Search Results for author:
Found 18 papers, 7 papers with code
Generalization Error Bounds for Learning under Censored Feedback
We then use this CDF error bound to provide a bound on the generalization error guarantees of a classifier trained on such non-IID data.
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
(3) However, algorithms such as FedDyn and SCAFFOLD are more prone to catastrophic failures without the support of additional techniques such as gradient clipping.
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
We show that subsets that closely preserve the cross-covariance of the images and captions of the full data provably achieve a superior generalization performance.
Prompt Mining for Language-based Human Mobility Forecasting
Specifically, the framework includes a prompt generation stage based on the information entropy of prompts and a prompt refinement stage to integrate mechanisms such as the chain of thought.
When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method.
Large Language Models Can Learn Temporal Reasoning
Instead of reasoning over the original context, we adopt a latent representation, temporal graph (TG) that facilitates the TR learning.
TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning
We first convert TKGs into a temporal event knowledge graph (TEKG) which has a more explicit representation of time in term of nodes of the graph.
Investigating the Impact of Weight Sharing Decisions on Knowledge Transfer in Continual Learning
Through this lens we demonstrate how task complexity and similarity influence the optimal weight sharing decisions, giving insights into the relationships between tasks and helping inform decision making in similar CL methods.
Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models
This work investigates the potential of undermining both fairness and detection performance in abusive language detection.
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature.
Few-shot Adaption to Distribution Shifts By Mixing Source and Target Embeddings
Pretrained machine learning models need to be adapted to distribution shifts when deployed in new target environments.
Text-to-SQL Error Correction with Language Models of Code
Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code.
Mitigating Group Bias in Federated Learning: Beyond Local Fairness
While many mitigation strategies have been proposed in centralized learning, many of these methods are not directly applicable in federated learning, where data is privately stored on multiple clients.
When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes
Thus, it is essential to ensure fairness in machine learning models.
Visual Question Answering based on Formal Logic
To achieve this, we take a symbolic reasoning based approach using the framework of formal logic.
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming
Most importantly, it allows for incorporating expert knowledge into the learning, and hence leading to much faster learning and better generalization compared to the standard deep reinforcement learning.
Inductive Logic Programming via Differentiable Deep Neural Logic Networks
In particular, we show that our proposed method outperforms the state of the art ILP solvers in classification tasks for Mutagenesis, Cora and IMDB datasets.
Learning Algorithms via Neural Logic Networks
In particular, we propose a new framework for learning the inductive logic programming (ILP) problems by exploiting the explicit representational power of NLN.
