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Found 14 papers, 3 papers with code
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees
We study a primal-dual reinforcement learning (RL) algorithm for the online constrained Markov decision processes (CMDP) problem, wherein the agent explores an optimal policy that maximizes return while satisfying constraints.
Towards Autonomous Hypothesis Verification via Language Models with Minimal Guidance
Research automation efforts usually employ AI as a tool to automate specific tasks within the research process.
LPML: LLM-Prompting Markup Language for Mathematical Reasoning
In this paper, we propose a novel framework that integrates the Chain-of-Thought (CoT) method with an external tool (Python REPL).
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performing practical RL algorithms.
Langevin Autoencoders for Learning Deep Latent Variable Models
Based on the ALD, we also present a new deep latent variable model named the Langevin autoencoder (LAE).
Equivariant and Invariant Reynolds Networks
We construct learning models based on the reductive Reynolds operator called equivariant and invariant Reynolds networks (ReyNets) and prove that they have universal approximation property.
Reynolds Equivariant and Invariant Networks
To overcome this difficulty, we consider representing the Reynolds operator as a sum over a subset instead of a sum over the whole group.
Group Equivariant Conditional Neural Processes
We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural processes (CNPs), and it also has transformation equivariance in data space.
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation
We report the computational efficiency and statistical reliability of our method in numerical experiments of the language modeling using RNNs, and the out-of-distribution detection with DNNs.
Universal Approximation Theorem for Equivariant Maps by Group CNNs
However, universal approximation theorems for CNNs have been separately derived with individual techniques according to each group and setting.
Variable Selection for Nonparametric Learning with Power Series Kernels
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation.
Regret Analysis for Continuous Dueling Bandit
The dueling bandit is a learning framework wherein the feedback information in the learning process is restricted to a noisy comparison between a pair of actions.
Learning Bound for Parameter Transfer Learning
We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task.
Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle
Our algorithm consists of two steps; one is the direction estimate step and the other is the search step.
