Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning. In this paper, we verify that au…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,878 papers · 148 categories
Trend · papers per month
48 results for “gradient-based search”
Gradient-based feature selection for large datasets.
problem Feature selection for large datasets with high-order correlations.
method Iterative mini-batch calculation, discrete-to-continuous relaxation.
result Efficiently finds higher-order feature correlations in both N > D and N < D regimes.
Novel BSG method for efficient stochastic optimization.
problem Efficient optimization of non-convex surfaces in stochastic settings.
method Binary search combined with first order gradient optimization.
result BSG produces more promising results and better generalization than other methods.
DrNAS improves neural architecture search with Dirichlet distribution and progressive learning.
problem Efficiently search for neural architectures with improved generalization and exploration.
method Formulates architecture search as a distribution learning problem using Dirichlet distribution and gradient-based optimization. Introduces a progressive learning scheme to handle large-scale tasks.
result Achieves state-of-the-art results on CIFAR-10 and ImageNet, demonstrating improved generalization and exploration.
Paper proposes MCTSPO for better reinforcement learning policy optimization.
problem Local optima and saddle points in gradient-based methods and poor initialization in gradient-free methods.
method Monte-Carlo tree search combined with gradient-free optimization.
result Improved performance on reinforcement learning tasks with deceptive or sparse reward functions.
A Bayesian approach to neural architecture search improves efficiency and accuracy.
problem Improving the efficiency and accuracy of neural architecture search.
method Formulating NAS from a Bayesian perspective, explicitly estimating the joint posterior distribution over architectures and weights, using Variational Dropout, and posterior-guided sampling.
result Posterior-guided NAS (PGNAS) achieves a good trade-off between precision and speed of search.
VINNAS uses variational inference to avoid mode collapse in neural architecture search.
problem Mode collapse in gradient-based NAS methods, leading to suboptimal architectures.
method Differentiable variational inference with variational dropout and automatic relevance determination.
result State-of-the-art accuracy with up to twice fewer non-zero parameters.
MTL-NAS combines NAS with GP-MTL for task-agnostic multi-task learning.
problem Designing architectures for diverse tasks with varying priors.
method Disentangled GP-MTL networks, hierarchical feature sharing, and gradient-based search.
result General-purpose model trained once can adapt to multiple tasks.
PDNAS optimizes GNN architectures for diverse datasets.
problem Inadequate adaptability and combinatorial search space in GNNs.
method Dual architecture search (micro- and macro-architectures) with gradient-based optimization.
result PDNAS finds deeper GNNs with better performance on diverse datasets.
Gradient-based method prunes large models to create transferable architectures.
problem Creating transferable architectures from large models with limited fine-tuning data.
method Gradient-based algorithm for architecture pruning and subset selection.
result Successfully retrain architectures on new tasks with few fine-tuning data.
A new framework improves graph construction for semi-supervised learning.
problem Improving graph construction for better semi-supervised classification accuracy.
method Parallel hyperparameter search with adaptive resource allocation for gradient-based optimization of edge weights.
result Significantly outperforms existing graph construction schemes in accuracy and scalability.
Paper introduces a privacy-preserving line search method for optimization.
problem Optimization performance depends on step size tuning, which is difficult and privacy-sensitive.
method Introduces a stochastic adaptive line search algorithm that satisfies differential privacy.
result The algorithm efficiently uses privacy budget and outperforms existing private optimizers.
Automatic methods for Neural Architecture Search (NAS) have been shown to produce state-of-the-art network models. Yet, their main drawback is the computational complexity of the search process. As some primal methods optimized over a discrete search space, thousands of days of GPU were required for convergence. A rece…
Proposes a gradient-based bilevel optimization method for efficient hyperparameter tuning.
problem Efficiently tuning hyperparameters in machine learning models.
method Gradient-based bilevel optimization approach.
result The proposed method is multiple times faster than existing techniques.
Planning problems are among the most important and well-studied problems in artificial intelligence. They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree. Among these algorithms, Monte-Carlo tree …
Neural architecture search has been shown to hold great promise towards the automation of deep learning. However in spite of its potential, neural architecture search remains quite costly. To this point, we propose a novel gradient-based framework for efficient architecture search by sharing information across several …
In structured output prediction tasks, labeling ground-truth training output is often expensive. However, for many tasks, even when the true output is unknown, we can evaluate predictions using a scalar reward function, which may be easily assembled from human knowledge or non-differentiable pipelines. But searching th…
Adaptor 'E' extends gradient-based optimizers to explore loss landscapes, improving generalization.
problem Finding lower and better-generalizing minima in deep learning.
method Proposes an adaptor 'E' to extend gradient-based optimizers, encouraging exploration along landscape valleys.
result Adapted optimizers increase test accuracy by an average of 2.5% in large-batch training tasks.
DSA efficiently allocates sparsity across layers for budgeted pruning.
problem Efficiently distributing resources (sparsity) across layers in pruning under resource constraints.
method DSA uses differentiable pruning to find continuous layer-wise pruning ratios via gradient-based optimization.
result DSA achieves superior performance and significantly reduces the time cost of pruning.
MetaNAS improves few-shot learning by optimizing neural architectures with meta-learning.
problem Few-shot learning challenges due to limited data and compute time.
method MetaNAS integrates NAS with gradient-based meta-learning to adapt neural architectures to new tasks efficiently.
result MetaNAS achieves state-of-the-art results on few-shot classification benchmarks.
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while…
Enhances neural architecture search efficiency and prevents performance collapse.
problem Improving memory efficiency and preventing performance collapse in neural architecture search.
method Employing continuous relaxation strategy and gradient-based optimization for over-parameterized BCNN construction, introducing Confident Learning Rate and partial channel connections.
result NAS-v2 delivers state-of-the-art search efficiency on CIFAR-10 and ImageNet.
A new NAS framework optimizes 3D medical image segmentation architectures.
problem Optimizing neural architectures for high-resolution 3D medical images.
method Stochastic sampling algorithm for scalable gradient-based optimization of neural connectivities and operation types in both encoder and decoder.
result Automatically designed architecture outperforms human-designed U-Net.
New hierarchical search algorithm improves neural architecture design across different operator sets.
problem DARTS's performance drops when search space changes due to operator correlation and optimization complexity.
method Operator clustering and optimization complexity matching in a hierarchical search algorithm.
result The algorithm consistently finds high-performance architectures across various search spaces, outperforming other methods.
Survey of techniques for automating hyperparameter optimization in machine learning.
problem Finding optimal hyperparameters for machine learning algorithms.
method Review of various hyperparameter optimization techniques.
result Unified treatment of hyperparameter optimization techniques.
High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them out-of-the-box to their own problems, albeit its purpose is to automate a part of tuning process. Aiming at a fast, robust, and widely-appli…
Paper stabilizes DARTS algorithm for better neural architecture search.
problem Weak stability of DARTS algorithm leading to unreliable results.
method Amended gradient estimation method to bridge optimization gap.
result Significant improvement in search stability and larger search spaces explored.
New distance metric for neural architecture search reduces search space complexity.
problem Reducing the complexity of neural architecture search.
method Fisher task distance for measuring task similarity and online neural architecture search.
result Reduced search space complexity for task-specific architectures.
New algorithms improve neural architecture search with faster convergence.
problem Improving efficiency and accuracy of neural architecture search.
method Geometry-aware gradient algorithms to optimize continuous relaxation of discrete search spaces.
result Exceeds state-of-the-art results on CIFAR and ImageNet benchmarks.
FDS tackles long horizon hyperparameter optimization issues.
problem Memory scaling and gradient degradation in long horizon tasks.
method Forward-mode differentiation with sharing (FDS).
result Significantly outperforms greedy gradient-based alternatives.
New robustness attacks improve evaluation of neural networks.
problem Difficulty in evaluating robustness of neural networks.
method Gradient-based adversarial attacks that are more reliable and efficient.
result Developed attacks are more reliable and efficient than existing methods.
Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics. However, despite the extensive research, NN-based controllers have not been adopted by the industry for safety critical systems. The primary reason is that systems with learning…
VaSST uses soft symbolic trees for probabilistic symbolic regression.
problem Efficiently recover symbolic expressions from noisy data.
method Variational inference with soft symbolic trees.
result Superior performance in structural recovery and predictive accuracy.
The paper proposes a gradient-based method for multi-penalty Ridge regression.
problem Optimizing multiple regularization hyperparameters for linear regression.
method Gradient-based optimization through matrix differential calculus.
result The method outperforms traditional regularization techniques like LASSO and Ridge.
Bayesian optimization improves policy search in reinforcement learning.
problem Finding optimal policies with high variance estimates from random samples.
method Develops an algorithm combining Bayesian optimization and policy gradients.
result Improves sample complexity and reduces variance in empirical evaluations.
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies. This paper provide…
Introduces GAMPS for better model-based policy learning.
problem Misspecified model classes lead to poor policy estimates.
method Exploits current policy to learn approximate transition model, focusing on relevant parts of the environment.
result Empirically validated GAMPS on benchmark domains, demonstrating improved properties.
In this paper we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whose behavior depends on an exogenous state variable which affects the shape of the objective function. Currently, there is no general purpose …
We propose a simple change to existing neural network structures for better defending against gradient-based adversarial attacks. Instead of using popular activation functions (such as ReLU), we advocate the use of k-Winners-Take-All (k-WTA) activation, a C0 discontinuous function that purposely invalidates the neural …
Recent breakthroughs in defenses against adversarial examples, like adversarial training, make the neural networks robust against various classes of attackers (e.g., first-order gradient-based attacks). However, it is an open question whether the adversarially trained networks are truly robust under unknown attacks. In…
In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accurac…
During recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improve the convergence by incorporating second order information. In this work we propose a direct least-squares approach conceptually similar to…
A new hyperparameter optimization method reduces overfitting.
problem Overfitting in hyperparameter optimization.
method PAC-Bayes bound minimization using gradient-based algorithm.
result Significant reduction in out-of-sample error.
FrostNet improves INT8 quantization efficiency in mobile networks.
problem The importance of network architecture for optimal INT8 quantization.
method Quantization-aware training (QAT) with StatAssist and GradBoost, hardware-aware NAS.
result FrostNets achieve higher recognition accuracy with comparable latency when quantized.
Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within mu…
The paper proposes a method to learn hyperparameters without validation sets, improving efficiency and accuracy.
problem Training large models on limited data to avoid overfitting and reduce validation set usage.
method Gradient-based learning of hyperparameters via a data-emphasized evidence lower bound (ELBO) objective.
result The data-emphasized ELBO reduces hyperparameter search time from 88+ hours to under 3 hours while maintaining comparable accuracy.
Square Attack efficiently attacks deep models with random updates.
problem Efficiently attacking deep learning models without gradient information.
method Randomized search of localized square updates.
result Significantly more query-efficient and higher success rate compared to state-of-the-art methods.
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions …