TGAN models tabular data using GAN for realistic synthetic data.
problem Modeling tabular data with mixed discrete and continuous columns.
method Conditional GAN for modeling tabular data.
result TGAN outperforms Bayesian methods on most real datasets.
Human advice improves deep learning from sparse samples.
problem Learning from sparse, noisy samples in deep models.
method Knowledge-augmented Column Networks using human advice.
result Significantly improved performance or faster convergence.
Proposes a deep learning framework guided by human advice.
problem Learning from sparse and noisy data.
method Knowledge-augmented Column Networks, leveraging human advice.
result Improves model performance in domains with structured representations.
This paper improves neural network efficiency by combining filter columns and retraining, boosting array utilization and accuracy.
problem Efficient implementation of sparse convolutional neural networks on systolic arrays.
method Column combining of filter matrices, retraining of remaining weights, joint optimization for high utilization and accuracy.
result Significantly increased systolic array utilization efficiency (e.g., ~4x) and maintained high classification accuracy.
The paper tackles one-for-many counterfactual explanations using column generation.
problem Minimizing the number of explanations needed for a group of instances with sparsity constraints.
method Developed a novel column generation framework to efficiently search for explanations for any black-box classifier.
result The column generation framework outperforms existing methods in scalability, computational performance, and solution quality.
Paper tackles BNSL with IP, improving quality of solutions.
problem Bayesian Network Structure Learning (BNSL) with IP formulations.
method Inexact column generation using difference-of-submodular optimization.
result Improved solutions quality compared to state-of-the-art approaches.
A method for community detection in multilayer networks using data matrices.
problem Community detection in multilayer networks with various node attributes.
method Data matrix representation and regular decomposition method extension for compression.
result Method identifies community structures well-aligned with real-world network hierarchies.
The paper proposes a new model to analyze directed networks and accurately estimate community memberships.
problem Modeling and estimating community memberships in directed networks with heterogeneous degrees.
method Directed Degree Corrected Mixed Membership (DiDCMM) model and DiMSC algorithm.
result The proposed DiMSC algorithm is asymptotically consistent and provides error bounds for community membership vectors.
DCoM uses deep neural networks to detect semantic data types from raw column values.
problem Detecting semantic data types from dirty and unseen data.
method DCoM employs multi-input NLP-based deep neural networks trained on 686,765 data columns.
result DCoM outperforms existing methods significantly on 78 different semantic data types.
Sherlock uses deep learning to accurately detect data types from column headers.
problem Detecting accurate semantic types of data columns for data science tasks.
method Sherlock is a multi-input deep neural network trained on a corpus of 686,765 data columns.
result Sherlock achieves a support-weighted F1 score of 0.89, outperforming existing methods.
A neuro-inspired architecture learns without supervision using clustering and predictive coding.
problem Achieving continual learning without supervision.
method Neuro-inspired architecture based on online clustering and hierarchical predictive coding.
result The architecture achieves continual learning without supervision.
DiMMSB models directed mixed membership networks, identifying distinct community structures.
problem Modeling directed mixed membership networks with distinct community structures.
method Directed Mixed Membership Stochastic Blockmodel (DiMMSB) with DiSP algorithm.
result DiSP algorithm is asymptotically consistent and outperforms competitors.
New method recovers matrix column space with active sampling for better results.
problem Recovering column space of partially observed matrices with limited data.
method Alternating minimization with active sampling strategy.
result Active sampling improves convergence to true column space with higher probability.
Improved model for grouping nodes in bipartite networks.
problem Challenges in grouping nodes in bipartite graphs.
method Introduced DC-LBM and developed variational EM algorithm.
result Significantly enhanced performance on real-world data.
We consider the problem of matrix column subset selection, which selects a subset of columns from an input matrix such that the input can be well approximated by the span of the selected columns. Column subset selection has been applied to numerous real-world data applications such as population genetics summarization,…
This paper studies quandles with one non-trivial column and their properties.
problem Understanding quandles with exactly one non-trivially permuted column.
method Investigates automorphism groups, polynomials, cohomology, and hom quandles of quandles with one non-trivial column.
result The properties of these quandles relate to linking number.
New bounds for transfer learning in linear models, improving generalization.
problem Understanding when auxiliary data helps in improving generalization in linear models.
method Derivation of exact error bounds and optimal task weights for linear regression and linear neural networks.
result First non-vacuous sufficient conditions for beneficial auxiliary learning in linear neural networks.
The column group is a subgroup of the symmetric group on the elements of a finite blackboard birack generated by the column permutations in the birack matrix. We use subgroups of the column group associated to birack homomorphisms to define an enhancement of the integral birack counting invariant and give examples whic…
In this paper, we consider matrix completion from non-uniformly sampled entries including fully observed and partially observed columns. Specifically, we assume that a small number of columns are randomly selected and fully observed, and each remaining column is partially observed with uniform sampling. To recover the …
Double autoencoder Ae2I improves missing value imputation in recommender systems.
problem Imputing missing values in tables using row-row and column-column relationships.
method Simultaneously uses row-row and column-column relationships through a double autoencoder.
result Ae2I outperforms state-of-the-art models in recommender systems. Neuromorphic column performs online unsupervised clustering.
problem Real-time clustering of streaming data.
method Localized, spike timing-dependent plasticity (STDP) neural column.
result Prototype column performs similarly to k-means clustering.
Boosts neural network performance by improving weight separability.
problem Improving the separability of weight vectors in neural networks.
method Proposes a new evaluation metric and feed-backward reconstruction loss to encourage weight separability.
result Improves visual recognition performance across various tasks.
Paper tackles low-rank matrix recovery with column ℓ2,0-norm regularization.
problem Low-rank matrix recovery problems with column sparsity constraints.
method Developed alternating majorization-minimization (AMM) methods with extrapolation and hybrid AMM.
result Global convergence analysis and superior performance in matrix completion problems.
A new method for learning row and column structures with missing data.
problem Learning row and column structures in data with missing values.
method A three-component unsupervised approach: estimating a complete matrix, computing pairwise distances, and constructing representations.
result Our method outperforms other methods in data visualization and clustering.
Transposable data represents interactions among two sets of entities, and are typically represented as a matrix containing the known interaction values. Additional side information may consist of feature vectors specific to entities corresponding to the rows and/or columns of such a matrix. Further information may also…
RECol generates error columns to improve outlier detection.
problem Outlier detection in data with complex relationships.
method Generates reconstruction error columns for leave-one-out feature sets.
result Improves ROC-AUC and PR-AUC values of common outlier detection methods.
The paper identifies redundant columns in matrices for feature selection and clustering.
problem Identifying redundant columns in matrices for feature selection and clustering.
method Proves that after re-ordering columns, a matrix can be block-diagonalized revealing linearly dependent columns.
result Identifies redundant columns in matrices, aiding in feature selection and clustering.
Paper tackles fair low-rank approximation and column subset selection.
problem Minimize loss over sub-populations in machine learning.
method Developed algorithms for fair low-rank approximation and fair column subset selection.
result Achieved polynomial time algorithms for fair low-rank approximation.
We consider a column of a rotating stationary surface in Euclidean space. We obtain a value l0>0 in such way that if the length l of column satisfies l>l0, then the surface is instable. This extends, in some sense, previous results due to Plateau and Rayleigh for columns of surfaces with constant mean curvature…
New algorithm learns principal subspace from random samples.
problem Learning principal subspace from small random submatrices.
method Stochastic gradient descent algorithm for neural networks.
result Algorithm can handle infinite rows and columns.
This paper considers the problem of matrix completion when some number of the columns are completely and arbitrarily corrupted, potentially by a malicious adversary. It is well-known that standard algorithms for matrix completion can return arbitrarily poor results, if even a single column is corrupted. One direct appl…
We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix map…
Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than non-collective classifiers, collective classification is computational cha…
Machine learning predicts flight connections for airline crew scheduling.
problem Predicting the next connecting flight for airline crews.
method Adapted neural network for multiclass classification from historical data.
result High accuracy (99.7%) in flight connection prediction.
This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a target matrix B. The paper then proposes a fast greedy algorithm for solving this problem and draws connections to different problems that ca…
The paper uses Column Generation for faster construction of binary decision trees.
problem Constructing efficient univariate binary decision trees for classification tasks.
method Proposes an Integer Linear Programming (ILP) formulation and solves it via Column Generation based heuristic.
result The approach is competitive with state-of-the-art ILP-based algorithms and can handle large datasets.
We consider the problem of performing matrix completion with side information on row-by-row and column-by-column similarities. We build upon recent proposals for matrix estimation with smoothness constraints with respect to row and column graphs. We present a novel iterative procedure for directly minimizing an informa…
It has been recently shown that numerical semiparametric bounds on the expected payoff of fi- nancial or actuarial instruments can be computed using semidefinite programming. However, this approach has practical limitations. Here we use column generation, a classical optimization technique, to address these limitations…
A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individual rows or columns of the matrix is limited by either system constrain…
We study the problem of instance segmentation in biological images with crowded and compact cells. We formulate this task as an integer program where variables correspond to cells and constraints enforce that cells do not overlap. To solve this integer program, we propose a column generation formulation where the prici…
Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix). Such a representation works nicely with algebraic manipulations. Here we introduce a simple method to augment an attributed graph with a virtual node that is bidirectionally connected to all existing nodes…
Solves portfolio optimization with cardinality constraints using column generation.
problem Portfolio optimization with cardinality constraints.
method Column generation method applied to a subset of assets in a master convex quadratic problem, using dual information to propose new assets.
result Solves portfolio optimization problems efficiently with cardinality constraints.
Unified methods for fast column selection in various applications.
problem Efficiently selecting columns for low-rank approximations in data science and machine learning.
method Deterministic and randomized algorithms exploiting nuclear scores.
result Theoretical guarantees and performance bounds for column selection.
A new method selects columns of a matrix for dimensionality reduction with better error bounds and performance.
problem Selecting a subset of columns for dimensionality reduction with good approximation error.
method A projection determinantal point process (DPP) for column subset selection.
result Improved bounds on approximation error compared to PCA and volume sampling.
Proposes a new matrix factorization model for interval-valued matrices.
problem Matrix factorization for matrices with entries in a given interval.
method Bounded simplex-structured matrix factorization (BSSMF) with fast algorithm for missing data.
result BSSMF provides a unique decomposition under certain conditions.
DeepTMR reorders matrices without prior knowledge of structural patterns.
problem Matrix reordering without prior structural knowledge.
method DeepTMR uses a neural network to automatically extract features and reorder matrices.
result Trained network produces denoised mean matrix for visualization.
We study a data model in which the data matrix D can be expressed as D = L + S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a matrix whose non-zero columns are outlying data points. To date, robust PCA algorithms have solely considered models with either S or C, but not both. As such, existi…
In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure. In this paper, we consider a class of binary matrices, arising in many applications, which exhibit both row and column cluster structure, and our goal is …