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1 code implementation • NeurIPS 2021 • Sicheng Zhu, Bang An, Furong Huang

In experiments on multiple datasets, we evaluate sample covering numbers for some commonly used transformations and show that the smaller sample covering number for a set of transformations (e. g., the 3D-view transformation) indicates a smaller gap between the test and training error for invariant models, which verifies our propositions.

no code implementations • NeurIPS 2021 • Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P Dickerson, Furong Huang, Tom Goldstein

Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.

Ranked #7 on Node Classification on Reddit

no code implementations • 29 Sep 2021 • Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam H Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein

Data poisoning and backdoor attacks manipulate training data to induce security breaches in a victim model.

no code implementations • 17 Sep 2021 • Tahseen Rabbani, Brandon Feng, Yifan Yang, Arjun Rajkumar, Amitabh Varshney, Furong Huang

A popular application of federated learning is using many clients to train a deep neural network, the parameters of which are maintained on a central server.

no code implementations • 20 Aug 2021 • Tahseen Rabbani, Apollo Jain, Arjun Rajkumar, Furong Huang

The power method is a classical algorithm with broad applications in machine learning tasks, including streaming PCA, spectral clustering, and low-rank matrix approximation.

1 code implementation • 13 Aug 2021 • Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

We describe new datasets for studying generalization from easy to hard examples.

1 code implementation • 3 Aug 2021 • Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein

We find that samples which cause similar parameters to malfunction are semantically similar.

no code implementations • 1 Aug 2021 • Huimin Zeng, Jiahao Su, Furong Huang

Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against $\ell_2$-norm adversarial perturbations.

no code implementations • 16 Jun 2021 • Jiahao Su, Wonmin Byeon, Furong Huang

To address this problem, we propose a theoretical framework for orthogonal convolutional layers, which establishes the equivalence between various orthogonal convolutional layers in the spatial domain and the paraunitary systems in the spectral domain.

no code implementations • 9 Jun 2021 • Yanchao Sun, Ruijie Zheng, Yongyuan Liang, Furong Huang

Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents.

1 code implementation • NeurIPS 2021 • Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein

In this work, we show that recurrent networks trained to solve simple problems with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference.

no code implementations • 24 May 2021 • Hyekang Joo, Calvin Bao, Ishan Sen, Furong Huang, Leilani Battle

Moreover, an analysis on the variance in a selected performance metric in the context of the model hyperparameters shows the impact that certain hyperparameters have on the performance metric.

no code implementations • 7 Mar 2021 • Chen Chen, Kezhi Kong, Peihong Yu, Juan Luque, Tom Goldstein, Furong Huang

Randomized smoothing (RS) is an effective and scalable technique for constructing neural network classifiers that are certifiably robust to adversarial perturbations.

no code implementations • 2 Mar 2021 • Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein

The InstaHide method has recently been proposed as an alternative to DP training that leverages supposed privacy properties of the mixup augmentation, although without rigorous guarantees.

no code implementations • 24 Oct 2020 • Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang

Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks.

no code implementations • ICLR 2021 • Yanchao Sun, Da Huo, Furong Huang

Poisoning attacks on Reinforcement Learning (RL) systems could take advantage of RL algorithm's vulnerabilities and cause failure of the learning.

1 code implementation • 21 Jun 2020 • Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein

Adaptive gradient methods such as RMSProp and Adam use exponential moving estimate of the squared gradient to compute adaptive step sizes, achieving better convergence than SGD in face of noisy objectives.

no code implementations • 17 Jun 2020 • Roozbeh Yousefzadeh, Furong Huang

We show that each image can be written as the summation of a finite number of rank-1 patterns in the wavelet space, providing a low rank approximation that captures the structures and patterns essential for learning.

no code implementations • 22 Feb 2020 • Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein

Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical robustness.

2 code implementations • NeurIPS 2020 • Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Animashree Anandkumar

Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation. However, existing methods still perform poorly on challenging video tasks such as long-term forecasting.

no code implementations • 16 Feb 2020 • Yanchao Sun, Xiangyu Yin, Furong Huang

Transferring knowledge among various environments is important to efficiently learn multiple tasks online.

no code implementations • NeurIPS 2020 • Jiahao Su, Shiqi Wang, Furong Huang

In this work, we propose to replace any traditional convolutional layer with an autoregressive moving-average (ARMA) layer, a novel module with an adjustable receptive field controlled by the learnable autoregressive coefficients.

no code implementations • 14 Jan 2020 • Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang

Recently proposed complexity measures have provided insights to understanding the generalizability in neural networks from perspectives of PAC-Bayes, robustness, overparametrization, compression and so on.

1 code implementation • 21 Dec 2019 • Yanchao Sun, Furong Huang

We propose a new model-based method called Greedy Inference Model (GIM) that infers the unknown dynamics from known dynamics based on the internal spectral properties of the environment.

no code implementations • ICLR 2020 • Jiahao Su, Milan Cvitkovic, Furong Huang

Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important.

no code implementations • 25 Oct 2019 • Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein

Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization.

no code implementations • 25 Sep 2019 • Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein

Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical (PGD) robustness.

no code implementations • 25 Sep 2019 • Jiahao Su, Wonmin Byeon, Furong Huang, Jan Kautz, Animashree Anandkumar

Long-term video prediction is highly challenging since it entails simultaneously capturing spatial and temporal information across a long range of image frames. Standard recurrent models are ineffective since they are prone to error propagation and cannot effectively capture higher-order correlations.

2 code implementations • NeurIPS Workshop ICBINB 2020 • W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein

The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.

1 code implementation • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

Machine learning (ML) techniques are enjoying rapidly increasing adoption.

no code implementations • 25 May 2018 • Furong Huang, Jialin Li, Xuchen You

We propose a Slicing Initialized Alternating Subspace Iteration (s-ASI) method that is guaranteed to recover top $r$ components ($\epsilon$-close) simultaneously for (a)symmetric tensors almost surely under the noiseless case (with high probability for a bounded noise) using $O(\log(\log \frac{1}{\epsilon}))$ steps of tensor subspace iterations.

no code implementations • 25 May 2018 • Jiahao Su, Jingling Li, Bobby Bhattacharjee, Furong Huang

We propose tensorial neural networks (TNNs), a generalization of existing neural networks by extending tensor operations on low order operands to those on high order ones.

no code implementations • ICML 2020 • Christopher DeCarolis, Mukul Ram, Seyed A. Esmaeili, Yu-Xiang Wang, Furong Huang

Overall, by combining the sensitivity and utility characterization, we obtain an end-to-end differentially private spectral algorithm for LDA and identify the corresponding configuration that outperforms others in any specific regime.

no code implementations • ICML 2018 • Furong Huang, Jordan Ash, John Langford, Robert Schapire

We prove that the training error decays exponentially with the depth $T$ if the \emph{weak module classifiers} that we train perform slightly better than some weak baseline.

1 code implementation • 10 Dec 2016 • Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein

We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks.

no code implementations • 20 Sep 2016 • Anthony Gitter, Furong Huang, Ragupathyraj Valluvan, Ernest Fraenkel, Animashree Anandkumar

We use a latent tree graphical model to analyze gene expression without relying on transcription factor expression as a proxy for regulator activity.

no code implementations • 10 Jun 2016 • Furong Huang, Animashree Anandkumar

More importantly, it is challenging for pre-trained models to obtain word-sequence embeddings that are universally good for all downstream tasks or for any new datasets.

no code implementations • 10 Jun 2016 • Furong Huang

This thesis presents theoretical results on convergence to globally optimal solution of tensor decomposition using the stochastic gradient descent, despite non-convexity of the objective.

no code implementations • 4 Feb 2016 • Furong Huang, Animashree Anandkumar, Christian Borgs, Jennifer Chayes, Ernest Fraenkel, Michael Hawrylycz, Ed Lein, Alessandro Ingrosso, Srinivas Turaga

Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles.

no code implementations • 10 Jun 2015 • Furong Huang, Animashree Anandkumar

Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning.

1 code implementation • 6 Mar 2015 • Rong Ge, Furong Huang, Chi Jin, Yang Yuan

To the best of our knowledge this is the first work that gives global convergence guarantees for stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points.

no code implementations • 18 Jun 2014 • Furong Huang, Niranjan U. N., Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar

We present an integrated approach for structure and parameter estimation in latent tree graphical models.

1 code implementation • 3 Sep 2013 • Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Animashree Anandkumar

We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles.

no code implementations • NeurIPS 2012 • Anima Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade

We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable is hidden and each mixture component can have a potentially different Markov graph structure and parameters over the observed variables.

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