Search Results for author:
Found 28 papers, 15 papers with code
Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics
In this work, we investigate the statistical properties of the kernel loss, which allows us to find a feasible set that contains the true value function with high probability.
Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM).
FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability
This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capability for their application as AI agents.
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
It meticulously standardizes and unifies these trajectories into a consistent format, streamlining the creation of a generic data loader optimized for agent training.
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs).
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
This demonstrates that using policy gradient optimization to improve language agents, for which we believe our work is one of the first, seems promising and can be applied to optimize other models in the agent architecture to enhance agent performances over time.
REX: Rapid Exploration and eXploitation for AI Agents
In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX.
FAMO: Fast Adaptive Multitask Optimization
One of the grand enduring goals of AI is to create generalist agents that can learn multiple different tasks from diverse data via multitask learning (MTL).
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild
Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when prompted with arbitrary languages.
HIVE: Harnessing Human Feedback for Instructional Visual Editing
Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences.
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems
Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied.
A Unified Framework for Alternating Offline Model Training and Policy Learning
In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model from historically collected data, and subsequently utilize the learned model and fixed datasets for policy learning, without further interacting with the environment.
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning
Furthermore, we introduce the metric residual network (MRN) that deliberately decomposes the action-value function Q(s, a, g) into the negated summation of a metric plus a residual asymmetric component.
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learning from static datasets, without interacting with the underlying environment during the learning process.
A Behavior Regularized Implicit Policy for Offline Reinforcement Learning
For training more effective agents, we propose a framework that supports learning a flexible yet well-regularized fully-implicit policy.
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning
The benefit of learning the operator is that we can incorporate any new reward function as input and attain its corresponding value function in a zero-shot manner.
Unsupervised Out-of-Domain Detection via Pre-trained Transformers
Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs.
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System
Two major challenges exist in this new task: (i) For the learning process, the system should incrementally learn new classes round by round without re-training on the examples of preceding classes; (ii) For the performance, the system should perform well on new classes without much loss on preceding classes.
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies.
Off-Policy Interval Estimation with Lipschitz Value Iteration
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data.
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
We consider off-policy evaluation (OPE), which evaluates the performance of a new policy from observed data collected from previous experiments, without requiring the execution of the new policy.
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Our method is doubly robust in that the bias vanishes when either the density ratio or the value function estimation is perfect.
A Kernel Loss for Solving the Bellman Equation
Value function learning plays a central role in many state-of-the-art reinforcement-learning algorithms.
Knowledge-guided Semantic Computing Network
Besides, to enhance the recognition ability of the semantic tree in aspects of the diversity, randomicity and variability, we use the traditional neural network to aid the semantic tree to learn some indescribable features.
Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning
Deep reinforcement learning has achieved remarkable successes in solving various challenging artificial intelligence tasks.
Action-depedent Control Variates for Policy Optimization via Stein's Identity
Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems.
Learning to Draw Samples with Amortized Stein Variational Gradient Descent
We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference.
Two Methods For Wild Variational Inference
Variational inference provides a powerful tool for approximate probabilistic in- ference on complex, structured models.
