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Found 34 papers, 5 papers with code
Fairness Risks for Group-conditionally Missing Demographics
Fairness-aware classification models have gained increasing attention in recent years as concerns grow on discrimination against some demographic groups.
Augmenting Offline Reinforcement Learning with State-only Interactions
Batch offline data have been shown considerably beneficial for reinforcement learning.
Machine Learning for Detection of 3D Features using sparse X-ray data
We utilize half a dozen different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data.
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e. g., graph classification.
Euclidean Invariant Recognition of 2D Shapes Using Histograms of Magnitudes of Local Fourier-Mellin Descriptors
Because the magnitude of inner products with its basis functions are invariant to rotation and scale change, the Fourier-Mellin transform has long been used as a component in Euclidean invariant 2D shape recognition systems.
Similarity Equivariant Linear Transformation of Joint Orientation-Scale Space Representations
Group convolution generalizes the concept to linear operations on functions of group elements representing more general geometric transformations and which commute with those transformations.
Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation
Probability discrepancy measure is a fundamental construct for numerous machine learning models such as weakly supervised learning and generative modeling.
Distributionally Robust Imitation Learning
We consider the imitation learning problem of learning a policy in a Markov Decision Process (MDP) setting where the reward function is not given, but demonstrations from experts are available.
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
Graph convolution networks (GCNs) have become effective models for graph classification.
Proximal Mapping for Deep Regularization
Underpinning the success of deep learning is effective regularizations that allow a variety of priors in data to be modeled.
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Invariance (defined in a general sense) has been one of the most effective priors for representation learning.
Generalised Lipschitz Regularisation Equals Distributional Robustness
The problem of adversarial examples has highlighted the need for a theory of regularisation that is general enough to apply to exotic function classes, such as universal approximators.
Certifying Distributional Robustness using Lipschitz Regularisation
Distributional robust risk (DRR) minimisation has arisen as a flexible and effective framework for machine learning.
Consistent Robust Adversarial Prediction for General Multiclass Classification
We propose a robust adversarial prediction framework for general multiclass classification.
Distributionally Robust Graphical Models
Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency.
Efficient and Consistent Adversarial Bipartite Matching
Many important structured prediction problems, including learning to rank items, correspondence-based natural language processing, and multi-object tracking, can be formulated as weighted bipartite matching optimizations.
Inductive Two-Layer Modeling with Parametric Bregman Transfer
Latent prediction models, exemplified by multi-layer networks, employ hidden variables that automate abstract feature discovery.
Exp-Concavity of Proper Composite Losses
In special cases like the Aggregating Algorithm (\cite{vovk1995game}) with mixable losses and the Weighted Average Algorithm (\cite{kivinen1999averaging}) with exp-concave losses, it is possible to achieve $O(1)$ regret bounds.
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search
Frank-Wolfe (FW) algorithms with linear convergence rates have recently achieved great efficiency in many applications.
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction
Adversarial machines, where a learner competes against an adversary, have regained much recent interest in machine learning.
Convex Two-Layer Modeling with Latent Structure
Unsupervised learning of structured predictors has been a long standing pursuit in machine learning.
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging.
Robust Bayesian Max-Margin Clustering
We present max-margin Bayesian clustering (BMC), a general and robust framework that incorporates the max-margin criterion into Bayesian clustering models, as well as two concrete models of BMC to demonstrate its flexibility and effectiveness in dealing with different clustering tasks.
Convex Deep Learning via Normalized Kernels
Deep learning has been a long standing pursuit in machine learning, which until recently was hampered by unreliable training methods before the discovery of improved heuristics for embedded layer training.
Generalized Conditional Gradient for Sparse Estimation
Structured sparsity is an important modeling tool that expands the applicability of convex formulations for data analysis, however it also creates significant challenges for efficient algorithm design.
Distributed Stochastic Optimization of the Regularized Risk
Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task.
Convex Two-Layer Modeling
Latent variable prediction models, such as multi-layer networks, impose auxiliary latent variables between inputs and outputs to allow automatic inference of implicit features useful for prediction.
Polar Operators for Structured Sparse Estimation
Structured sparse estimation has become an important technique in many areas of data analysis.
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
For the representer theorem to hold, the linear functionals are required to be bounded in the RKHS, and we show that this is true for a variety of commonly used RKHS and invariances.
Convex Relaxations of Bregman Divergence Clustering
Although many convex relaxations of clustering have been proposed in the past decade, current formulations remain restricted to spherical Gaussian or discriminative models and are susceptible to imbalanced clusters.
Convex Multi-view Subspace Learning
Subspace learning seeks a low dimensional representation of data that enables accurate reconstruction.
Accelerated Training for Matrix-norm Regularization: A Boosting Approach
Sparse learning models typically combine a smooth loss with a nonsmooth penalty, such as trace norm.
Lower Bounds on Rate of Convergence of Cutting Plane Methods
By exploiting the structure of the objective function we can devise an algorithm that converges in $O(1/\sqrt{\epsilon})$ iterations.
Kernel Measures of Independence for non-iid Data
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion.
