Search Results for author:
Found 21 papers, 12 papers with code
Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
We introduce PrefEval, a benchmark for evaluating LLMs' ability to infer, memorize and adhere to user preferences in a long-context conversational setting.
Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents
We validate PAE on challenging vision-based web navigation, using both real-world and self-hosted websites from WebVoyager and WebArena. To the best of our knowledge, this work represents the first effective learning system to apply autonomous task proposal with RL for agents that generalizes real-world human-annotated benchmarks with SOTA performances.
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk
This metric is used to filter the generated conversational data that is fed back in LLM for training.
Automated Few-shot Classification with Instruction-Finetuned Language Models
We observe, in the context of classification tasks, that instruction finetuned language models exhibit remarkable prompt robustness, and we subsequently propose a simple method to eliminate the need for handcrafted prompts, named AuT-Few.
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic
While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a challenging problem owing to the lack of sufficient monolingual dialectal data and parallel translation data of such dialectal variants.
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning
We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively.
Learning to Act with Affordance-Aware Multimodal Neural SLAM
With the proposed Affordance-aware Multimodal Neural SLAM (AMSLAM) approach, we obtain more than 40% improvement over prior published work on the ALFRED benchmark and set a new state-of-the-art generalization performance at a success rate of 23. 48% on the test unseen scenes.
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning
We present a two-step hybrid reinforcement learning (RL) policy that is designed to generate interpretable and robust hierarchical policies on the RL problem with graph-based input.
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts.
Off-Policy Imitation Learning from Observations
To further accelerate the learning procedure, we regulate the policy update with an inverse action model, which assists distribution matching from the perspective of mode-covering.
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders
Unsupervised anomaly detection plays a crucial role in many critical applications.
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Transfer Learning in Deep Reinforcement Learning: A Survey
Reinforcement learning is a learning paradigm for solving sequential decision-making problems.
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg
For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in convex settings.
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
SAIL bridges the advantages of IL and RL to reduce the sample complexity substantially, by effectively exploiting sup-optimal demonstrations and efficiently exploring the environment to surpass the demonstrated performance.
Ranking Policy Gradient
Sample inefficiency is a long-lasting problem in reinforcement learning (RL).
Differentially Private Generative Adversarial Network
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models.
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet Management
Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency.
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation
Even though there is mild cognitive decline in MCI patients, they have normal overall cognition and thus is challenging to distinguish from normal aging.
Collaborative Deep Reinforcement Learning
Motivated by human collaborative learning, in this paper we propose a collaborative deep reinforcement learning (CDRL) framework that performs adaptive knowledge transfer among heterogeneous learning agents.
