Cover Image

Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Loukas, Andreas, Martinkus, Karolis, Wagstaff, Ed, Cho, Kyunghyun
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient and Scalable Graph Generation through Iterative Local Expansion
by: Bergmeister, Andreas, et al.
Published: (2023)

AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies
by: Martinkus, Karolis, et al.
Published: (2023)

Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient
by: Tagasovska, Nataša, et al.
Published: (2024)

A Brief Introduction to Causal Inference in Machine Learning
by: Cho, Kyunghyun
Published: (2024)

Machine Learning: a Lecture Note
by: Cho, Kyunghyun
Published: (2025)

Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation
by: Cha, Sungmin, et al.
Published: (2025)

Efficient semantic uncertainty quantification in language models via diversity-steered sampling
by: Park, Ji Won, et al.
Published: (2025)

Why Alignment Must Precede Distillation: A Minimal Working Explanation
by: Cha, Sungmin, et al.
Published: (2025)

Non-convolutional Graph Neural Networks
by: Wang, Yuanqing, et al.
Published: (2024)

Active and Passive Causal Inference Learning
by: Im, Daniel Jiwoong, et al.
Published: (2023)

Meta-Statistical Learning: Supervised Learning of Statistical Estimators
by: Peyrard, Maxime, et al.
Published: (2025)

Generalization within in silico screening
by: Loukas, Andreas, et al.
Published: (2023)

Hyperparameters in Continual Learning: A Reality Check
by: Cha, Sungmin, et al.
Published: (2024)

None To Optima in Few Shots: Bayesian Optimization with MDP Priors
by: Li, Diantong, et al.
Published: (2025)

Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
by: Cha, Sungmin, et al.
Published: (2023)

Language Models as Causal Effect Generators
by: Bynum, Lucius E. J., et al.
Published: (2024)

Temporal Generalization: A Reality Check
by: Madaan, Divyam, et al.
Published: (2025)

Unified all-atom molecule generation with neural fields
by: Kirchmeyer, Matthieu, et al.
Published: (2025)

Semiparametric conformal prediction
by: Park, Ji Won, et al.
Published: (2024)

Show Your Work with Confidence: Confidence Bands for Tuning Curves
by: Lourie, Nicholas, et al.
Published: (2023)

Hyperparameter Loss Surfaces Are Simple Near their Optima
by: Lourie, Nicholas, et al.
Published: (2025)

Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse
by: Lippl, Samuel, et al.
Published: (2023)

RL-finetuning LLMs from on- and off-policy data with a single algorithm
by: Tang, Yunhao, et al.
Published: (2025)

Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
by: Sakho, Abdoulaye, et al.
Published: (2024)

Low-rank finetuning for LLMs: A fairness perspective
by: Das, Saswat, et al.
Published: (2024)

ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuning
by: Qiu, Ruizhong, et al.
Published: (2026)

Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization
by: Lee, Deokjae, et al.
Published: (2024)

Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check
by: Lourie, Nicholas, et al.
Published: (2025)

On the Relationship Between the Choice of Representation and In-Context Learning
by: Marinescu, Ioana, et al.
Published: (2025)

Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
by: Dreyer, Frédéric A., et al.
Published: (2025)

Deep Autoregressive Models as Causal Inference Engines
by: Im, Daniel Jiwoong, et al.
Published: (2024)

Latent State Models of Training Dynamics
by: Hu, Michael Y., et al.
Published: (2023)

Reference-Specific Unlearning Metrics Can Hide the Truth: A Reality Check
by: Cho, Sungjun, et al.
Published: (2025)

Forget Forgetting: Continual Learning in a World of Abundant Memory
by: Cho, Dongkyu, et al.
Published: (2025)

BadGPT-4o: stripping safety finetuning from GPT models
by: Krupkina, Ekaterina, et al.
Published: (2024)

PreFT: Prefill-only finetuning for efficient inference
by: Lanpouthakoun, Andrew, et al.
Published: (2026)

Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling
by: Madaan, Divyam, et al.
Published: (2026)

Training Dynamics of Learning 3D-Rotational Equivariance
by: Shen, Max W., et al.
Published: (2025)

Multitask finetuning and acceleration of chemical pretrained models for small molecule drug property prediction
by: Adrian, Matthew, et al.
Published: (2025)

Protein Discovery with Discrete Walk-Jump Sampling
by: Frey, Nathan C., et al.
Published: (2023)