Diverse sampling improves kernel methods' performance in sparse regions.
problem Improving kernel methods' performance in sparse regions of datasets.
method Using Determinantal Point Processes (DPP) for sampling diverse landmarks in Nyström approximation.
result Nyström kernel regression with diverse landmarks increases accuracy in sparse regions of the dataset.
BADGE samples diverse and uncertain points for deep neural nets.
problem Efficiently selecting batches for active learning with deep models.
method Samples groups of points that are disparate and high-magnitude in a hallucinated gradient space.
result BADGE consistently performs as well or better than other methods for active learning.
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.
We introduce a new sampling method for large language models that balances diversity and parallelism.
problem Balancing diversity and parallelism in decoding for large language models.
method Arithmetic sampling framework compatible with various sampling variations.
result Improves estimation of expected BLEU score reward and reduces the gap with beam search.
Enhances sample diversity in SGMCMC for better uncertainty estimation in BNNs.
problem Limited sample diversity in SGMCMC affects uncertainty estimation and model performance.
method Reparameterizes neural network weights to produce a more diverse set of samples.
result The proposed approach achieves superior performance in image classification tasks, including OOD robustness.
Introduce a variance-weighted batch distribution for diverse sampling in diffusion models.
problem Independent sampling in diffusion models.
method Introduce a variance-weighted batch distribution.
result Sampler with a transparent probabilistic target.
The paper tackles fair and diverse data summarization using DPPs.
problem Tackles bias in data summarization methods.
method Uses determinantal measures of diversity and corresponding distributions (DPPs) to incorporate fairness constraints.
result Developed a fast sampler for constrained determinantal distributions that is provably good under certain conditions.
SteinGen generates diverse graph samples from a single example.
problem Generating graphs with characteristic structures and diversity from a single example.
method Combines Stein's method and MCMC with Glauber dynamics and re-estimation of the Stein operator.
result High distributional similarity to the original data, combined with high sample diversity.
iRDM selects unlabeled samples for regression without labels, improving model accuracy.
problem Selecting unlabeled samples for regression without label information.
method Iterative representativeness-diversity maximization (iRDM).
result iRDM significantly outperforms supervised ALR, especially with limited labeled samples.
New SLC distributions enable easier control over diversity.
problem Lack of easy control over diversity in existing models.
method Developed strongly log-concave distributions and two tools for sampling and mode finding.
result Established weak log-submodularity for SLC functions and optimization guarantees for mode finding.
Proposes GM Score to evaluate GANs considering diversity, disentanglement, and discriminability.
problem Evaluation of GANs for sample quality and diversity.
method Integrates various factors including intra-class and inter-class diversity, disentanglement, and discriminability metrics.
result Demonstrates improved evaluation of GANs on MNIST dataset.
This work studies the impact of intra-/inter-class diversity on pre-training datasets and finds a balance for optimal performance.
problem The impact of intra-/inter-class diversity on supervised pre-training datasets and their effect on downstream tasks.
method Empirical study and theoretical analysis of the relationship between diversity types and downstream performance.
result The optimal class-to-sample ratio is invariant to the size of the pre-training dataset and can be predicted.
Improved uncertainty estimation through diverse sampling in neural networks.
problem Enhancing uncertainty estimation for machine learning models.
method Data-driven correlations and determinantal point processes-based sampling for dropout layers.
result State-of-the-art results in uncertainty estimation for regression and classification tasks.
A novel online feature selection method using DPP for diversity.
problem Online feature selection for diverse feature sets.
method DPP-based framework with three stages: sampling, local criteria, and global criteria.
result Demonstrated better compactness and comparable/outsuperior performance.
SEERL uses ensemble methods to improve reinforcement learning efficiency.
problem High sample complexity and computational expense in reinforcement learning.
method Directed perturbation of model parameters to learn diverse policies, selection of an adequately diverse set of policies.
result Our approach outperforms state-of-the-art scores in Atari 2600 and Mujoco.
A new algorithm uses bandits to diversify database activity monitoring.
problem Limitation of current DAM systems in collecting diverse data.
method Redefined DAM sampling as a bandit problem and developed a novel algorithm combining expert knowledge and random exploration.
result Adding diversity to sampling using the bandit-based approach improves coverage without decreasing alert quality.
Paper tackles sample-efficient offline RL, proposing data diversity and unified algorithms.
problem Sample-efficient learning from historical data for sequential decision-making.
method Proposes data diversity and unifies three offline RL algorithm classes: VS, RO, and PS.
result Comparable sample efficiency for VS, RO, and PS algorithms under standard assumptions.
Two new ALR approaches based on GS reduce labeled samples needed for regression.
problem Need substantial labeled samples for regression models, but unlabeled samples are easy to collect.
method Proposes two new ALR approaches based on greedy sampling (GS) to select beneficial unlabeled samples.
result Extensive experiments on various datasets verified the effectiveness and robustness of the approaches.
New metric evaluates generative models across domains, diagnosing fidelity, diversity, and generalization.
problem Evaluating generative models in diverse domains with limited metrics.
method Introduces a 3D evaluation metric (α-Precision, β-Recall, Authenticity) for domain-agnostic diagnostics. result Unified metric characterizes fidelity, diversity, and generalization, diagnosing model performance.
GM-GAN improves GANs for diverse datasets, enabling better image synthesis and clustering.
problem Limited performance of GANs on diverse datasets.
method Proposes GM-GAN with a mixture of Gaussians in latent space, and a new scoring method.
result Quantitatively outperforms baselines in diversity and quality of generated images.
A new model synthesizes population with fewer structural and sampling zeros.
problem Synthesizing a feasible and diverse synthetic population from limited data.
method A deep generative model with two regularizations to minimize structural zeros and preserve sampling zeros.
result The model significantly improves feasibility and diversity of synthetic populations.
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 co-evolutionary approach for Heston model calibration reduces overfitting with diverse datasets.
problem Overfitting and lack of generalization in Heston model calibration.
method Coupling a genetic algorithm with an evolving neural inverse map, using both GA-history sampling and Latin hypercube sampling.
result Diverse datasets improve out-of-sample stability and calibration accuracy.
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.
A new method optimizes diversity and sparsity for index tracking.
problem Accurately replicating a benchmark index with a small number of diverse assets.
method Jointly optimizes diversity and sparsity using a regularizer based on asset similarity.
result The proposed algorithm outperforms existing methods in out-of-sample backtesting.
This paper proposes new methods for ALR that consider informativeness, representativeness, and diversity.
problem Efficiently label samples for regression models with limited labeled data.
method Integrates informativeness, representativeness, and diversity in pool-based sequential active learning.
result Demonstrates effectiveness of new ALR approaches on 12 datasets.
Our model improves sequence prediction accuracy and diversity.
problem Predicting future sequences with uncertainty and diversity.
method Gaussian Latent Variable model with 'Best of Many' sample objective.
result Empirically outperforms prior work on diverse tasks.
We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity. We also derive a fam…
Proposes a new GAN framework using adversarial dropout to improve sample diversity and stability.
problem Mode collapse in GANs.
method Adversarial dropout in a dynamic ensemble of discriminators.
result Promotes sample diversity and stabilizes training.
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.
Ensemble of diverse CNNs detects and mitigates adversarial attacks.
problem Detecting and defending against adversarial attacks.
method An ensemble of specialized CNNs with a voting mechanism.
result Significant reduction in adversarial attack risk rate.
Paper organizes sampling methods for generative modeling.
problem Challenges in sampling with diffusion models.
method Reviews and organizes existing sampling methods.
result Reveals links between methods to overcome challenges.
DAC enhances exploration in reinforcement learning with entropy regularization.
problem Improving exploration efficiency in reinforcement learning.
method Sample-aware entropy regularization using replay buffer action distributions.
result DAC significantly outperforms existing algorithms in reinforcement learning tasks.
Paper tests DPPs for diversity models, distinguishing them from other distributions.
problem Testing whether a given distribution is a Determinantal Point Process (DPP) or far from any DPP.
method Proposes the first algorithm for DPP testing and establishes a lower bound on sample complexity.
result Establishes a matching lower bound on the sample complexity of DPP testing.
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different gener…
The paper proposes methods to diversify latent variable models using Bayesian learning.
problem Achieving diverse components in latent variable models to capture infrequent patterns and prevent overfitting.
method Two approaches: a mutual angular prior and diversity-promoting regularization over the post-data distribution, both implemented with variational inference and MCMC sampling.
result Efficient methods to diversify Bayesian mixture of experts and infinite latent feature models demonstrated on various datasets.
Study tackles RLHF with diverse human feedback, showing limitations and proposing a meta-learning approach.
problem Traditional RLHF fails to balance diverse human preferences.
method Integrates meta-learning and multiple social welfare functions to optimize diverse preferences.
result Establishes sample complexity bounds for optimizing diverse social welfare functions.
Proposes Vendi Score for evaluating diversity in ML models.
problem Lack of flexible diversity evaluation metrics in ML.
method Integrates ecological and quantum statistical mechanics concepts to define Vendi Score.
result Vendi Score enables flexible diversity evaluation without requiring a reference dataset.
AI models struggle to generate diverse natural chemical structures.
problem Generating diverse chemical structures for drug discovery.
method Quantified internal chemical diversity; challenge with two models.
result AI models fail to reproduce natural chemical diversity.
HyperGAN generates diverse neural network parameters for improved performance and uncertainty.
problem Overconfidence of neural networks in out-of-distribution data.
method Generative model using a novel mixer to learn a distribution of neural network parameters.
result HyperGAN can generate parameters that perform competitively with fully supervised learning and provide better uncertainty estimates.
Solves GAN mode collapse by assigning minibatches to multiple discriminators.
problem Mode collapse in GANs where models generate similar samples.
method Multiple discriminators, microbatching, and changing tasks.
result Promotes sample diversity in generated sets.
ODS improves adversarial attacks by maximizing output diversity.
problem Efficiency and effectiveness of adversarial attacks, especially black-box attacks.
method Output Diversified Sampling (ODS) that maximizes diversity in model outputs.
result ODS reduces the number of queries needed for black-box attacks on ImageNet by a factor of two.
New model reduces sampling cost in diffusion models, making them faster and applicable to real-world applications.
problem Challenges in generating high-quality samples, mode coverage, and fast sampling in deep generative models.
method Proposes denoising diffusion GANs that model each denoising step using a multimodal conditional GAN to reduce sampling cost.
result Demonstrates 2000imes faster sampling on CIFAR-10 dataset while maintaining competitive sample quality and diversity. 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 detects out-of-distribution samples in regression tasks.
problem Detecting instances far from training data in regression models.
method Estimating predictor entropy based on nearest neighbors and generative models.
result A new method for robust OOD detection in regression tasks.
Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 r…
Proposes DC3-GAN for diverse unsupervised conditional generation.
problem Low diversity in unsupervised conditional generation.
method Integrates encoder-generator pair with generator-encoder pair to enhance diversity.
result Improves clustering performance and disentanglement of latent variables.
Fine-tunes diffusion models to generate diverse samples with high genuine rewards.
problem Reward collapse in finetuning diffusion models.
method Entropy-regularized control against pretrained diffusion models.
result Efficient generation of diverse samples with high genuine rewards.