Publications (59)
Distribution-Free Distribution Regression
Barnabas Poczos, Alessandro Rinaldo, Aarti Singh +1
Equivariance Through Parameter-Sharing
Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos +2
A First Look at creating mock catalogs with machine learning techniques
Xiaoying Xu, Shirley Ho, Hy Trac +3
Fast Function to Function Regression
Junier Oliva, Willie Neiswanger, Barnabas Poczos +2
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos +1
Kalman-filtering using local interactions
Barnabas Poczos, Andras Lorincz
A Deep Reinforcement Learning Approach for Global Routing
Haiguang Liao, Wentai Zhang, Xuliang Dong +3
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Simon S. Du, Jason D. Lee, Yuandong Tian +2
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives
Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos +2
Independent Process Analysis without A Priori Dimensional Information
Barnabas Poczos, Zoltan Szabo, Melinda Kiszlinger +1
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing
Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos +2
Collaborative Filtering via Group-Structured Dictionary Learning
Zoltan Szabo, Barnabas Poczos, Andras Lorincz
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language
Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos +2
Undercomplete Blind Subspace Deconvolution via Linear Prediction
Zoltan Szabo, Barnabas Poczos, Andras Lorincz
Undercomplete Blind Subspace Deconvolution
Zoltan Szabo, Barnabas Poczos, Andras Lorincz
The Statistical Recurrent Unit
Junier B. Oliva, Barnabas Poczos, Jeff Schneider
Two-stage Sampled Learning Theory on Distributions
Zoltan Szabo, Arthur Gretton, Barnabas Poczos +1
Stochastic Neural Networks with Monotonic Activation Functions
Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider +2
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider +2
High Dimensional Bayesian Optimisation and Bandits via Additive Models
Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos
Separation Theorem for K-Independent Subspace Analysis with Sufficient Conditions
Zoltan Szabo, Barnabas Poczos, Andras Lorincz
Data-driven Random Fourier Features using Stein Effect
Wei-Cheng Chang, Chun-Liang Li, Yiming Yang +1
On Estimating $L_2^2$ Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos +1
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos +2
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions
Barnabas Poczos, Liang Xiong, Jeff Schneider
Boolean Matrix Factorization and Noisy Completion via Message Passing
Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner
A Generic Approach for Escaping Saddle points
Sashank J Reddi, Manzil Zaheer, Suvrit Sra +4
Deep Learning with Sets and Point Clouds
Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM
Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos
Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau +4
Multi-fidelity Gaussian Process Bandit Optimisation
Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva +2
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh +3
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images
Siamak Ravanbakhsh, Francois Lanusse, Rachel Mandelbaum +2
Fast Distribution To Real Regression
Junier B. Oliva, Willie Neiswanger, Barnabas Poczos +2
Fast Incremental Method for Nonconvex Optimization
Sashank J. Reddi, Suvrit Sra, Barnabas Poczos +1
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider +1
Separation Theorem for Independent Subspace Analysis with Sufficient Conditions
Zoltan Szabo, Barnabas Poczos, Andras Lorincz
Stochastic Variance Reduction for Nonconvex Optimization
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra +2
FuSSO: Functional Shrinkage and Selection Operator
Junier B. Oliva, Barnabas Poczos, Timothy Verstynen +4
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang +3
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider +1
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh +3
CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
Francois Lanusse, Quanbin Ma, Nan Li +5
Hypothesis Transfer Learning via Transformation Functions
Simon Shaolei Du, Jayanth Koushik, Aarti Singh +1
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization
Sashank J. Reddi, Suvrit Sra, Barnabas Poczos +1
D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks
Barnabas Poczos, Andras Lorincz
Bayesian Nonparametric Kernel-Learning
Junier Oliva, Avinava Dubey, Andrew G. Wilson +3
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos +1
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
Yifan Wu, Barnabas Poczos, Aarti Singh
Recurrent Estimation of Distributions
Junier B. Oliva, Kumar Avinava Dubey, Barnabas Poczos +2
Hallucinating Point Cloud into 3D Sculptural Object
Chun-Liang Li, Eunsu Kang, Songwei Ge +4
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Simon S. Du, Chi Jin, Jason D. Lee +3
Learning Theory for Distribution Regression
Zoltan Szabo, Bharath Sriperumbudur, Barnabas Poczos +1
Point Cloud GAN
Chun-Liang Li, Manzil Zaheer, Yang Zhang +2
An Analysis of Active Learning With Uniform Feature Noise
Aaditya Ramdas, Barnabas Poczos, Aarti Singh +1
Competence-based Curriculum Learning for Neural Machine Translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig +2
Stochastic Frank-Wolfe Methods for Nonconvex Optimization
Sashank J. Reddi, Suvrit Sra, Barnabas Poczos +1
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis
Hai Pham, Thomas Manzini, Paul Pu Liang +1