Identifies latent actions and dynamics from offline data with diverse demonstrators.
problem Recovering latent actions and environment dynamics from action-free trajectories.
method Assumes distinct policies for each demonstrator, identifies latent transitions and policies via matrix factorization.
result Identifies latent transitions and demonstrator policies up to permutation.
New model improves diversity in question generation over text.
problem Inadequate evaluation metrics for diverse questions.
method Proposed a variational encoder-decoder model and evaluation scheme.
result Improved diversity in generated questions without loss of quality.
Self-distillation improves model performance by increasing teacher diversity and smoothing predictions.
problem Improving model generalization and performance through self-distillation.
method Interpreting self-distillation as MAP estimation and proposing instance-specific label smoothing.
result Self-distillation enhances model performance by increasing teacher diversity and smoothing predictions.
High-capacity neural network ensembles often benefit more from high-capacity models than from increased diversity.
problem The performance of high-capacity neural network ensembles is often harmed by interventions that promote predictive diversity.
method A large-scale study of nearly 600 neural network classification ensembles, examining various interventions and architectures.
result Discouraging predictive diversity can be benign in large-network ensembles, and higher-capacity models often yield better performance than diverse architectures.
Ensembles depend on diversity for improved performance. Many ensemble training methods, therefore, attempt to optimize for diversity, which they almost always define in terms of differences in training set predictions. In this paper, however, we demonstrate the diversity of predictions on the training set does not nece…
We address the problem of partial index tracking, replicating a benchmark index using a small number of assets. Accurate tracking with a sparse portfolio is extensively studied as a classic finance problem. However in practice, a tracking portfolio must also be diverse in order to minimise risk -- a requirement which h…
Grassmannian packings improve CNN kernels' diversity and reduce sparsity.
problem Kernel sparsity and lack of diversity in CNNs decrease model capacity.
method Initialize CNN kernels with Grassmannian packings to maximize diversity and minimize sparsity.
result Grassmannian packings lead to diverse features and improved classification accuracy.
New framework predicts diverse, contextually plausible 3D human motions.
problem Predicting multiple plausible future 3D poses given observed poses.
method Developed a new variational framework that conditions latent variable on past observation to encourage relevant information.
result Our approach generates motions of higher quality and preserves contextual information.
Proposes MEDM to balance entropy minimization and diversity maximization for better domain adaptation.
problem Trivial solutions in entropy minimization for unsupervised domain adaptation.
method Introduces diversity maximization to balance with entropy minimization, controlled by deep embedded validation.
result MEDM outperforms state-of-the-art methods on four domain adaptation datasets.
A financial market is called "diverse" if no single stock is ever allowed to dominate the entire market in terms of relative capitalization. In the context of the standard Ito-process model initiated by Samuelson (1965) we formulate this property (and the allied, successively weaker notions of "weak diversity" and "asy…
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.
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.
Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction. Final goals are specified to our agent via images o…
How can we measure whether a natural language generation system produces both high quality and diverse outputs? Human evaluation captures quality but not diversity, as it does not catch models that simply plagiarize from the training set. On the other hand, statistical evaluation (i.e., perplexity) captures diversity b…
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.
New method learns diverse solutions in reinforcement learning without gradient bias.
problem Lack of diverse solutions in reinforcement learning tasks.
method Maximizes state-action-based mutual information directly, using variational lower bound.
result Successfully learns an infinite set of diverse solutions.
Heterogeneous SVO leads to diverse policies in sequential social dilemmas.
problem Understanding how diverse social value orientations affect behavior in sequential social dilemmas.
method Extending prior reinforcement learning studies, we instantiated heterogeneous SVO in a sequential social dilemma setting and measured task-specific diversity metrics.
result Heterogeneous SVO leads to meaningfully diverse policies across various incentive structures.
Paper presents a deterministic method for diverse subset selection.
problem Diverse subset selection problems in recommendation, summarization, and search.
method Greedy deterministic adaptation of k-DPP for low-rank approximations and image search.
result The method yields low-rank approximations of kernel matrices and demonstrates effectiveness in image search.
Proposes a method to increase diversity without sacrificing meritocracy.
problem Systemic bias in datasets affecting diversity and meritocracy.
method Optimally flipping outcome labels and training classification models simultaneously.
result The price of diversity is low and sometimes negative, enhancing diversity without significantly affecting meritocracy.
Study shows diverse data sources improve cryptocurrency forecasting models.
problem Improving cryptocurrency market forecasting accuracy.
method Integrating various data types, including on-chain metrics, traditional indices, and macroeconomic indicators.
result Data source diversity significantly enhances forecasting model performance.
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.
DMNL bandits optimize assortment choices balancing relevance and diversity.
problem Balancing relevance-driven choice with within-assortment diversity.
method Augments MNL choice probabilities with a submodular diversity function, proposing a white-box UCB-based algorithm.
result Achieves at least a (1−e+11)-approximate regret bound of $ ilde{O}\left(d \sqrt{T/K}
ight)$. New method improves neural architecture search by optimizing for both performance and diversity.
problem Traditional multi-objective NAS fails to address practical constraints and niches.
method Formulated as quality diversity optimization, introduces multifidelity optimizers.
result Quality diversity NAS outperforms multi-objective NAS in quality and efficiency.
This paper improves model generalization by integrating diverse pretrained models.
problem Leveraging diverse pretrained models for robust out-of-distribution generalization.
method Characterize and integrate diverse pretrained models based on diversity and correlation shifts.
result Demonstrates state-of-the-art out-of-distribution generalization performance.
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.
Proposes a Bayesian federated learning method for diverse tasks.
problem Current federated learning approaches focus on homogeneous tasks, ignoring task diversity.
method Integrates multi-task learning with MOGP at the local level and federated learning at the global level.
result Demonstrates superior predictive performance and uncertainty calibration on diverse tasks.
EDU method finds diverse optimal solutions for expensive simulators.
problem Optimizing expensive black-box simulators for diverse solutions.
method EDU method searches for diverse locally-optimal solutions within a tolerance level.
result EDU yields a closed-form acquisition function facilitating efficient sequential queries.
We propose a novel diverse feature selection method based on determinantal point processes (DPPs). Our model enables one to flexibly define diversity based on the covariance of features (similar to orthogonal matching pursuit) or alternatively based on side information. We introduce our approach in the context of Bayes…
Hypernetworks are neural networks that generate weights for another neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We explain how this simple formulation gener…
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.
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.
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.
Standard reinforcement learning methods aim to master one way of solving a task whereas there may exist multiple near-optimal policies. Being able to identify this collection of near-optimal policies can allow a domain expert to efficiently explore the space of reasonable solutions. Unfortunately, existing approaches t…
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.
TimeMCL forecasts diverse time series futures using neural networks and WTA loss.
problem Forecasting multiple plausible time series futures.
method Multiple Choice Learning (MCL) framework with Winner-Takes-All (WTA) loss.
result TimeMCL efficiently predicts diverse time series futures at low computational cost.
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 goal of imitation learning (IL) is to learn a good policy from high-quality demonstrations. However, the quality of demonstrations in reality can be diverse, since it is easier and cheaper to collect demonstrations from a mix of experts and amateurs. IL in such situations can be challenging, especially when the lev…
DASH improves ensemble generalizability by encouraging diverse, flat loss landscapes.
problem Improving generalization and robustness of deep ensembles.
method DASH promotes diversity and flatness in deep ensembles by encouraging base learners to move towards low-loss regions of minimal sharpness.
result DASH improves ensemble generalizability, as demonstrated by extensive empirical evidence.
CPRA efficiently finds diverse solutions in CO problems using UL and parallelization.
problem Finding optimal solutions often requires diverse outcomes in real-world applications.
method CPRA, an UL-based framework, discovers shared representations to generate diverse solutions.
result CPRA outperforms existing UL-based solvers in generating diverse solutions.
MAP-Elites generates diverse trading strategies for improved execution performance.
problem Optimizing trading execution schedules in volatile market conditions.
method Quality-diversity algorithm (MAP-Elites) generating a portfolio of specialized strategies.
result Diverse strategies achieve 8-10% performance improvements, validating quality-diversity methods.
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.
Recent work on mode connectivity in the loss landscape of deep neural networks has demonstrated that the locus of (sub-)optimal weight vectors lies on continuous paths. In this work, we train a neural network that serves as a hypernetwork, mapping a latent vector into high-performance (low-loss) weight vectors, general…
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 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.
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.
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.
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.