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.
Prior work finds a diversity paradox: diversity breeds innovation, and yet, underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-population of ~1.2 million US doctoral recipients from 1977…
SharedRep-RLHF learns shared traits for diverse groups, improving fairness and performance.
problem Uniform-reward RLHF fails to capture diverse preferences, leading to unfairness.
method SharedRep-RLHF learns shared traits among various groups, improving fairness and performance.
result SharedRep-RLHF outperforms MaxMin-RLHF by up to 20% in win rate.
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.
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.
Study optimal stopping for group with diverse discount rates using an attitude function.
problem Optimal stopping for a group with diverse discount rates under an aggregation preference.
method Develop iterative approach using consistent planning for time-consistent equilibria.
result Characterize all time-consistent mild equilibria as fixed points of an operator.
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…
Study examines how decoding algorithms affect fairness in language generation models.
problem Impact of decoding algorithms on fairness in open-ended language generation.
method Systematic analysis of top-p, top-k, and temperature decoding algorithms. result Decoding algorithms significantly impact fairness across demographic groups.
G-FIGS uses instance weights to create interpretable models from diverse data.
problem Generalizing to diverse data distributions while maintaining interpretability.
method Estimates group membership probabilities, uses as instance weights in FIGS to grow decision trees.
result Achieves state-of-the-art prediction performance and maintains interpretability.
Enhances student diversity in collaborative learning.
problem Student homogenization in large groups.
method Random routing, diverse feature sets, and random subgroup imitation.
result Significantly outperforms state-of-the-art approaches.
Paper perfect clusters sparse, diverse multilayer networks.
problem Clustering sparse, diverse multilayer networks.
method Tensor-based methodology pooling all layers' information.
result Achieves perfect clustering under sparser conditions than previous models.
Distillation is an effective knowledge-transfer technique that uses predicted distributions of a powerful teacher model as soft targets to train a less-parameterized student model. A pre-trained high capacity teacher, however, is not always available. Recently proposed online variants use the aggregated intermediate pr…
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.
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.
The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, then the aggregate decisions made by a group will often be better than those of its individual members. Inspired by this concept, we present a…
DRFormer uses dynamic tokenization and multi-scale transformer to forecast long time series.
problem Forecasting long-term time series data across diverse scales.
method Dynamic tokenizer, multi-scale transformer, dynamic sparse learning, rotary position encoding.
result DRFormer outperforms existing methods in forecasting accuracy.
Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of current users for information that will lead to better decisions in the future. Recently, concerns have been raised about whether the process …
Unique minimal translation surfaces found in Heisenberg group.
problem Classifying minimal translation surfaces in Heisenberg group.
method Complete classification through generating curves analysis.
result Uniqueness of minimal translation surfaces up to isometries.
The study explores smooth structures on specific four-manifolds with cyclic groups, finding many admit infinitely many smooth structures.
problem Exploring smooth structures on four-manifolds with finite cyclic fundamental groups.
method Analyzes topological four-manifolds with odd intersection forms and diverse fundamental groups.
result Many four-manifolds with cyclic fundamental groups admit infinitely many distinct smooth structures.
Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes. This skew goes beyond that which already exists in the world - e.g., Kay et al. showed that although 28% of CEOs in US are women, only 10% of the top 100 results for CEO in Goog…
The Hull-Strominger system for supersymmetric vacua of the heterotic string allows general unitary Hermitian connections with torsion and not just the Chern unitary connection. Solutions on unimodular Lie groups exploiting this flexibility were found by T. Fei and S.T. Yau. The Anomaly flow is a flow whose stationary p…
Model shows how diversity on corporate boards influences decision-making and innovation.
problem Understanding dynamics of diversity and innovation in corporate boards.
method Developed a dynamic model calibrated with empirical data of firm and board networks.
result Homophily and visibility biases shape the trajectory towards equality in corporate boards.
Paper uses SSC for identifying layers with identical community structures in DIMPLE networks.
problem Identifying layers with identical community structures in DIMPLE networks.
method Sparse Subspace Clustering (SSC) for identifying groups of layers with identical community structures.
result SSC leads to strongly consistent between-layer clustering under mild conditions.
Graph Metanetworks process diverse neural architectures efficiently.
problem Processing diverse neural architectures efficiently.
method Builds metanetworks using graph neural networks to process graphs representing input neural networks.
result Proves GMNs are expressive and equivariant to parameter permutation symmetries.
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learnin…
New methods improve uncertainty explanations for models.
problem Improving interpretation of uncertainty estimates from probabilistic models.
method Developed new methods to generate diverse and global explanations for uncertain model predictions.
result Generated diverse and global explanations for uncertain model predictions, addressing previous limitations.
Adaptive populations such as those in financial markets and distributed control can be modeled by the Minority Game. We consider how their dynamics depends on the agents' initial preferences of strategies, when the agents use linear or quadratic payoff functions to evaluate their strategies. We find that the fluctuatio…
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the data. Due to their multiplicity, multi-view data, can have different groupings that are reasonable and …
Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element -- "diversity." The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization…
New method estimates individual treatment effects using domain generalization.
problem Estimating causal individual treatment effects from observational data with treatment bias.
method Invariant Risk Minimization (IRM) framework to learn predictors invariant to domain-dependent factors.
result IRM-based ITE estimator shows gains over classical regression approaches in settings with pronounced support mismatch.
We study the topology of compact manifolds with a Lie group action for which there are only finitely many non-principal orbits, and describe the possible orbit spaces which can occur. If some non-principal orbit is singular, we show that the Lie group action must have odd cohomogeneity. We pay special attention to mani…
MosaicMRI expands public datasets for musculoskeletal MRI, revealing cross-anatomical correlations.
problem Limited diversity in public MRI datasets hinders model evaluation across different anatomical settings.
method Developed a large, diverse dataset (MosaicMRI) and conducted experiments on a baseline model (VarNet).
result Models trained on combined anatomies outperform anatomy-specific models in low-sample regimes.
The Burau representation is a fundamental bridge between the braid group and diverse other topics in mathematics. A 1974 question of Birman asks for a description of the image; in this paper we give a "strong approximation" to the answer. Since a 1984 paper of Squier it has been known that the Burau representation pres…
Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are designed for single-view data. We assume that the individuality and commonality of multi-view data can be leveraged to generate high-quality …
RobustTAD detects anomalies in diverse time series data.
problem Effective anomaly detection for complex time series data.
method Robust seasonal-trend decomposition + CNN architecture with data augmentation.
result RobustTAD outperforms other methods on public datasets.
Machine learning models predict which ideas will be innovated based on subjective perspectives.
problem Predicting high-impact innovation based on subjective perspectives and interpersonal innovation opportunities.
method Quantifying subjective perspectives and their interaction based on innovator positions within a geometric space of concepts.
result Subjective perspectives predict which ideas individuals and groups will creatively attend to and successfully combine in the future.
Proposes method to discover diverse near-optimal policies in reinforcement learning.
problem Finding different solutions to the same problem in reinforcement learning.
method Formalizes problem as CMDP, uses Successor Features, proposes new diversity rewards.
result Proposed method discovers diverse near-optimal policies that are robust and distinct.
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.
Unified theory explains diversity in ensemble learning.
problem Explaining diversity in ensemble learning across various scenarios.
method Developed a framework revealing diversity as a hidden dimension in bias-variance decomposition.
result Proved exact bias-variance-diversity decompositions for multiple losses in regression and classification.
SharpBalance improves deep ensemble performance by balancing sharpness and diversity.
problem Improving deep ensemble performance in both in-distribution and out-of-distribution scenarios.
method Introducing SharpBalance, a novel training approach that balances sharpness and diversity within ensembles.
result SharpBalance effectively improves the sharpness-diversity trade-off and ensemble performance in ID and OOD scenarios.
Equivariant cohomology simplifies symplectic manifold integrals with group actions.
problem Complex integrals on symplectic manifolds with Lie group actions.
method Atiyah-Bott Localization Theorem
result Simplifies integrals by revealing hidden structure.
Ensemble learning is a methodology that integrates multiple DNN learners for improving prediction performance of individual learners. Diversity is greater when the errors of the ensemble prediction is more uniformly distributed. Greater diversity is highly correlated with the increase in ensemble accuracy. Another attr…
Framework for ensuring fairness in machine learning models across multiple groups.
problem Ensuring fairness in machine learning models across multiple groups.
method Introduces (s,G,α)−GMC for multi-dimensional mappings and constraint sets, proposing algorithms to achieve multicalibration. result Demonstrates the effectiveness of the framework on various scenarios, including image segmentation, hierarchical classification, and text generation.
Research finds investors may lose from more diverse workplaces.
problem Investors' returns may be lower in more diverse companies.
method Examined D&I scores by Refinitiv in US and European markets.
result Investors may suffer lower returns for investing in more diverse companies.
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.
Given an integer b and a finitely presented group G we produce a compact symplectic six-manifold with c_1 = 0, b_2 > b, b_3 > b and fundamental group G. In the simply-connected case we can also arrange for b_3 = 0; in particular these examples are not diffeomorphic to Kähler manifolds with c_1 = 0. The construction beg…
Develops neural networks for reductive Lie groups, enhancing symmetry respect.
problem Symmetry respect in neural networks for reductive Lie groups.
method General equivariant neural network architecture for any reductive Lie Group G.
result Demonstrates generality and performance in top quark decay tagging and shape recognition.
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.