Search Results for author:
Found 16 papers, 2 papers with code
Finding Sub-task Structure with Natural Language Instruction
When mapping a natural language instruction to a sequence of actions, it is often useful toidentify sub-tasks in the instruction.
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks.
Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO
Scientific computing has experienced a surge empowered by advancements in technologies such as neural networks.
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
Our approach allows the user to provide trajectory-level feedback on agent's behavior during training, which can be integrated as a reward shaping signal in the following training iteration.
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations
NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving.
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.
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context.
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function.
Logical Credal Networks
This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability.
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements.
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
Counting the Optimal Solutions in Graphical Models
We introduce #opt, a new inference task for graphical models which calls for counting the number of optimal solutions of the model.
Generating Dialogue Agents via Automated Planning
Dialogue systems have many applications such as customer support or question answering.
From Stochastic Planning to Marginal MAP
This yields a novel algebraic gradient-based solver (AGS) for MMAP.
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models
The paper presents and evaluates the power of parallel search for exact MAP inference in graphical models.
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models
We provide two algorithms for compiling the AOMDD of a graphical model.
