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1 code implementation • ICML 2020 • Zoltán Milacski, Barnabás Póczos, Andras Lorincz

Deep Neural Networks (DNNs) achieve the state-of-the-art results on a wide range of image processing tasks, however, the majority of such solutions are problem-specific, like most AI algorithms.

no code implementations • 8 Jun 2021 • Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos

However, in machine learning, models are most often trained to solve the target tasks directly. Inspired by human learning, we propose a novel curriculum learning approach which decomposes challenging tasks into sequences of easier intermediate goals that are used to pre-train a model before tackling the target task.

1 code implementation • 16 Apr 2021 • George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos

Finally, aside from our analysis we also release Re-TACRED, a new completely re-annotated version of the TACRED dataset that can be used to perform reliable evaluation of relation extraction models.

2 code implementations • NAACL 2021 • Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency

Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e. g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence.

1 code implementation • 9 Dec 2020 • George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos

Relation extraction (RE) aims to predict a relation between a subject and an object in a sentence, while knowledge graph link prediction (KGLP) aims to predict a set of objects, O, given a subject and a relation from a knowledge graph.

no code implementations • EMNLP 2020 • ZiRui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime Carbonell

State-of-the-art lifelong language learning methods store past examples in episodic memory and replay them at both training and inference time.

1 code implementation • ICLR 2020 • Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos

Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification.

1 code implementation • 3 Apr 2020 • George Stoica, * Otilia Stretcu, * Emmanouil Antonios Platanios, * Tom M. Mitchell, Barnabás Póczos

More specifically, we treat relations as the context in which source entities are processed to produce predictions, by using relation embeddings to generate the parameters of a model operating over source entity embeddings.

Ranked #2 on Link Prediction on NELL-995

no code implementations • 27 Nov 2019 • Joel Ruben Antony Moniz, Eunsu Kang, Barnabás Póczos

In this work, we aim to propose a set of techniques to improve the controllability and aesthetic appeal when DeepDream, which uses a pre-trained neural network to modify images by hallucinating objects into them, is applied to videos.

1 code implementation • NeurIPS 2019 • Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu

While graph kernels (GKs) are easy to train and enjoy provable theoretical guarantees, their practical performances are limited by their expressive power, as the kernel function often depends on hand-crafted combinatorial features of graphs.

no code implementations • CVPR 2019 • Chun-Liang Li, Tomas Simon, Jason Saragih, Barnabás Póczos, Yaser Sheikh

As input, we take a sequence of point clouds to be registered as well as an artist-rigged mesh, i. e. a template mesh equipped with a linear-blend skinning (LBS) deformation space parameterized by a skeleton hierarchy.

no code implementations • 26 Feb 2019 • Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos

While learning the kernel in a data driven way has been investigated, in this paper we explore learning the spectral distribution of kernel via implicit generative models parametrized by deep neural networks.

no code implementations • NeurIPS 2019 • Ananya Uppal, Shashank Singh, Barnabás Póczos

Thus, we show how our results imply bounds on the statistical error of a GAN, showing, for example, that GANs can strictly outperform the best linear estimator.

2 code implementations • ICLR 2019 • Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos

Detecting the emergence of abrupt property changes in time series is a challenging problem.

no code implementations • CVPR 2019 • Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime Carbonell

When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task.

1 code implementation • 15 Nov 2018 • Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos

We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory.

no code implementations • 30 May 2018 • Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos

We also study a notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret.

no code implementations • 24 May 2018 • Yotam Hechtlinger, Barnabás Póczos, Larry Wasserman

Our construction is based on $p(x|y)$ rather than $p(y|x)$ which results in a classifier that is very cautious: it outputs the null set --- meaning "I don't know" --- when the object does not resemble the training examples.

no code implementations • NeurIPS 2018 • Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos

We study minimax convergence rates of nonparametric density estimation under a large class of loss functions called "adversarial losses", which, besides classical $\mathcal{L}^p$ losses, includes maximum mean discrepancy (MMD), Wasserstein distance, and total variation distance.

no code implementations • 30 Mar 2018 • Shashank Singh, Bharath K. Sriperumbudur, Barnabás Póczos

We study estimation of (semi-)inner products between two nonparametric probability distributions, given IID samples from each distribution.

no code implementations • 24 Feb 2018 • Shashank Singh, Barnabás Póczos

The Wasserstein metric is an important measure of distance between probability distributions, with applications in machine learning, statistics, probability theory, and data analysis.

no code implementations • ICML 2018 • Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider

Further, through a comprehensive study over both real world and synthetic data, we show for that jointly leveraging transformations of variables and autoregressive conditional models, results in a considerable improvement in performance.

1 code implementation • 29 Aug 2017 • Shashank Singh, Barnabás Póczos, Jian Ma

Sparse dictionary learning (SDL) has become a popular method for adaptively identifying parsimonious representations of a dataset, a fundamental problem in machine learning and signal processing.

2 code implementations • NeurIPS 2017 • Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos

In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN.

no code implementations • 3 Feb 2017 • Kirthevasan Kandasamy, Jeff Schneider, Barnabás Póczos

In this paper, we study active posterior estimation in a Bayesian setting when the likelihood is expensive to evaluate.

no code implementations • NeurIPS 2016 • Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabás Póczos

We study a variant of the classical stochastic $K$-armed bandit where observing the outcome of each arm is expensive, but cheap approximations to this outcome are available.

no code implementations • NeurIPS 2016 • Shashank Singh, Barnabás Póczos

We provide finite-sample analysis of a general framework for using k-nearest neighbor statistics to estimate functionals of a nonparametric continuous probability density, including entropies and divergences.

1 code implementation • NeurIPS 2016 • Shashank Singh, Simon S. Du, Barnabás Póczos

Sobolev quantities (norms, inner products, and distances) of probability density functions are important in the theory of nonparametric statistics, but have rarely been used in practice, partly due to a lack of practical estimators.

no code implementations • 28 Mar 2016 • Shashank Singh, Barnabás Póczos

Estimating divergences in a consistent way is of great importance in many machine learning tasks.

no code implementations • 28 Mar 2016 • Shashank Singh, Barnabás Póczos

Estimating entropy and mutual information consistently is important for many machine learning applications.

no code implementations • 13 Nov 2015 • Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff Schneider

The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision.

no code implementations • 24 Sep 2015 • Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff Schneider

This work develops the first random features for pdfs whose dot product approximates kernels using these non-Euclidean metrics, allowing estimators using such kernels to scale to large datasets by working in a primal space, without computing large Gram matrices.

no code implementations • NeurIPS 2015 • Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alex Smola

We demonstrate the empirical performance of our method through a concrete realization of asynchronous SVRG.

no code implementations • 9 Jun 2014 • Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry Wasserman

This paper is about two related decision theoretic problems, nonparametric two-sample testing and independence testing.

no code implementations • 1 Feb 2012 • Danica J. Sutherland, Liang Xiong, Barnabás Póczos, Jeff Schneider

Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest.

no code implementations • NeurIPS 2011 • Liang Xiong, Barnabás Póczos, Jeff G. Schneider

We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.

no code implementations • NeurIPS 2010 • Dávid Pál, Barnabás Póczos, Csaba Szepesvári

We present simple and computationally efficient nonparametric estimators of R\'enyi entropy and mutual information based on an i. i. d.

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