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1 code implementation • 6 Feb 2023 • Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang

However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training.

no code implementations • 2 Feb 2023 • Ruijie Zheng, Xiyao Wang, Huazhe Xu, Furong Huang

To test this hypothesis, we devise two practical robust training mechanisms through computing the adversarial noise and regularizing the value network's spectral norm to directly regularize the Lipschitz condition of the value functions.

no code implementations • 28 Jan 2023 • Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha

Directed Exploration is a crucial challenge in reinforcement learning (RL), especially when rewards are sparse.

Model-based Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 24 Jan 2023 • Yanchao Sun, Shuang Ma, Ratnesh Madaan, Rogerio Bonatti, Furong Huang, Ashish Kapoor

Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining representations without explicit labels.

1 code implementation • 2 Nov 2022 • Kaiwen Yang, Yanchao Sun, Jiahao Su, Fengxiang He, Xinmei Tian, Furong Huang, Tianyi Zhou, DaCheng Tao

In experiments, we show that our method consistently brings non-trivial improvements to the three aforementioned learning tasks from both efficiency and final performance, either or not combined with strong pre-defined augmentations, e. g., on medical images when domain knowledge is unavailable and the existing augmentation techniques perform poorly.

no code implementations • 25 Oct 2022 • Marco Bornstein, Tahseen Rabbani, Evan Wang, Amrit Singh Bedi, Furong Huang

Furthermore, we provide theoretical results for IID and non-IID settings without any bounded-delay assumption for slow clients which is required by other asynchronous decentralized FL algorithms.

1 code implementation • 12 Oct 2022 • Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Furong Huang

Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be particularly vulnerable to adversarial perturbations on input observations.

no code implementations • 26 Sep 2022 • Xiaofeng Xue, Haokun Mao, Qiong Li, Furong Huang

Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new federated learning framework which updates model from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT).

3 code implementations • 19 Aug 2022 • Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein

We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice.

no code implementations • 25 Jul 2022 • Xiyao Wang, Wichayaporn Wongkamjan, Ruonan Jia, Furong Huang

Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning.

1 code implementation • 26 Jun 2022 • Bang An, Zora Che, Mucong Ding, Furong Huang

In many real-world applications, however, such an assumption is often violated as previously trained fair models are often deployed in a different environment, and the fairness of such models has been observed to collapse.

no code implementations • 22 Jun 2022 • Amrit Singh Bedi, Chen Fan, Alec Koppel, Anit Kumar Sahu, Brian M. Sadler, Furong Huang, Dinesh Manocha

In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as a constrained program.

no code implementations • 21 Jun 2022 • Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh, Yongyuan Liang, Soheil Feizi, Sumitra Ganesh, Furong Huang

Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions.

no code implementations • 2 Jun 2022 • Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha

In this work, we propose a novel ${\bf K}$ernelized ${\bf S}$tein Discrepancy-based Posterior Sampling for ${\bf RL}$ algorithm (named $\texttt{KSRL}$) which extends model-based RL based upon posterior sampling (PSRL) in several ways: we (i) relax the need for any smoothness or Gaussian assumptions, allowing for complex mixture models; (ii) ensure it is applicable to large-scale training by incorporating a compression step such that the posterior consists of a \emph{Bayesian coreset} of only statistically significant past state-action pairs; and (iii) develop a novel regret analysis of PSRL based upon integral probability metrics, which, under a smoothness condition on the constructed posterior, can be evaluated in closed form as the kernelized Stein discrepancy (KSD).

1 code implementation • 11 Feb 2022 • Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

Algorithmic extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems.

no code implementations • ICLR 2022 • Yanchao Sun, Ruijie Zheng, Xiyao Wang, Andrew Cohen, Furong Huang

In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramatic changes over time (e. g. increased number of observable features).

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

Based on this notion, we refine the generalization bound for invariant models and characterize the suitability of a set of data transformations by the sample covering number induced by transformations, i. e., the smallest size of its induced sample covers.

1 code implementation • 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 #11 on Node Classification on Reddit

no code implementations • 29 Sep 2021 • Jiahao Su, Wonmin Byeon, Furong Huang

Some of these designs are not exactly orthogonal, while others only consider standard convolutional layers and propose specific classes of their realizations.

no code implementations • ICLR 2022 • Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang

This paper proposes a new algorithm for learning the optimal policies under a novel multi-agent predictive state representation reinforcement learning model.

no code implementations • ICLR 2022 • Xiaoyu Liu, Jiahao Su, Furong Huang

Guided by tensor diagram representations, we formulate a design space where we can analyze the expressive power of the network structure, providing new directions and possibilities for enhanced performance.

no code implementations • 29 Sep 2021 • Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein

We observe that in most cases, we need both a suitable domain generalization algorithm and a strong GNN backbone model to optimize out-of-distribution test performance.

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 • 29 Sep 2021 • Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

Classical machine learning systems perform best when they are trained and tested on the same distribution, and they lack a mechanism to increase model power after training is complete.

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.

1 code implementation • ICLR 2022 • Yanchao Sun, Ruijie Zheng, Yongyuan Liang, Furong Huang

Existing works on adversarial RL either use heuristics-based methods that may not find the strongest adversary, or directly train an RL-based adversary by treating the agent as a part of the environment, which can find the optimal adversary but may become intractable in a large state space.

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.

1 code implementation • 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.

1 code implementation • 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.

Ranked #1 on Video Prediction on KTH (Cond metric)

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.

1 code implementation • 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.

Model-based Reinforcement Learning
reinforcement-learning
**+1**

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.

no code implementations • 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 • 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 • 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 • 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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