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Found 17 papers, 4 papers with code
Improving LLM General Preference Alignment via Optimistic Online Mirror Descent
Reinforcement learning from human feedback (RLHF) has demonstrated remarkable effectiveness in aligning large language models (LLMs) with human preferences.
Teaching LLMs to Refine with Tools
We propose CaP, a novel approach that uses external tools to refine chain-of-thought (CoT) responses generated by the same or other LLMs.
Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors
In this paper, we propose CoDiffMob, a diffusion method for urban mobility generation with collaborative noise priors, we emphasize the critical role of noise in diffusion models for generating mobility data.
Understanding World or Predicting Future? A Comprehensive Survey of World Models
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence.
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
Specifically, we formulate the problem as a two-player game and propose a novel online algorithm, iterative Nash policy optimization (INPO).
Provably Efficient Interactive-Grounded Learning with Personalized Reward
Interactive-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in which a learner aims at maximizing unobservable rewards through interacting with an environment and observing reward-dependent feedback on the taken actions.
On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation
We study off-policy evaluation (OPE) in partially observable environments with complex observations, with the goal of developing estimators whose guarantee avoids exponential dependence on the horizon.
Efficient Contextual Bandits with Uninformed Feedback Graphs
Bandits with feedback graphs are powerful online learning models that interpolate between the full information and classic bandit problems, capturing many real-life applications.
Online Iterative Reinforcement Learning from Human Feedback with General Preference Model
We investigate Reinforcement Learning from Human Feedback (RLHF) in the context of a general preference oracle.
FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Model
Front-running attacks, a unique form of security threat, pose significant challenges to the integrity of blockchain transactions.
Offline Learning in Markov Games with General Function Approximation
We study offline multi-agent reinforcement learning (RL) in Markov games, where the goal is to learn an approximate equilibrium -- such as Nash equilibrium and (Coarse) Correlated Equilibrium -- from an offline dataset pre-collected from the game.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
For general strongly observable graphs, we develop an algorithm that achieves the optimal regret $\widetilde{\mathcal{O}}((\sum_{t=1}^T\alpha_t)^{1/2}+\max_{t\in[T]}\alpha_t)$ with high probability, where $\alpha_t$ is the independence number of the feedback graph at round $t$.
Improved Algorithms for Neural Active Learning
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.
Improving Robustness to Model Inversion Attacks via Mutual Information Regularization
This paper studies defense mechanisms against model inversion (MI) attacks -- a type of privacy attacks aimed at inferring information about the training data distribution given the access to a target machine learning model.
Convolutional Ordinal Regression Forest for Image Ordinal Estimation
We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data.
Ordinal Distribution Regression for Gait-based Age Estimation
This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation.
