Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models
Large Language Models (LLMs) based on Mixture-of-Experts (MoE) architecture are showing promising performance on various tasks. However, running them on resource-constrained settings, where GPU memory...
View ArticleInformativeness of Reward Functions in Reinforcement Learning
Reward functions are central in specifying the task we want a reinforcement learning agent to perform. Given a task and desired optimal behavior, we study the problem of designing informative reward...
View ArticleFedImpro: Measuring and Improving Client Update in Federated Learning
Federated Learning (FL) models often experience client drift caused by heterogeneous data, where the distribution of data differs across clients. To address this issue, advanced research primarily...
View ArticleClients Collaborate: Flexible Differentially Private Federated Learning with...
To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic integrity of the...
View ArticleGuided Sketch-Based Program Induction by Search Gradients
Many tasks can be easily solved using machine learning techniques. However, some tasks cannot readily be solved using statistical models, requiring a symbolic approach instead. Program induction is one...
View ArticleNon-linear Fusion in Federated Learning: A Hypernetwork Approach to Federated...
Federated Learning (FL) has emerged as a promising paradigm in which multiple clients collaboratively train a shared global model while preserving data privacy. To create a robust and practicable FL...
View ArticleIn-Context Data Distillation with TabPFN
Foundation models have revolutionized tasks in computer vision and natural language processing. However, in the realm of tabular data, tree-based models like XGBoost continue to dominate. TabPFN, a...
View ArticleContextual Stochastic Vehicle Routing with Time Windows
We study the vehicle routing problem with time windows (VRPTW) and stochastic travel times, in which the decision-maker observes related contextual information, represented as feature variables, before...
View ArticleDeepCover: Advancing RNN Test Coverage and Online Error Prediction using...
Recurrent neural networks (RNNs) have emerged as powerful tools for processing sequential data in various fields, including natural language processing and speech recognition. However, the lack of...
View ArticleTree Ensembles for Contextual Bandits
We propose a novel framework for contextual multi-armed bandits based on tree ensembles. Our framework integrates two widely used bandit methods, Upper Confidence Bound and Thompson Sampling, for both...
View ArticleTraining dynamics in Physics-Informed Neural Networks with feature mapping
Physics-Informed Neural Networks (PINNs) have emerged as an iconic machine learning approach for solving Partial Differential Equations (PDEs). Although its variants have achieved significant progress,...
View ArticleOpenFedLLM: Training Large Language Models on Decentralized Private Data via...
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting...
View ArticleAssessing Uncertainty Estimation Methods for 3D Image Segmentation under...
In recent years, machine learning has witnessed extensive adoption across various sectors, yet its application in medical image-based disease detection and diagnosis remains challenging due to...
View ArticleLearning Attributed Graphlets: Predictive Graph Mining by Graphlets with...
The graph classification problem has been widely studied; however, achieving an interpretable model with high predictive performance remains a challenging issue. This paper proposes an interpretable...
View ArticleClustering Techniques Selection for a Hybrid Regression Model: A Case Study...
This work addresses the performance comparison between four clustering techniques with the objective of achieving strong hybrid models in supervised learning tasks. A real dataset from a bio-climatic...
View ArticleGenerating Chain-of-Thoughts with a Direct Pairwise-Comparison Approach to...
To improve the ability of the large language model (LLMs) to handle complex reasoning problems, chain-of-thoughts (CoT) methods were proposed to guide LLMs to reason step-by-step, facilitating problem...
View ArticleSolving Deep Reinforcement Learning Benchmarks with Linear Policy Networks
Although Deep Reinforcement Learning (DRL) methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex and training times are often...
View ArticleTopological Neural Networks: Mitigating the Bottlenecks of Graph Neural...
The irreducible complexity of natural phenomena has led Graph Neural Networks to be employed as a standard model to perform representation learning tasks on graph-structured data. While their capacity...
View ArticleUnderstanding Test-Time Augmentation
Test-Time Augmentation (TTA) is a very powerful heuristic that takes advantage of data augmentation during testing to produce averaged output. Despite the experimental effectiveness of TTA, there is...
View ArticlePrincipled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised learning setting, where static objective functions with...
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