MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
PaDGAN generates diverse, high-quality designs with improved performance.
problem Lack of diversity and performance improvement in generated designs.
method Integrates Determinantal Point Processes for diversity and quality, using GAN framework.
result PaDGAN generates higher quality designs with better diversity and without mode collapse.
The paper improves experimental design by weighting diversity metrics with quality, leading to more diverse and effective discoveries.
problem Existing experimental design techniques favor exploitation over exploration, leading to local optima and insufficient diversity.
method The paper extends Vendi scores to account for quality and applies them to various experimental design problems.
result Quality-weighted Vendi scores allow for better balance between quality and diversity, resulting in 70%-170% more effective discoveries.
Pantypes improve prototypical models by capturing diverse input distributions.
problem Prototypical models lack sufficient data representation in low density regions.
method Introducing pantypes, a sparse set of diverse objects to represent the full diversity of input distribution.
result Pantypes empower prototypical models to foster high diversity, interpretability, and fairness.
Developing a range-aware Bayesian optimization framework for discovering diverse designs within target property windows.
problem Discovering multiple, distinct solutions within target property windows.
method Range-aware Bayesian optimization framework.
result Consistently recovers larger and more diverse sets of valid designs.
METASET selects diverse unit cells for efficient data-driven metamaterial design.
problem Imbalanced datasets in unit cells can bias data-driven metamaterial design.
method METASET uses similarity metrics and DPPs to select diverse subsets of unit cells.
result Smaller, diverse subsets improve search process and structural performance.
New method learns diverse protein scaffolds for motif design.
problem Designing long, diverse protein scaffolds for specific motifs.
method E(3)-equivariant graph neural network for diffusion modeling.
result First to guarantee conditional sampling from diffusion models.
New method designs joint initial noises for diffusion models to improve diversity and alignment.
problem Independent initial noises limit diversity in generated images.
method Coupling of initial noises, maintaining Gaussian distribution while allowing dependence.
result Repulsive Gaussian coupling improves diversity without increasing sampling cost.
We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both pred…
New framework shows diverse training data improves subgroup and overall performance.
problem Lack of understanding how diverse training data affects subgroup and overall performance.
method Casts data collection as part of the learning process, analyzes dataset compositions, and guides dataset design.
result Diverse representation in training data improves subgroup and overall performance.
PGEL learns embeddings to diversify protein motifs while maintaining biological function.
problem Generating diverse protein structures while preserving biological function.
method Embedding learning framework that enhances motif diversity in a diffusion model's frozen denoiser.
result PGEL achieves greater structural diversity, better designability, and improved self-consistency compared to partial diffusion.
MO-PaDGAN improves multi-objective optimization by generating diverse and high-performing designs.
problem Challenges in parameterizing engineering designs for multi-objective optimization.
method MO-PaDGAN uses a generative adversarial network with a Determinantal Point Processes loss function to address these challenges.
result MO-PaDGAN generates designs with improved performance and coverage, even surpassing training data.
Automates neural network design for diverse tasks.
problem Designing neural networks for new, under-explored domains.
method Introduces XD-Operations and a weight-sharing scheme to transform standard backbones into search spaces of operations.
result Models using XD-Operations achieve lower error than baseline and expert-designed networks on diverse tasks.
There has been extensive research on developing defense techniques against adversarial attacks; however, they have been mainly designed for specific model families or application domains, therefore, they cannot be easily extended. Based on the design philosophy of ensemble of diverse weak defenses, we propose ATHENA---…
DIVA generates diverse tasks for complex simulators, enabling adaptive agent training.
problem Lack of diverse training data for complex, open-ended simulators.
method Evolutionary approach using domain randomization and procedural generation.
result Successfully trains adaptive agent behavior in complex simulators.
This work improves molecular design by efficiently selecting diverse candidate molecules.
problem Designing molecules that satisfy multiple conflicting objectives.
method A modular 'generate-then-optimize' framework using generative models and a novel acquisition function.
result Significant improvements in sample efficiency across synthetic and application-driven tasks.
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run,…
RNE tackles scalable recommendation for billion-scale scenarios.
problem Designing a scalable recommendation system for diverse and dynamic user interests.
method RNE uses a diversity- and dynamics-aware neighbor sampling method for scalable network embedding.
result RNE achieves high-quality and diverse results on a billion-scale user-item graph.
New algorithm reduces regret with diverse contexts in bandits.
problem Impact of context diversity on stochastic linear contextual bandits.
method Design of LinUCB-d algorithm and analysis of its regret performance.
result Cumulative expected regret is bounded by a constant under diverse context assumption.
New method improves text classification without labeled target data.
problem Improving text classification under domain shift without labeled target data.
method Diversity-based generalization using multi-head attention with diversity constraints.
result Method matches state-of-the-art performance without labeled target data.
Improved UCB algorithm for diversity in bandits with lower bounds.
problem Improving diversity in bandit-based recommendations.
method Designing a UCB algorithm exploiting the specific structure of the problem.
result The UCB algorithm achieves bounded distribution-dependent regret under certain conditions.
einspace expands NAS search space to include diverse neural architectures.
problem NAS results are often limited to existing structures; new designs are rare.
method einspace uses a probabilistic context-free grammar to create a versatile search space.
result einspace discovers novel and improved architectures, including convolutions and attention.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introd…
Improved scaffold generation for protein motifs using SE(3) flow matching.
problem Lack of structural diversity in generated scaffolds for protein motifs.
method Extended FrameFlow for motif-scaffolding with motif amortization and motif guidance.
result 2.5 times more designable and unique motif-scaffolds compared to state-of-the-art.
Automates optimizer design for diverse tasks efficiently.
problem Scalability, generability, and sample efficiency in optimizer search.
method Re-arranged optimizer space into a super-tree, applying tree traversal methods.
result Discover optimizers with only 128 evaluations, surpassing human-designed and prior methods.
DiCE uses diverse agents to explore and learn, avoiding local minima.
problem Local minima in RL due to limited exploration and correlated behavior.
method DiCE employs a group of heterogeneous agents to explore simultaneously and share experiences, with a diversity regularization mechanism.
result DiCE achieves substantial improvement over baselines in MuJoCo locomotion tasks.
This research categorizes AMM designs for secure token exchanges.
problem Designing AMMs for cryptoeconomic systems can lead to financial risks and inefficiencies.
method Developed an AMM taxonomy and proposed three archetypes.
result AMM archetypes meet key requirements for token issuance and exchange.
We focus on the challenge of finding a diverse collection of quality solutions on complex continuous domains. While quality diver-sity (QD) algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are designed to generate a diverse range of solutions, these algorithms require a large number of evalua…
Proposes MOGFNs for generating diverse Pareto optimal solutions in multi-objective optimization.
problem Generating diverse candidates in multi-objective optimization with conflicting objectives.
method Introduces MOGFNs based on GFlowNets, with two variants: MOGFN-PC and MOGFN-AL.
result Improved candidate diversity compared to existing methods.
The paper clusters hypergraphs to find diverse and experienced groups based on past experiences.
problem Finding diverse and experienced groups with respect to past experiences.
method Regularized edge-based hypergraph clustering objective with a 2-approximation algorithm.
result Demonstrates an efficient 2-approximation algorithm for clustering hypergraphs.
This paper shows how diverse tasks can make inefficient exploration in MTRL efficient.
problem The challenge of efficient exploration in Multitask Reinforcement Learning.
method A generic policy-sharing algorithm with myopic exploration design trained on diverse tasks.
result A generic policy-sharing algorithm with myopic exploration design can be sample-efficient in MTRL.
A new LLM-based method enhances diversity in oversampling for imbalanced classification.
problem Limited diversity in synthetic minority samples generated by current LLM-based approaches reduces robustness and generalizability.
method Condition synthetic sample generation on minority labels and features, use permutation strategy for fine-tuning, fine-tune on minority and interpolated samples.
result Significantly outperforms eight SOTA baselines in diverse synthetic sample generation and downstream classification tasks.
Presents STRIPE model for probabilistic forecasting of non-stationary time series.
problem Probabilistic forecasting of non-stationary time series.
method STRIPE model representing structured diversity based on shape and time features, with diversification mechanism using determinantal point processes (DPP).
result STRIPE significantly outperforms baseline methods for representing diversity while maintaining forecasting accuracy.
P3BO optimizes biological sequence design by combining multiple methods.
problem Variability in performance of black-box optimization methods for biological sequence design.
method Population-Based Black-Box Optimization (P3BO) that samples sequences from an ensemble of methods, weighting by past performance.
result P3BO outperforms individual methods, proposing higher quality and more diverse sequences.
RL enhances LLM planning but introduces spurious solutions and diversity collapse.
problem Theoretical understanding of RL's benefits and limitations in LLM planning.
method Graph-based abstraction, policy gradient, Q-learning, supervised fine-tuning.
result RL's exploration is crucial for generalization, but PG suffers from diversity collapse.
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input…
HRF enhances tree diversity in random forests to improve performance.
problem Selection bias and lack of diversity in random forests.
method Introducing heterogeneity during tree construction by assigning lower weights to features used in previous trees.
result HRF outperforms other ensemble methods in accuracy across 52 datasets.
LoRA-MCL improves language models by generating diverse sentence continuations.
problem Language models struggle with generating diverse, plausible sentence continuations.
method Low-Rank Adaptation combined with Multiple Choice Learning (MCL) to handle ambiguity.
result LoRA-MCL generates high-diversity and relevant outputs in various tasks.
Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize sm…
A recommendation framework helps users choose healthcare interventions.
problem Choice overload in online healthcare communities.
method Multi-Armed Bandit (MAB) approach with innovative model components.
result Our recommendation design outperforms state-of-the-art systems.
Agent learns diverse hierarchical structures in unknown environments.
problem Autonomous discovery and learning of diverse structures in unknown changing environments.
method Progressive construction of a Hierarchy of Observation Latent Models for Exploration Stratification (HOLMES).
result Agent can learn and reuse representations to progressively explore and discover diverse structures.
Proposes qPO, a new acquisition strategy for batched Bayesian optimization that maximizes the probability of including the optimum.
problem Efficiently identifying top-performing compounds from a large chemical library.
method qPO (multipoint Probability of Optimality) acquisition strategy that maximizes the probability of including the true optimum.
result Empirical evidence shows that qPO is competitive with and complements other state-of-the-art methods in batched Bayesian optimization.
Enhances molecular design models by fine-tuning uncertainty-guided VAEs.
problem Fine-tuning pre-trained generative models for specific molecular property optimization.
method Uncertainty-guided fine-tuning of variational autoencoders in an active learning setting.
result Uncertainty-guided fine-tuning improves model performance across multiple molecular properties.
A new algorithm optimizes multiple molecular properties efficiently.
problem Designing molecules with conflicting objectives and costly evaluations.
method Multi-objective Bayesian optimization with GFlowNets.
result HN-GFN samples diverse molecules from an approximate Pareto front.
Contrastive learning simplifies statistical inference for complex models.
problem Computational intractability of likelihood functions for certain models.
method Contrastive learning as an alternative for parameter estimation and inference.
result Contrastive learning enables practical methods for diverse statistical problems.
Training task diversity improves ICL with linear attention.
problem Understanding the impact of training task diversity on in-context learning.
method Modeling training task vectors as a mixture of low-rank Gaussians.
result Our model explains why training with task diversity shortens the ICL plateau and achieves out-of-distribution generalization.
This work tackles semi-supervised federated learning by reducing model gradient diversity.
problem Improving test accuracy in semi-supervised federated learning with limited labeled data.
method Investigates and compares various design choices including consistency regularization loss, Batch Normalization, and Group Normalization.
result Grouping-based model averaging combined with Group Normalization and consistency regularization loss improves test accuracy.
Deep neural network (DNN) has demonstrated its success in multiple domains. However, DNN models are inherently vulnerable to adversarial examples, which are generated by adding adversarial perturbations to benign inputs to fool the DNN model to misclassify. In this paper, we present a cross-layer strategic ensemble fra…