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Found 28 papers, 5 papers with code
Selectively Contextual Bandits
These online learning frameworks learn a treatment assignment policy in the presence of treatment effects that vary with the observed contextual features of the users.
Active Multitask Learning with Committees
The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.
Learning Correlated Latent Representations with Adaptive Priors
Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data.
A New Distribution on the Simplex with Auto-Encoding Applications
We construct a new distribution for the simplex using the Kumaraswamy distribution and an ordered stick-breaking process.
Correlated Variational Auto-Encoders
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data.
Beta Survival Models
This article analyzes the problem of estimating the time until an event occurs, also known as survival modeling.
Thompson Sampling for Noncompliant Bandits
Thompson sampling, a Bayesian method for balancing exploration and exploitation in bandit problems, has theoretical guarantees and exhibits strong empirical performance in many domains.
Item Recommendation with Variational Autoencoders and Heterogenous Priors
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback.
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals.
A refinement of Bennett's inequality with applications to portfolio optimization
The bound is strictly sharper in the homogeneous setting and very often significantly sharper in the heterogeneous setting.
Variational Autoencoders for Collaborative Filtering
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.
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Initialization and Coordinate Optimization for Multi-way Matching
We consider the problem of consistently matching multiple sets of elements to each other, which is a common task in fields such as computer vision.
Frank-Wolfe Algorithms for Saddle Point Problems
We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained smooth convex-concave saddle point (SP) problems.
Binary embeddings with structured hashed projections
We prove several theoretical results showing that projections via various structured matrices followed by nonlinear mappings accurately preserve the angular distance between input high-dimensional vectors.
Bethe Learning of Conditional Random Fields via MAP Decoding
Many machine learning tasks can be formulated in terms of predicting structured outputs.
Making Pairwise Binary Graphical Models Attractive
The later has better convergence properties but typically provides poorer estimates.
Clamping Variables and Approximate Inference
It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is upper bounded by the true partition function for a binary pairwise model that is attractive.
On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or "bags", and only the proportion of each class in each bag is known.
Approximating the Bethe partition function
When belief propagation (BP) converges, it does so to a stationary point of the Bethe free energy $F$, and is often strikingly accurate.
Adaptive Anonymity via b-Matching
The adaptive anonymity problem is formalized where each individual shares their data along with an integer value to indicate their personal level of desired privacy.
A multi-agent control framework for co-adaptation in brain-computer interfaces
In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user's neural response.
Stochastic Bound Majorization
Recently a majorization method for optimizing partition functions of log-linear models was proposed alongside a novel quadratic variational upper-bound.
Semistochastic Quadratic Bound Methods
Batch methods based on the quadratic bound were recently proposed for this class of problems, and performed favorably in comparison to state-of-the-art techniques.
$\propto$SVM for learning with label proportions
We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known.
Majorization for CRFs and Latent Likelihoods
The partition function plays a key role in probabilistic modeling including conditional random fields, graphical models, and maximum likelihood estimation.
Learning a Distance Metric from a Network
To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm for learning a Mahalanobis distance metric from a network such that the learned distances are tied to the inherent connectivity structure of the network.
Variance Penalizing AdaBoost
Thus, the proposed algorithm solves a key limitation of previous empirical Bernstein boosting methods which required brute force enumeration of all possible weak learners.
Relative Margin Machines
In classification problems, Support Vector Machines maximize the margin of separation between two classes.
