Imagen de Portada

Strong convexity-guided hyper-parameter optimization for flatter losses

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yedida, Rahul, Saha, Snehanshu
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications
por: Mehendale, Shlok, et al.
Publicado: (2025)

Is Hyper-Parameter Optimization Different for Software Analytics?
por: Yedida, Rahul, et al.
Publicado: (2024)

QuantProb: Generalizing Probabilities along with Predictions for a Pre-trained Classifier
por: Challa, Aditya, et al.
Publicado: (2023)

Quantile Activation: Correcting a Failure Mode of ML Models
por: Challa, Aditya, et al.
Publicado: (2024)

AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
por: Mohapatra, Rohan, et al.
Publicado: (2020)

DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries
por: Mehra, Ashman, et al.
Publicado: (2023)

A Granger-Causal Perspective on Gradient Descent with Application to Pruning
por: Shah, Aditya, et al.
Publicado: (2024)

Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data
por: Saha, Snehanshu, et al.
Publicado: (2023)

Matching High-Dimensional Geometric Quantiles for Test-Time Adaptation of Transformers and Convolutional Networks Alike
por: Danda, Sravan, et al.
Publicado: (2026)

Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond
por: Mehta, Shanay, et al.
Publicado: (2024)

Multi-Agent Training-free Urban Food Delivery System using Resilient UMST Network
por: Hasan, Md Nahid, et al.
Publicado: (2026)

The Nyström method for convex loss functions
por: Della Vecchia, Andrea, et al.
Publicado: (2020)

Altruistic Ride Sharing: A Framework for Fair and Sustainable Urban Mobility via Peer-to-Peer Incentives
por: Singh, Divyanshu, et al.
Publicado: (2025)

What augmentations are sensitive to hyper-parameters and why?
por: Awais, Ch Muhammad, et al.
Publicado: (2021)

On the consistency of hyper-parameter selection in value-based deep reinforcement learning
por: Obando-Ceron, Johan, et al.
Publicado: (2024)

Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity
por: Yang, Yan, et al.
Publicado: (2024)

Exploring the loss landscape of regularized neural networks via convex duality
por: Kim, Sungyoon, et al.
Publicado: (2024)

The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
por: Chizat, Lénaïc, et al.
Publicado: (2023)

Near-optimal delta-convex estimation of Lipschitz functions
por: Balázs, Gábor
Publicado: (2025)

On amortizing convex conjugates for optimal transport
por: Amos, Brandon
Publicado: (2022)

Non-geodesically-convex optimization in the Wasserstein space
por: Luu, Hoang Phuc Hau, et al.
Publicado: (2024)

BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification
por: Wang, Jun, et al.
Publicado: (2024)

A simple uniformly optimal method without line search for convex optimization
por: Li, Tianjiao, et al.
Publicado: (2023)

Learning based convex approximation for constrained parametric optimization
por: Liu, Kang, et al.
Publicado: (2025)

Online combinatorial optimization with stochastic decision sets and adversarial losses
por: Neu, Gergely, et al.
Publicado: (2026)

Regularized GLISp for sensor-guided human-in-the-loop optimization
por: Cercola, Matteo, et al.
Publicado: (2025)

Spectral entropy prior-guided deep feature fusion architecture for magnetic core loss
por: Yao, Cong, et al.
Publicado: (2025)

Surrogate-guided optimization in quantum networks
por: Prielinger, Luise, et al.
Publicado: (2024)

Cubic regularized subspace Newton for non-convex optimization
por: Zhao, Jim, et al.
Publicado: (2024)

Extremal graphical modeling with latent variables via convex optimization
por: Engelke, Sebastian, et al.
Publicado: (2024)

Strong identifiability and parameter learning in regression with heterogeneous response
por: Do, Dat, et al.
Publicado: (2022)

Instance-optimal stochastic convex optimization: Can we improve upon sample-average and robust stochastic approximation?
por: Jiang, Liwei, et al.
Publicado: (2026)

MADA: Meta-Adaptive Optimizers through hyper-gradient Descent
por: Ozkara, Kaan, et al.
Publicado: (2024)

Deep learning-guided evolutionary optimization for protein design
por: Hartman, Erik, et al.
Publicado: (2026)

Tractable hierarchies of convex relaxations for polynomial optimization on the nonnegative orthant
por: Mai, Ngoc Hoang Anh, et al.
Publicado: (2022)

DPG loss functions for learning parameter-to-solution maps by neural networks
por: Castillo, Pablo Cortés, et al.
Publicado: (2025)

Fractional Naive Bayes (FNB): non-convex optimization for a parsimonious weighted selective naive Bayes classifier
por: Hue, Carine, et al.
Publicado: (2024)

A novel gradient-based method for decision trees optimizing arbitrary differential loss functions
por: Konstantinov, Andrei V., et al.
Publicado: (2025)

How does the optimizer implicitly bias the model merging loss landscape?
por: Zhang, Chenxiang, et al.
Publicado: (2025)

Non-convex entropic mean-field optimization via Best Response flow
por: Lascu, Razvan-Andrei, et al.
Publicado: (2025)