We describe an elementary combinatorial move on the set of quadratic differentials with a horizontal one cylinder decom-position. Computer experiment suggests that the corresponding equivalent classes are in one-to-one correspondence with the con-nected component of the strata.
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48 results for “correspondence experiments”
We present new findings in regard to data analysis in very high dimensional spaces. We use dimensionalities up to around one million. A particular benefit of Correspondence Analysis is its suitability for carrying out an orthonormal mapping, or scaling, of power law distributed data. Power law distributed data are foun…
We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities. The approach is capable of learning correspondences between views as well as correspondences between individual data-points. The proposed method requires only a few aligned exam…
Study detects illegal discrimination by employers using correspondence experiments.
problem Detecting illegal discrimination by individual employers based on protected characteristics.
method Correspondence experiments, bounding higher moments of causal effects, decision rules for investigation.
result 85% of jobs contacting both white and black applicants are likely to discriminate.
Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experimentscs.CL
This paper introduces PyDCI, a new implementation of Distributional Correspondence Indexing (DCI) written in Python. DCI is a transfer learning method for cross-domain and cross-lingual text classification for which we had provided an implementation (here called JaDCI) built on top of JaTeCS, a Java framework for text …
Numerical experiments support conjecture about opers and nonabelian Hodge.
problem Testing predictions of Gaiotto-Moore-Neitzke and Gaiotto conjectures.
method Numerical experiments on polynomial holomorphic differentials.
result Supports conjectural formulas for Stokes data and Hitchin metric tensor.
New method learns transport cost from subset correspondence.
problem Aligning multiple datasets with accurate cost functions.
method End-to-end optimizer (OT-SI) that differentiates through Sinkhorn algorithm.
result Substantially outperforms state-of-the-art benchmarks.
New method minimizes experiment cost while maintaining accuracy.
problem Minimizing cost in experiments with interference or other concerns.
method Synthetically Controlled Thompson Sampling (SCTS).
result Minimizes regret and maintains inferential ability.
ROBOT framework solves regression without correspondence for large data and complex models.
problem Regression without known correspondence in large datasets.
method ROBOT framework reformulates regression as a continuous optimization problem and uses hypergradient approach.
result ROBOT achieves better performance than existing methods in linear and nonlinear regression tasks.
We develop topological methods for analyzing difference topology experiments involving 3-string tangles. Difference topology is a novel technique used to unveil the structure of stable protein-DNA complexes involving two or more DNA segments. We analyze such experiments for the Mu protein-DNA complex. We characterize t…
New method learns shape correspondences robustly from raw geometry.
problem Inaccurate and poor generalization of shape correspondences.
method Learning-based approach with feature-extraction network and functional map representation.
result Robust and accurate shape correspondence learning with less training data.
Hashing has been widely adopted for large-scale data retrieval in many domains, due to its low storage cost and high retrieval speed. Existing cross-modal hashing methods optimistically assume that the correspondence between training samples across modalities are readily available. This assumption is unrealistic in pra…
FRD protects privacy in distributed RL by sharing proxy experience memory.
problem Privacy violation in exchanging experience memory in distributed RL.
method Proposes FRD framework using proxy experience memory.
result Numerical evaluation shows FRD is effective and performance depends on proxy memory structure.
Method learns equivariances from data without custom architecture design.
problem Learning equivariances for tasks without manually designed architectures.
method Reparameterization to learn equivariant parameter sharing.
result Can learn equivariances for any finite group of transformations.
In this work, a unified framework for gradient-free Multidimensional Scaling (MDS) based on Coordinate Search (CS) is proposed. This family of algorithms is an instance of General Pattern Search (GPS) methods which avoid the explicit computation of derivatives but instead evaluate the objective function while searching…
We introduce exact macroscopic on-line learning dynamics of two-layer neural networks with ReLU units in the form of a system of differential equations, using techniques borrowed from statistical physics. For the first experiments, numerical solutions reveal similar behavior compared to sigmoidal activation researched …
New algorithm recovers matrices with unknown correspondences.
problem Recovering matrices from observations with unknown correspondences.
method Solves a nuclear norm minimization problem via proximal gradient with a Max-Oracle.
result Achieves state-of-the-art performance and high accuracy in recovering ground-truth correspondences.
Paper proposes PRR network for better experience reuse in reinforcement learning.
problem Efficient experience reuse in reinforcement learning across multiple granularities.
method Proposes PRR network trained on multi-level architecture to extract and store experience.
result PRR network leads to better experience reuse and improved performance.
New confidence intervals improve treatment effect estimation in randomized experiments.
problem Improving confidence intervals for treatment effects in randomized experiments.
method Systematic exploitation of negative dependence or variance adaptivity.
result Achieved nonasymptotic confidence intervals with the same effective sample size as asymptotic ones.
BoTier optimizes experiments by balancing multiple objectives hierarchically.
problem Balancing multiple competing objectives in scientific experiments.
method Composite objective that flexibly represents a hierarchy of preferences over outcomes and parameters.
result Demonstrates robust applicability across various use cases and seamless integration.
Spatial audio is an essential medium to audiences for 3D visual and auditory experience. However, the recording devices and techniques are expensive or inaccessible to the general public. In this work, we propose a self-supervised audio spatialization network that can generate spatial audio given the corresponding vide…
MGLM models all possible language channel factorizations for improved multilingual generation.
problem Generating multilingual text with flexibility and quality.
method Generative joint distribution model over language channels, marginalizing all possible factorizations.
result MGLM outperforms traditional models in multilingual generation tasks.
Graph neural networks can perform approximate reasoning in latent space for mathematical statements.
problem Can neural networks perform steps of approximate reasoning in a fixed dimensional latent space?
method Design and conduct an experiment using graph neural networks to predict rewrite-success of mathematical statements in a latent space.
result Graph neural networks can make non-trivial predictions about rewrite-success of statements in latent space.
Generalization Bounds for Semi-supervised Matrix Completion with Distributional Side Informationcs.LG
The paper analyzes matrix completion with unlabeled implicit feedback and provides error bounds.
problem Matrix completion with shared low-rank ground truth and sampling distribution.
method Combining subspace recovery theory and matrix completion bounds.
result Error bounds showing contributions from estimating the sampling distribution and ground truth.
This study optimizes covariate density and propensity score for efficient ATE estimation.
problem Efficiently estimating average treatment effects (ATEs) with minimal variance.
method Adaptive experiment optimizing both covariate density and propensity score.
result Proposed method minimizes the semiparametric efficiency bound for ATE estimation.
3D good continuation model explains stereo vision using neurogeometry.
problem Understanding how the brain processes 3D visual correspondence.
method Developed a neurogeometric model involving spatial and orientation disparities.
result Provides insight into neural organization and correspondence problem.
Proposes a method to optimize neural network initialization using marginal likelihood maximization.
problem Optimizing hyperparameters for neural network initialization.
method Leverages the connection between neural networks and Gaussian processes to infer optimal hyperparameters.
result Marginal likelihood maximization provides near-optimal prediction performance on MNIST classification tasks.
Optimal adaptive experiment for choosing best treatment with binary outcomes.
problem Choosing the best treatment from binary options in an adaptive experiment.
method Adaptive experiment with two phases: treatment allocation and choice. Neyman allocation method used.
result Neyman allocation is minimax and Bayes optimal, matching lower bounds for regret.
Proposes FROT for high-dimensional data, avoiding curse of dimensionality.
problem High-dimensional data challenges in optimal transport.
method Feature selection and min-max optimization for robust transport plan.
result FROT achieves state-of-the-art performance in semantic correspondence.
Random forests classify Pokemon names based on evolutionary status.
problem Classifying Pokemon names into pre-evolution and post-evolution categories.
method Trained random forests using sound symbolism from Pokemon names, conducted experiments with human participants.
result Random forests outperform human participants in classifying Pokemon names.
High-dimensional ConvNets detect patterns in 32+ dimensions for geometric registration.
problem Detecting geometric patterns in high-dimensional spaces.
method High-dimensional convolutional networks applied to geometric registration problems.
result High-dimensional ConvNets outperform global pooling approaches in 3D registration and image correspondence.
A new method explains Siamese neural networks using feature comparison and autoencoder.
problem Explaining the decision-making process of Siamese neural networks.
method Feature comparison at embedding level and autoencoder reconstruction.
result The method effectively explains Siamese neural networks using MNIST dataset.
Traditionally, psychophysical experiments are conducted by repeated measurements on a few well-trained participants under well-controlled conditions, often resulting in, if done properly, high quality data. In recent years, however, crowdsourcing platforms are becoming increasingly popular means of data collection, mea…
We present convolutional neural network (CNN) based approaches for unsupervised multimodal subspace clustering. The proposed framework consists of three main stages - multimodal encoder, self-expressive layer, and multimodal decoder. The encoder takes multimodal data as input and fuses them to a latent space representa…
Bayesian Experience Reuse improves learning from multiple experts.
problem Learning from multiple experts with conflicting goals.
method Bayesian neural networks with shared features to model uncertainty and derive a probability distribution over expert models.
result BERS method effectively samples demonstrations from the derived distribution to reuse them in new tasks.
Machine learning experiments often mislead due to unmet assumptions.
problem Machine learning experiments with pooled data may not meet necessary assumptions for unbiased causal effect estimation.
method Analysis of assumptions required for unbiased causal effect estimation in machine learning experiments.
result Practical applications of A/B-tests with machine learning models may not yield unbiased estimates of causal effect.
Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.
problem Estimating long-term treatment effects in online advertising systems.
method Modeling treatment effects as a stopped random walk, using experimental design, and statistical methods.
result Confidence intervals for long-term treatment effects constructed using statistical techniques.
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.
Wiki-CS dataset benchmarks Graph Neural Networks using Wikipedia articles.
problem Benchmarking Graph Neural Networks on a new domain with structural differences.
method Derived from Wikipedia, nodes represent Computer Science articles, edges from hyperlinks, 10 classes for different branches, evaluated semi-supervised node classification and link prediction.
result Graph Neural Networks perform well on Wiki-CS, showing structural differences from earlier benchmarks.
Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e…
Generative Adversarial Networks improve affective speech feature generation.
problem Improving feature representation for emotion recognition.
method Experimented with GAN architectures to generate feature vectors corresponding to emotions.
result GANs generate realistic synthetic samples for emotion recognition.
Efficient classifier error estimation without re-training.
problem Estimating classifier error without re-training.
method Generalized resubstitution based on empirical measures.
result Consistent and asymptotically unbiased error estimation.
We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures. Two existing classes are contained as special cases: the original two-parameter Indian buffet process, corresponding to the Dirichlet process, and the stable (or three-param…
We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution. That is, given the capacity to reset the state with those corresponding to the agent's past observations, we help exploration by promoting faster state-space coverage via restarting the agent from a mo…
A CNN-DRL model improves learning in finance with scalable actions.
problem Finance environments with large action scales are hard for MLP-based DRL agents.
method Designed a CNN agent that uses historical data to adapt to large action scales.
result The CNN-DRL model remains stable and learns the environment better than MLP.
We study the problem of causal structure learning when the experimenter is limited to perform at most non-adaptive experiments of size . We formulate the problem of finding the best intervention target set as an optimization problem, which aims to maximize the average number of edges whose directions are resolve…
Graphical lasso may fail to fit models when data points are insufficient.
problem When does graphical lasso fail to select and fit a graphical model?
method Computational experiments with graphical lasso.
result Graphical lasso may fail when the number of data points is less than the maximum likelihood threshold.
Learning an optimal policy from a multi-modal reward function is a challenging problem in reinforcement learning (RL). Hierarchical RL (HRL) tackles this problem by learning a hierarchical policy, where multiple option policies are in charge of different strategies corresponding to modes of a reward function and a gati…