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Found 19 papers, 1 papers with code
Analysis of the Order Flow Auction under Proposer-Builder Separation
In this paper, we consider the impact of the order flow auction (OFA) in the context of the proposer-builder separation (PBS) mechanism through a game-theoretic perspective.
Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning
Reinforcement learning from human feedback (RLHF), which aligns a diffusion model with input prompt, has become a crucial step in building reliable generative AI models.
Regret of exploratory policy improvement and $q$-learning
We study the convergence of $q$-learning and related algorithms introduced by Jia and Zhou (J. Mach.
RainbowPO: A Unified Framework for Combining Improvements in Preference Optimization
Recently, numerous preference optimization algorithms have been introduced as extensions to the Direct Preference Optimization (DPO) family.
Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey
Preference tuning is a crucial process for aligning deep generative models with human preferences.
Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning
Reinforcement Learning from human feedback (RLHF) has been shown a promising direction for aligning generative models with human intent and has also been explored in recent works for alignment of diffusion generative models.
MallowsPO: Fine-Tune Your LLM with Preference Dispersions
Direct Preference Optimization (DPO) has recently emerged as a popular approach to improve reinforcement learning with human feedback (RLHF), leading to better techniques to fine-tune large language models (LLM).
Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond
This paper aims to develop and provide a rigorous treatment to the problem of entropy regularized fine-tuning in the context of continuous-time diffusion models, which was recently proposed by Uehara et al. (arXiv:2402. 15194, 2024).
Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial
This is an expository article on the score-based diffusion models, with a particular focus on the formulation via stochastic differential equations (SDE).
Contractive Diffusion Probabilistic Models
In view of possibly unguaranteed score matching, we propose a new criterion -- the contraction of backward sampling in the design of DPMs, leading to a novel class of contractive DPMs (CDPMs).
Trading and wealth evolution in the Proof of Stake protocol
With the increasing adoption of the Proof of Stake (PoS) blockchain, it is timely to study the economy created by such blockchain.
Trading under the Proof-of-Stake Protocol -- a Continuous-Time Control Approach
In particular, we show when a participant is risk-neutral or risk-seeking, corresponding to the risk-adjusted valuation being a martingale or a sub-martingale, the optimal strategy must be to either buy all the time, sell all the time, or first buy then sell, and with both buying and selling executed at full capacity.
Stability of shares in the Proof of Stake Protocol -- Concentration and Phase Transitions
This paper is concerned with the stability of shares in a cryptocurrency where the new coins are issued according to the Proof of Stake protocol.
Asset Selection via Correlation Blockmodel Clustering
Finally, we conduct an empirical analysis to verify the performance of the algorithm.
Simulated annealing from continuum to discretization: a convergence analysis via the Eyring--Kramers law
in cumulative step size), and provide an explicit rate as a function of the model parameters.
Learning an arbitrary mixture of two multinomial logits
In this paper, we consider mixtures of multinomial logistic models (MNL), which are known to $\epsilon$-approximate any random utility model.
Escaping Saddle Points Efficiently with Occupation-Time-Adapted Perturbations
Motivated by the super-diffusivity of self-repelling random walk, which has roots in statistical physics, this paper develops a new perturbation mechanism for optimization algorithms.
On identifiability and consistency of the nugget in Gaussian spatial process models
We formally establish results on the identifiability and consistency of the nugget in spatial models based upon the Gaussian process within the framework of in-fill asymptotics, i. e. the sample size increases within a sampling domain that is bounded.
Spatial Interpolation
Statistics Theory
Statistics Theory
A class of stochastic games and moving free boundary problems
In this paper we propose and analyze a class of $N$-player stochastic games that include finite fuel stochastic games as a special case.
