:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Thudi, Anvith, Maddison, Chris J.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2406.01477
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MixMin: Finding Data Mixtures via Convex Minimization
by: Thudi, Anvith, et al.
Published: (2025)

Fast Exact Unlearning for In-Context Learning Data for LLMs
by: Muresanu, Andrei I., et al.
Published: (2024)

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
by: Thudi, Anvith, et al.
Published: (2023)

Efficient Public Verification of Private ML via Regularization
by: Bell, Zoë Ruha, et al.
Published: (2025)

Selective Prediction via Training Dynamics
by: Rabanser, Stephan, et al.
Published: (2022)

Gauss-Newton Unlearning for the LLM Era
by: McKinney, Lev, et al.
Published: (2026)

Leveraging Per-Instance Privacy for Machine Unlearning
by: Sepahvand, Nazanin Mohammadi, et al.
Published: (2025)

Predicting Large Model Test Losses with a Noisy Quadratic System
by: Li, Chuning, et al.
Published: (2026)

Observational Scaling Laws and the Predictability of Language Model Performance
by: Ruan, Yangjun, et al.
Published: (2024)

A Geometric Analysis of PCA
by: Hanchi, Ayoub El, et al.
Published: (2025)

Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
by: Dhawan, Nikita, et al.
Published: (2026)

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
by: Johnson, Daniel D., et al.
Published: (2024)

MaD-Mix: Multi-Modal Data Mixtures via Latent Space Coupling for Vision-Language Model Training
by: Xie, Wanyun, et al.
Published: (2026)

End-To-End Causal Effect Estimation from Unstructured Natural Language Data
by: Dhawan, Nikita, et al.
Published: (2024)

Minimax Linear Regression under the Quantile Risk
by: Hanchi, Ayoub El, et al.
Published: (2024)

On the Efficiency of ERM in Feature Learning
by: Hanchi, Ayoub El, et al.
Published: (2024)

Sequential Function-Space Variational Inference via Gaussian Mixture Approximation
by: Zhu, Menghao Waiyan William, et al.
Published: (2025)

Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces
by: Wang, Shengbo, et al.
Published: (2024)

Distribution-Free Robust Predict-Then-Optimize in Function Spaces
by: Patel, Yash, et al.
Published: (2026)

Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions
by: Epasto, Alessandro, et al.
Published: (2020)

Scaling Laws for Optimal Data Mixtures
by: Shukor, Mustafa, et al.
Published: (2025)

Reasoning to Learn from Latent Thoughts
by: Ruan, Yangjun, et al.
Published: (2025)

Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions
by: Kanj, Haitham, et al.
Published: (2026)

Causal Risk Minimization for High-Dimensional Treatments
by: Dhawan, Nikita, et al.
Published: (2026)

MaxSketch: Robust Distinct Counting in Streams via Random Projections
by: Tsikouras, Nikos, et al.
Published: (2026)

MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging
by: Wang, Jiapeng, et al.
Published: (2026)

Unifying Distributionally Robust Optimization via Optimal Transport Theory
by: Blanchet, Jose, et al.
Published: (2023)

Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
by: Shi, Qiankun, et al.
Published: (2025)

Optimal Transport Aggregation for Distributed Mixture-of-Experts
by: Chamroukhi, Faïcel, et al.
Published: (2023)

Learning Optimal Distributionally Robust Individualized Treatment Rules Integrating Multi-Source Data
by: Cui, Wenhai, et al.
Published: (2026)

Re-Mix: Optimizing Data Mixtures for Large Scale Imitation Learning
by: Hejna, Joey, et al.
Published: (2024)

Linear Mixture Distributionally Robust Markov Decision Processes
by: Liu, Zhishuai, et al.
Published: (2025)

Mixture-of-Experts for Distributed Edge Computing with Channel-Aware Gating Function
by: Song, Qiuchen, et al.
Published: (2025)

Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces
by: Schröder, Tobias, et al.
Published: (2024)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
by: Lu, Miao, et al.
Published: (2024)

Stochastic Optimal Control for Diffusion Bridges in Function Spaces
by: Park, Byoungwoo, et al.
Published: (2024)

Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities
by: Parhi, Rahul, et al.
Published: (2023)

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
by: Zhu, Linglingzhi, et al.
Published: (2024)

Distributed Differentially Private Data Analytics via Secure Sketching
by: Burkhardt, Jakob, et al.
Published: (2024)

Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms
by: Rezaei, Parham, et al.
Published: (2024)