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Found 14 papers, 2 papers with code
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals.
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking
Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods.
Vector-Matrix-Vector Queries for Solving Linear Algebra, Statistics, and Graph Problems
We consider the general problem of learning about a matrix through vector-matrix-vector queries.
Analysis of Alignment Phenomenon in Simple Teacher-student Networks with Finite Width
Recent theoretical analysis suggests that ultra-wide neural networks always converge to global minima near the initialization under first order methods.
Average-Case Communication Complexity of Statistical Problems
Our motivation is to understand the statistical-computational trade-offs in streaming, sketching, and query-based models.
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Offline reinforcement learning (RL), which refers to decision-making from a previously-collected dataset of interactions, has received significant attention over the past years.
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient data coverage.
Provably Efficient Reinforcement Learning via Surprise Bound
Value function approximation is important in modern reinforcement learning (RL) problems especially when the state space is (infinitely) large.
On Representation Complexity of Model-based and Model-free Reinforcement Learning
To the best of our knowledge, this work is the first to study the circuit complexity of RL, which also provides a rigorous framework for future research.
Learning Personalized Story Evaluation
We further develop a personalized story evaluation model PERSE to infer reviewer preferences and provide a personalized evaluation.
End-to-end Story Plot Generator
Story plots, while short, carry most of the essential information of a full story that may contain tens of thousands of words.
Towards Optimal Statistical Watermarking
Key to our formulation is a coupling of the output tokens and the rejection region, realized by pseudo-random generators in practice, that allows non-trivial trade-offs between the Type I error and Type II error.
Efficient Prompt Caching via Embedding Similarity
In this paper, we aim to improve the inference efficiency of LLMs by prompt caching, i. e., if the current prompt can be answered by the same response of a previous prompt, one can directly utilize that previous response without calling the LLM.
Avoiding Catastrophe in Continuous Spaces by Asking for Help
Specifically, we assume that the payoff each round represents the chance of avoiding catastrophe that round, and try to maximize the product of payoffs (the overall chance of avoiding catastrophe).
