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Found 25 papers, 7 papers with code
Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS
The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology.
Context-Former: Stitching via Latent Conditioned Sequence Modeling
On the other hand, Decision Transformer (DT) abstracts the decision-making as sequence modeling, showcasing competitive performance on offline RL benchmarks, however, recent studies demonstrate that DT lacks of stitching capability, thus exploit stitching capability for DT is vital to further improve its performance.
Deep Dict: Deep Learning-based Lossy Time Series Compressor for IoT Data
We propose Deep Dict, a deep learning-based lossy time series compressor designed to achieve a high compression ratio while maintaining decompression error within a predefined range.
Probing Structured Semantics Understanding and Generation of Language Models via Question Answering
Extensive experiments with models of different sizes and in different formal languages show that today's state-of-the-art LLMs' understanding of the logical forms can approach human level overall, but there still are plenty of room in generating correct logical forms, which suggest that it is more effective to use LLMs to generate more natural language training data to reinforce a small model than directly answering questions with LLMs.
A dynamical clipping approach with task feedback for Proximal Policy Optimization
Different from previous clipping approaches, we consider increasing the maximum cumulative Return in reinforcement learning (RL) tasks as the preference of the RL task, and propose a bi-level proximal policy optimization paradigm, which involves not only optimizing the policy but also dynamically adjusting the clipping bound to reflect the preference of the RL tasks to further elevate the training outcomes and stability of PPO.
Offline Imitation Learning with Variational Counterfactual Reasoning
We theoretically analyze the influence of the generated expert data and the improvement of generalization.
Sample Efficient Reward Augmentation in offline-to-online Reinforcement Learning
Offline-to-online RL can make full use of pre-collected offline datasets to initialize policies, resulting in higher sample efficiency and better performance compared to only using online algorithms alone for policy training.
Multidomain transformer-based deep learning for early detection of network intrusion
Timely response of Network Intrusion Detection Systems (NIDS) is constrained by the flow generation process which requires accumulation of network packets.
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Due to the existence of similarity trap, such consistency regularization improperly enhances the consistency possiblity of the model's predictions between segment pairs, and thus reduces the confidence in reward learning, since the augmented distribution does not match with the original one in PbRL.
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
To resolve these challenges, we cast the chip placement as an offline RL formulation and present ChiPFormer that enables learning a transferable placement policy from fixed offline data.
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Naturally, such a paradigm raises three core questions that are not fully answered by prior non-iterative offline RL counterparts like reward-conditioned policy: (q1) What information should we transfer from the inner-level to the outer-level?
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
As a remedy for the labor-intensive labeling, we propose to endow offline RL tasks with a few expert data and utilize the limited expert data to drive intrinsic rewards, thus eliminating the need for extrinsic rewards.
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data.
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
Beyond Reward: Offline Preference-guided Policy Optimization
Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively.
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction
In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously.
Behavior Proximal Policy Optimization
Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs.
ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction
Document-level relation extraction with graph neural networks faces a fundamental graph construction gap between training and inference - the golden graph structure only available during training, which causes that most methods adopt heuristic or syntactic rules to construct a prior graph as a pseudo proxy.
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats
Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics.
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
Specifically, DARA emphasizes learning from those source transition pairs that are adaptive for the target environment and mitigates the offline dynamics shift by characterizing state-action-next-state pairs instead of the typical state-action distribution sketched by prior offline RL methods.
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals and learn the goal-conditioned policy.
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain
To achieve this, the widely used GAN-inspired IRL method is adopted, and its discriminator, recognizing policy-generating trajectories, is modified with the quantification of dynamics difference.
Risk-Aware Fine-Grained Access Control in Cyber-Physical Contexts
The risk levels are associated with access control decisions recommended by a security policy.
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
It is of significance for an agent to learn a widely applicable and general-purpose policy that can achieve diverse goals including images and text descriptions.
Learning transitional skills with intrinsic motivation
By maximizing an information theoretic objective, a few recent methods empower the agent to explore the environment and learn useful skills without supervision.
