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Found 16 papers, 3 papers with code
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
Ranked #1 on
Drug Discovery
on LIT-PCBA(KAT2A)
Self-Supervised Contrastive Pre-Training for Multivariate Point Processes
Self-supervision is one of the hallmarks of representation learning in the increasingly popular suite of foundation models including large language models such as BERT and GPT-3, but it has not been pursued in the context of multivariate event streams, to the best of our knowledge.
Adaptive Primal-Dual Method for Safe Reinforcement Learning
In this paper, we propose, analyze and evaluate adaptive primal-dual (APD) methods for SRL, where two adaptive LRs are adjusted to the Lagrangian multipliers so as to optimize the policy in each iteration.
Matching Table Metadata with Business Glossaries Using Large Language Models
The resulting matching enables the use of an available or curated business glossary for retrieval and analysis without or before requesting access to the data contents.
Probabilistic Constraint for Safety-Critical Reinforcement Learning
Furthermore, we propose a Safe Primal-Dual algorithm that can leverage both SPGs to learn safe policies.
The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
However, the dynamic (i. e., input-dependent) nature of these pathways makes it difficult to prune dense self-attention during training.
Ranked #12 on
Graph Regression
on PCQM4Mv2-LSC
AutoDOViz: Human-Centered Automation for Decision Optimization
AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts.
Policy Gradients for Probabilistic Constrained Reinforcement Learning
In particular, we consider the notion of probabilistic safety.
Learning Temporal Rules from Noisy Timeseries Data
Events across a timeline are a common data representation, seen in different temporal modalities.
Neural Temporal Logic Programming
Events across a timeline are a common data representation, seen in different temporal modalities.
Global Self-Attention as a Replacement for Graph Convolution
The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on graph-structured data.
Ranked #1 on
Graph Regression
on PCQM4Mv2-LSC
A Multi-Channel Neural Graphical Event Model with Negative Evidence
Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains.
Proximal Graphical Event Models
Event datasets include events that occur irregularly over the timeline and are prevalent in numerous domains.
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning
We describe how to use robust Markov decision processes for value function approximation with state aggregation.
Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation
We propose solution methods for previously-unsolved constrained MDPs in which actions can continuously modify the transition probabilities within some acceptable sets.
