Image de couverture de livre

Benchmarking Optimizers for MLPs in Tabular Deep Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Gorishniy, Yury, Rubachev, Ivan, Feoktistov, Dmitrii, Babenko, Artem
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
par: Rubachev, Ivan, et autres
Publié: (2024)

TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
par: Gorishniy, Yury, et autres
Publié: (2024)

Unveiling the Role of Data Uncertainty in Tabular Deep Learning
par: Kartashev, Nikolay, et autres
Publié: (2025)

On Finetuning Tabular Foundation Models
par: Rubachev, Ivan, et autres
Publié: (2025)

TabDDPM: Modelling Tabular Data with Diffusion Models
par: Kotelnikov, Akim, et autres
Publié: (2022)

Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data
par: Yakushev, George, et autres
Publié: (2025)

Aligning Distributionally Robust Optimization with Practical Deep Learning Needs
par: Feoktistov, Dmitrii, et autres
Publié: (2025)

Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
par: Kovalev, Dmitry, et autres
Publié: (2024)

Turning Tabular Foundation Models into Graph Foundation Models
par: Eremeev, Dmitry, et autres
Publié: (2025)

Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling
par: Feoktistov, Dmitrii, et autres
Publié: (2026)

Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data
par: Holzmüller, David, et autres
Publié: (2024)

Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs
par: Yan, Jiahuan, et autres
Publié: (2024)

TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
par: Simonetto, Thibault, et autres
Publié: (2024)

GraphPFN: A Prior-Data Fitted Graph Foundation Model
par: Eremeev, Dmitry, et autres
Publié: (2025)

A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
par: Platonov, Oleg, et autres
Publié: (2023)

Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking
par: Rabbani, Shourav B., et autres
Publié: (2024)

Improving Deep Tabular Learning
par: Sarafian, Sivan, et autres
Publié: (2025)

Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond
par: Sawaya, Kazuma
Publié: (2025)

Bayesian Inference with Shaped Deep Non-linear MLPs
par: Hanin, Boris, et autres
Publié: (2026)

Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
par: Rabbani, Shourav B., et autres
Publié: (2023)

STRABLE: Benchmarking Tabular Machine Learning with Strings
par: Blayer, Gioia, et autres
Publié: (2026)

A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets
par: Shmuel, Assaf, et autres
Publié: (2024)

Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
par: Platonov, Oleg, et autres
Publié: (2022)

MLPMoE: Zero-Shot Architectural Metamorphosis of Dense LLM MLPs into Static Mixture-of-Experts
par: Novikov, Ivan
Publié: (2025)

Interpolated-MLPs: Controllable Inductive Bias
par: Wu, Sean, et autres
Publié: (2024)

Converting MLPs into Polynomials in Closed Form
par: Belrose, Nora, et autres
Publié: (2025)

MLPs Learn In-Context on Regression and Classification Tasks
par: Tong, William L., et autres
Publié: (2024)

MLPs at the EOC: Spectrum of the NTK
par: Terjék, Dávid, et autres
Publié: (2025)

MLPs at the EOC: Concentration of the NTK
par: Terjék, Dávid, et autres
Publié: (2025)

On the Limits of Tabular Hardness Metrics for Deep RL: A Study with the Pharos Benchmark
par: Conserva, Michelangelo, et autres
Publié: (2025)

A Survey on Deep Tabular Learning
par: Somvanshi, Shriyank, et autres
Publié: (2024)

Tabular and Deep Learning for the Whittle Index
par: Relaño, Francisco Robledo, et autres
Publié: (2024)

Tabular and Deep Reinforcement Learning for Gittins Index
par: Dhankhar, Harshit, et autres
Publié: (2024)

Mambular: A Sequential Model for Tabular Deep Learning
par: Thielmann, Anton Frederik, et autres
Publié: (2024)

Iterative Feature Exclusion Ranking for Deep Tabular Learning
par: Shaninah, Fathi Said Emhemed, et autres
Publié: (2024)

On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning
par: Thielmann, Anton Frederik, et autres
Publié: (2024)

How Data Mixing Shapes In-Context Learning: Asymptotic Equivalence for Transformers with MLPs
par: Demir, Samet, et autres
Publié: (2025)

Extreme Compression of Large Language Models via Additive Quantization
par: Egiazarian, Vage, et autres
Publié: (2024)

Bilinear MLPs enable weight-based mechanistic interpretability
par: Pearce, Michael T., et autres
Publié: (2024)

Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
par: Madhusudhanan, Kiran, et autres
Publié: (2024)