL) agent is to incorporate external/expert1 advice in its learning.
Topical: Learning Repository Embeddings from Source Code using Attention
This paper presents Topical, a novel deep neural network for repository level embeddings.
Learning to Be Cautious
An agent that could learn to be cautious would overcome this challenge by discovering for itself when and how to behave cautiously.
Useful Policy Invariant Shaping from Arbitrary Advice
Furthermore, if the reward is constructed from a potential function, the optimal policy is guaranteed to be unaltered.
Exploiting Submodular Value Functions For Scaling Up Active Perception
Furthermore, we show that, under certain conditions, including submodularity, the value function computed using greedy PBVI is guaranteed to have bounded error with respect to the optimal value function.
Real-Time Resource Allocation for Tracking Systems
Automated tracking is key to many computer vision applications.
Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent's uncertainty.
Probably Approximately Correct Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection
Submodular function maximization finds application in a variety of real-world decision-making problems.
