Search Results for author:
Found 19 papers, 2 papers with code
Plansformer: Generating Symbolic Plans using Transformers
Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP).
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach
Machine learning problems with multiple objective functions appear either in learning with multiple criteria where learning has to make a trade-off between multiple performance metrics such as fairness, safety and accuracy; or, in multi-task learning where multiple tasks are optimized jointly, sharing inductive bias between them.
Targeted Advertising on Social Networks Using Online Variational Tensor Regression
The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor, thereby enabling to capture the heterogeneity that may exist over user preferences, products, and campaign strategies in a unified manner.
Auto-Transfer: Learning to Route Transferrable Representations
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications.
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming
These rules are learned in an online manner and applied with an ASP solver to predict an action for the agent.
Inductive logic programming
Natural Language Understanding
+1
Case-based Reasoning for Better Generalization in Textual Reinforcement Learning
Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency.
Out-of-Distribution Generalization
reinforcement-learning
+2
Auto-Transfer: Learning to Route Transferable Representations
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labelled data can be difficult to obtain in many applications.
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL).
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents
This improves the agent's overall understanding of the game 'scene' and objects' relationships to the world around them, and the variety of visual representations on offer allow the agent to generate a better generalization of a relationship.
Thinking Fast and Slow in AI
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making.
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making.
Ranked #1 on
Commonsense Reasoning for RL
on commonsense-rl
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches
We introduce a number of RL agents that combine the sequential context with a dynamic graph representation of their beliefs of the world and commonsense knowledge from ConceptNet in different ways.
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge
In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments.
Lifelong Learning with Output Kernels
Lifelong learning poses considerable challenges in terms of effectiveness (minimizing prediction errors for all tasks) and overall computational tractability for real-time performance.
Active Learning from Peers
This paper addresses the challenge of learning from peers in an online multitask setting.
Co-Clustering for Multitask Learning
This paper presents a new multitask learning framework that learns a shared representation among the tasks, incorporating both task and feature clusters.
Self-Paced Multitask Learning with Shared Knowledge
This paper introduces self-paced task selection to multitask learning, where instances from more closely related tasks are selected in a progression of easier-to-harder tasks, to emulate an effective human education strategy, but applied to multitask machine learning.
Adaptive Smoothed Online Multi-Task Learning
This paper addresses the challenge of jointly learning both the per-task model parameters and the inter-task relationships in a multi-task online learning setting.
Multi-Task Multiple Kernel Relationship Learning
The problem is formulated as a regularization-based approach called \textit{Multi-Task Multiple Kernel Relationship Learning} (\textit{MK-MTRL}), which models the task relationship matrix from the weights learned from latent feature spaces of task-specific base kernels.
