Search Results for author: Tony Jebara

Found 27 papers, 4 papers with code

Active Multitask Learning with Committees

no code implementations24 Mar 2021 Jingxi Xu, Da Tang, Tony Jebara

The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.

Transfer Learning

Learning Correlated Latent Representations with Adaptive Priors

no code implementations14 Jun 2019 Da Tang, Dawen Liang, Nicholas Ruozzi, Tony Jebara

Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data.

Link Prediction

A New Distribution on the Simplex with Auto-Encoding Applications

1 code implementation NeurIPS 2019 Andrew Stirn, Tony Jebara, David A. Knowles

We construct a new distribution for the simplex using the Kumaraswamy distribution and an ordered stick-breaking process.

Correlated Variational Auto-Encoders

2 code implementations ICLR Workshop DeepGenStruct 2019 Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi

Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data.

Link Prediction

Beta Survival Models

no code implementations9 May 2019 David Hubbard, Benoit Rostykus, Yves Raimond, Tony Jebara

This article analyzes the problem of estimating the time until an event occurs, also known as survival modeling.

Thompson Sampling for Noncompliant Bandits

no code implementations3 Dec 2018 Andrew Stirn, Tony Jebara

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

no code implementations17 Jul 2018 Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan, Jie Yuan, Da Tang, Tony Jebara

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.

Collaborative Filtering

Subgoal Discovery for Hierarchical Dialogue Policy Learning

no code implementations EMNLP 2018 Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara

Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals.

Hierarchical Reinforcement Learning

A refinement of Bennett's inequality with applications to portfolio optimization

no code implementations16 Apr 2018 Tony Jebara

The bound is strictly sharper in the homogeneous setting and very often significantly sharper in the heterogeneous setting.

Portfolio Optimization

Variational Autoencoders for Collaborative Filtering

14 code implementations16 Feb 2018 Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara

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.

Bayesian Inference Collaborative Filtering +2

Initialization and Coordinate Optimization for Multi-way Matching

no code implementations2 Nov 2016 Da Tang, Tony Jebara

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

no code implementations25 Oct 2016 Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien

We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained smooth convex-concave saddle point (SP) problems.

Structured Prediction

Binary embeddings with structured hashed projections

no code implementations16 Nov 2015 Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann Lecun

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

no code implementations4 Mar 2015 Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara

Many machine learning tasks can be formulated in terms of predicting structured outputs.

Semantic Segmentation

Making Pairwise Binary Graphical Models Attractive

no code implementations NeurIPS 2014 Nicholas Ruozzi, Tony Jebara

The later has better convergence properties but typically provides poorer estimates.

Clamping Variables and Approximate Inference

no code implementations NeurIPS 2014 Adrian Weller, Tony Jebara

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

1 code implementation24 Feb 2014 Felix X. Yu, Krzysztof Choromanski, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang

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

no code implementations30 Dec 2013 Adrian Weller, Tony Jebara

When belief propagation (BP) converges, it does so to a stationary point of the Bethe free energy $F$, and is often strikingly accurate.

A multi-agent control framework for co-adaptation in brain-computer interfaces

no code implementations NeurIPS 2013 Josh S. Merel, Roy Fox, Tony Jebara, Liam Paninski

In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user's neural response.

Adaptive Anonymity via b-Matching

no code implementations NeurIPS 2013 Krzysztof M. Choromanski, Tony Jebara, Kui Tang

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.

Stochastic Bound Majorization

no code implementations22 Sep 2013 Anna Choromanska, Tony Jebara

Recently a majorization method for optimizing partition functions of log-linear models was proposed alongside a novel quadratic variational upper-bound.

Stochastic Optimization

Semistochastic Quadratic Bound Methods

no code implementations5 Sep 2013 Aleksandr Y. Aravkin, Anna Choromanska, Tony Jebara, Dimitri Kanevsky

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

no code implementations4 Jun 2013 Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang

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

no code implementations NeurIPS 2012 Tony Jebara, Anna Choromanska

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

no code implementations NeurIPS 2011 Blake Shaw, Bert Huang, Tony Jebara

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.

Graph Embedding Metric Learning

Variance Penalizing AdaBoost

no code implementations NeurIPS 2011 Pannagadatta K. Shivaswamy, Tony Jebara

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

no code implementations NeurIPS 2008 Tony Jebara, Pannagadatta K. Shivaswamy

In classification problems, Support Vector Machines maximize the margin of separation between two classes.

General Classification

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