A Gap Between Decision Trees and Neural Networks
Fuente:
arXiv
Saved in:
| Main Author: | Kumar, Akash |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis
by: Kumar, Akash, et al.
Published: (2025)
by: Kumar, Akash, et al.
Published: (2025)
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
by: Sponner, Max, et al.
Published: (2024)
by: Sponner, Max, et al.
Published: (2024)
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
by: Huang, Zhenhua, et al.
Published: (2024)
by: Huang, Zhenhua, et al.
Published: (2024)
Implicit Hypergraph Neural Network
by: Choudhuri, Akash, et al.
Published: (2025)
by: Choudhuri, Akash, et al.
Published: (2025)
Dictionary Learning: The Complexity of Learning Sparse Superposed Features with Feedback
by: Kumar, Akash
Published: (2025)
by: Kumar, Akash
Published: (2025)
TREE-G: Decision Trees Contesting Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2022)
by: Bechler-Speicher, Maya, et al.
Published: (2022)
Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks
by: Acharya, Kamal, et al.
Published: (2025)
by: Acharya, Kamal, et al.
Published: (2025)
Decision-focused Graph Neural Networks for Combinatorial Optimization
by: Liu, Yang, et al.
Published: (2024)
by: Liu, Yang, et al.
Published: (2024)
Enhancing LIME using Neural Decision Trees
by: Bouyahia, Mohamed Aymen, et al.
Published: (2026)
by: Bouyahia, Mohamed Aymen, et al.
Published: (2026)
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space
by: Chen, Jingdi, et al.
Published: (2024)
by: Chen, Jingdi, et al.
Published: (2024)
Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
by: Plecko, Drago, et al.
Published: (2024)
by: Plecko, Drago, et al.
Published: (2024)
GLEAMS: Bridging the Gap Between Local and Global Explanations
by: Visani, Giorgio, et al.
Published: (2024)
by: Visani, Giorgio, et al.
Published: (2024)
Deriving Equivalent Symbol-Based Decision Models from Feedforward Neural Networks
by: Seidel, Sebastian, et al.
Published: (2025)
by: Seidel, Sebastian, et al.
Published: (2025)
Mind The Gap: Quantifying Mechanistic Gaps in Algorithmic Reasoning via Neural Compilation
by: Saldyt, Lucas, et al.
Published: (2025)
by: Saldyt, Lucas, et al.
Published: (2025)
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
by: Diallo, Alec F., et al.
Published: (2024)
by: Diallo, Alec F., et al.
Published: (2024)
On the MIA Vulnerability Gap Between Private GANs and Diffusion Models
by: Sebag, Ilana, et al.
Published: (2025)
by: Sebag, Ilana, et al.
Published: (2025)
Switchable Decision: Dynamic Neural Generation Networks
by: Zhang, Shujian, et al.
Published: (2024)
by: Zhang, Shujian, et al.
Published: (2024)
Bridging the Gap Between Bayesian Deep Learning and Ensemble Weather Forecasts
by: Xiong, Xinlei, et al.
Published: (2025)
by: Xiong, Xinlei, et al.
Published: (2025)
How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
by: Ma, Bin, et al.
Published: (2025)
by: Ma, Bin, et al.
Published: (2025)
Learning Smooth Distance Functions via Queries
by: Kumar, Akash, et al.
Published: (2024)
by: Kumar, Akash, et al.
Published: (2024)
A Neural Network Alternative to Tree-based Models
by: Raieli, Salvatore, et al.
Published: (2024)
by: Raieli, Salvatore, et al.
Published: (2024)
Bridging the Gap Between Molecule and Textual Descriptions via Substructure-aware Alignment
by: Park, Hyuntae, et al.
Published: (2025)
by: Park, Hyuntae, et al.
Published: (2025)
Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning
by: Vincent, Théo, et al.
Published: (2025)
by: Vincent, Théo, et al.
Published: (2025)
A Methodology-Oriented Study of Catastrophic Forgetting in Incremental Deep Neural Networks
by: Kumar, Ashutosh, et al.
Published: (2024)
by: Kumar, Ashutosh, et al.
Published: (2024)
A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
by: Banse, Adrien, et al.
Published: (2024)
by: Banse, Adrien, et al.
Published: (2024)
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization
by: Gaur, Mudit, et al.
Published: (2024)
by: Gaur, Mudit, et al.
Published: (2024)
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
by: Chakraborty, Biswadeep, et al.
Published: (2024)
by: Chakraborty, Biswadeep, et al.
Published: (2024)
Optimal Decision Tree Policies for Markov Decision Processes
by: Vos, Daniël, et al.
Published: (2023)
by: Vos, Daniël, et al.
Published: (2023)
A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery
by: Saravanan, Akash, et al.
Published: (2024)
by: Saravanan, Akash, et al.
Published: (2024)
Stock Market Price Prediction using Neural Prophet with Deep Neural Network
by: Chhibber, Navin, et al.
Published: (2026)
by: Chhibber, Navin, et al.
Published: (2026)
Bridging the Gap Between Simulated and Real Network Data Using Transfer Learning
by: Güemes-Palau, Carlos, et al.
Published: (2025)
by: Güemes-Palau, Carlos, et al.
Published: (2025)
Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint
by: Kumar, Harshit, et al.
Published: (2024)
by: Kumar, Harshit, et al.
Published: (2024)
Bridging the Gap Between Preference Alignment and Machine Unlearning
by: Feng, Xiaohua, et al.
Published: (2025)
by: Feng, Xiaohua, et al.
Published: (2025)
A Rectification-Based Approach for Distilling Boosted Trees into Decision Trees
by: Audemard, Gilles, et al.
Published: (2025)
by: Audemard, Gilles, et al.
Published: (2025)
On the Design Space Between Transformers and Recursive Neural Nets
by: Chowdhury, Jishnu Ray, et al.
Published: (2024)
by: Chowdhury, Jishnu Ray, et al.
Published: (2024)
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
by: Sponner, Max, et al.
Published: (2024)
by: Sponner, Max, et al.
Published: (2024)
Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge
by: Kumari, Neha, et al.
Published: (2024)
by: Kumari, Neha, et al.
Published: (2024)
Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders
by: Rozonoyer, Benjamin, et al.
Published: (2026)
by: Rozonoyer, Benjamin, et al.
Published: (2026)
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
by: Kofinas, Miltiadis, et al.
Published: (2024)
by: Kofinas, Miltiadis, et al.
Published: (2024)
AI Generalisation Gap In Comorbid Sleep Disorder Staging
by: Bose, Saswata, et al.
Published: (2026)
by: Bose, Saswata, et al.
Published: (2026)
Similar Items
-
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis
by: Kumar, Akash, et al.
Published: (2025) -
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
by: Sponner, Max, et al.
Published: (2024) -
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
by: Huang, Zhenhua, et al.
Published: (2024) -
Implicit Hypergraph Neural Network
by: Choudhuri, Akash, et al.
Published: (2025) -
Dictionary Learning: The Complexity of Learning Sparse Superposed Features with Feedback
by: Kumar, Akash
Published: (2025)