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1 code implementation • ICML 2020 • Jiaming Song, Stefano Ermon

Generative adversarial networks (GANs) variants approximately minimize divergences between the model and the data distribution using a discriminator.

no code implementations • NeurIPS 2021 • Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon

We propose a stronger notion of calibration called threshold calibration, which is exactly the condition required to ensure that decision loss is predicted accurately for threshold decisions.

no code implementations • NeurIPS 2021 • Chris Cundy, Aditya Grover, Stefano Ermon

A structural equation model (SEM) is an effective framework to reason over causal relationships represented via a directed acyclic graph (DAG). Recent advances have enabled effective maximum-likelihood point estimation of DAGs from observational data.

1 code implementation • NeurIPS 2021 • Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon

A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods.

no code implementations • NeurIPS 2021 • Andy Shih, Dorsa Sadigh, Stefano Ermon

Probabilistic circuits (PCs) are a family of generative models which allows for the computation of exact likelihoods and marginals of its probability distributions.

1 code implementation • NeurIPS 2021 • Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon

Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire.

no code implementations • 22 Nov 2021 • Kristy Choi, Chenlin Meng, Yang song, Stefano Ermon

We then estimate the instantaneous rate of change of the bridge distributions indexed by time (the "time score") -- a quantity defined analogously to data (Stein) scores -- with a novel time score matching objective.

no code implementations • NeurIPS Workshop Deep_Invers 2021 • Yang song, Liyue Shen, Lei Xing, Stefano Ermon

These measurements are typically synthesized from images using a fixed physical model of the measurement process, which hinders the generalization capability of models to unknown measurement processes.

no code implementations • NeurIPS 2021 • Chenlin Meng, Yang song, Wenzhe Li, Stefano Ermon

By leveraging Tweedie's formula on higher order moments, we generalize denoising score matching to estimate higher order derivatives.

1 code implementation • 8 Nov 2021 • Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David B. Lobell, Stefano Ermon

Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.

no code implementations • NeurIPS 2021 • Lantao Yu, Jiaming Song, Yang song, Stefano Ermon

Energy-based models (EBMs) offer flexible distribution parametrization.

no code implementations • 29 Sep 2021 • Lantao Yu, Yujia Jin, Stefano Ermon

Binary density ratio estimation (DRE), the problem of estimating the ratio $p_1/p_2$ given their empirical samples, provides the foundation for many state-of-the-art machine learning algorithms such as contrastive representation learning and covariate shift adaptation.

no code implementations • 29 Sep 2021 • Fan-Yun Sun, Jonathan Kuck, Hao Tang, Stefano Ermon

Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution.

1 code implementation • 16 Aug 2021 • Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

1 code implementation • 2 Aug 2021 • Chenlin Meng, Yang song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon

We introduce a new image editing and synthesis framework, Stochastic Differential Editing (SDEdit), based on a recent generative model using stochastic differential equations (SDEs).

no code implementations • NeurIPS 2021 • Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon

In this work, we introduce a new notion -- \emph{decision calibration} -- that requires the predicted distribution and true distribution to be ``indistinguishable'' to a set of downstream decision-makers.

no code implementations • 10 Jul 2021 • Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon

Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions, which is essential for understanding physical, social, and team-play systems.

1 code implementation • NeurIPS 2021 • Yusuke Tashiro, Jiaming Song, Yang song, Stefano Ermon

In this paper, we propose Conditional Score-based Diffusion models for Imputation (CSDI), a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data.

1 code implementation • 5 Jul 2021 • Kristy Choi, Madeline Liao, Stefano Ermon

Density ratio estimation serves as an important technique in the unsupervised machine learning toolbox.

no code implementations • NeurIPS 2021 • Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon

In many sequential decision-making problems (e. g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task.

no code implementations • NeurIPS 2021 • Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon

High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others.

3 code implementations • 14 Jun 2021 • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon

High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.

1 code implementation • 12 Jun 2021 • Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon

Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire.

no code implementations • NeurIPS 2021 • Mike Wu, Noah Goodman, Stefano Ermon

In traditional software programs, it is easy to trace program logic from variables back to input, apply assertion statements to block erroneous behavior, and compose programs together.

1 code implementation • 19 Apr 2021 • Willie Neiswanger, Ke Alexander Wang, Stefano Ermon

Given such an $\mathcal{A}$, and a prior distribution over $f$, we refer to the problem of inferring the output of $\mathcal{A}$ using $T$ evaluations as Bayesian Algorithm Execution (BAX).

1 code implementation • ICLR 2021 • Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh

Humans can quickly adapt to new partners in collaborative tasks (e. g. playing basketball), because they understand which fundamental skills of the task (e. g. how to dribble, how to shoot) carry over across new partners.

no code implementations • ICLR 2021 • Chenlin Meng, Jiaming Song, Yang song, Shengjia Zhao, Stefano Ermon

While autoregressive models excel at image compression, their sample quality is often lacking.

no code implementations • ICLR Workshop Neural_Compression 2021 • Abhishek Sinha, Jiaming Song, Stefano Ermon

We illustrate that with one set of representations, the hybrid approach is able to achieve good performance on multiple downstream tasks such as classification, reconstruction, and generation.

1 code implementation • ICLR 2021 • Yilun Xu, Yang song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon

Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling.

no code implementations • 22 Feb 2021 • Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon

Probabilistic classifiers output confidence scores along with their predictions, and these confidence scores must be well-calibrated (i. e. reflect the true probability of an event) to be meaningful and useful for downstream tasks.

no code implementations • 15 Feb 2021 • Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H. -S. Philip Wong, Armin Alaghi

We propose a radically different approach that: (i) employs analog memories to maximize the capacity of each memory cell, and (ii) jointly optimizes model compression and physical storage to maximize memory utility.

2 code implementations • ICLR 2021 • Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon

Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.

Ranked #6 on Image Generation on CIFAR-100

2 code implementations • NeurIPS 2021 • Yang song, Conor Durkan, Iain Murray, Stefano Ermon

Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matching losses.

Ranked #4 on Image Generation on ImageNet 32x32

no code implementations • 1 Jan 2021 • Laëtitia Shao, Yang song, Stefano Ermon

Although deep neural networks are effective on supervised learning tasks, they have been shown to be brittle.

no code implementations • 1 Jan 2021 • Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon

Based on ideas from decision theory, we investigate a new class of discrepancies that are based on the optimal decision loss.

no code implementations • 1 Jan 2021 • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon

High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.

no code implementations • 30 Dec 2020 • Chris Cundy, Stefano Ermon

We consider the task of training a policy that maximizes reward while minimizing disclosure of certain sensitive state variables through the actions.

1 code implementation • 12 Dec 2020 • Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon

A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods.

5 code implementations • ICLR 2021 • Yang song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9. 89 and FID of 2. 20, a competitive likelihood of 2. 99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.

Ranked #3 on Image Generation on CIFAR-10

no code implementations • 20 Nov 2020 • Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon

Finally, we improve standard ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.

1 code implementation • ICCV 2021 • Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon

Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks.

no code implementations • 15 Nov 2020 • Shengjia Zhao, Stefano Ermon

Decision makers often need to rely on imperfect probabilistic forecasts.

no code implementations • NeurIPS 2020 • Chenlin Meng, Lantao Yu, Yang song, Jiaming Song, Stefano Ermon

To increase flexibility, we propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariate log-conditionals (scores), which need not be normalized.

1 code implementation • NeurIPS 2020 • Andy Shih, Stefano Ermon

Inference in discrete graphical models with variational methods is difficult because of the inability to re-parameterize gradients of the Evidence Lower Bound (ELBO).

no code implementations • NeurIPS 2021 • Kuno Kim, Akshat Jindal, Yang song, Jiaming Song, Yanan Sui, Stefano Ermon

We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) using the density as a reward.

no code implementations • NeurIPS Workshop DL-IG 2020 • Berivan Isik, Kristy Choi, Xin Zheng, H.-S. Philip Wong, Stefano Ermon, Tsachy Weissman, Armin Alaghi

Efficient compression and storage of neural network (NN) parameters is critical for resource-constrained, downstream machine learning applications.

5 code implementations • ICLR 2021 • Jiaming Song, Chenlin Meng, Stefano Ermon

Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.

no code implementations • 5 Oct 2020 • Laëtitia Shao, Yang song, Stefano Ermon

From this observation, we develop a detection criteria for samples on which a classifier is likely to fail at test time.

no code implementations • 21 Aug 2020 • Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese

In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy.

2 code implementations • NeurIPS 2020 • Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Re

A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed.

Ranked #6 on Sequential Image Classification on Sequential MNIST

no code implementations • NeurIPS 2020 • Jiaming Song, Stefano Ermon

We demonstrate that the proposed approach is able to lead to better mutual information estimation, gain empirical improvements in unsupervised representation learning, and beat a current state-of-the-art knowledge distillation method over 10 out of 13 tasks.

1 code implementation • NeurIPS 2020 • Tianyu Pang, Kun Xu, Chongxuan Li, Yang song, Stefano Ermon, Jun Zhu

Several machine learning applications involve the optimization of higher-order derivatives (e. g., gradients of gradients) during training, which can be expensive in respect to memory and computation even with automatic differentiation.

no code implementations • NeurIPS 2020 • Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon

Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems.

no code implementations • 29 Jun 2020 • Anusri Pampari, Stefano Ermon

A probabilistic model is said to be calibrated if its predicted probabilities match the corresponding empirical frequencies.

1 code implementation • 23 Jun 2020 • Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon

The use of past experiences to accelerate temporal difference (TD) learning of value functions, or experience replay, is a key component in deep reinforcement learning.

1 code implementation • 18 Jun 2020 • Shengjia Zhao, Christopher Yeh, Stefano Ermon

We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samples.

no code implementations • ICML 2020 • Shengjia Zhao, Tengyu Ma, Stefano Ermon

We show that calibration for individual samples is possible in the regression setup if the predictions are randomized, i. e. outputting randomized credible intervals.

6 code implementations • NeurIPS 2020 • Yang Song, Stefano Ermon

Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization.

Ranked #26 on Image Generation on CIFAR-10

1 code implementation • 15 Jun 2020 • Jihyeon Lee, Dylan Grosz, Burak Uzkent, Sicheng Zeng, Marshall Burke, David Lobell, Stefano Ermon

Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world.

no code implementations • 7 Jun 2020 • Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon

The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring.

1 code implementation • ICLR 2021 • Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar Carlsson, Stefano Ermon

Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models.

2 code implementations • NeurIPS 2020 • Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma

We also characterize the trade-off between the gain and risk of leaving the support of the batch data.

no code implementations • 11 Apr 2020 • Han Lin Aung, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon

Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation to collect this valuable data.

1 code implementation • NeurIPS 2020 • Yusuke Tashiro, Yang song, Stefano Ermon

Adversarial attacks often involve random perturbations of the inputs drawn from uniform or Gaussian distributions, e. g., to initialize optimization-based white-box attacks or generate update directions in black-box attacks.

1 code implementation • ICML 2020 • Lantao Yu, Yang song, Jiaming Song, Stefano Ermon

Experimental results demonstrate the superiority of f-EBM over contrastive divergence, as well as the benefits of training EBMs using f-divergences other than KL.

3 code implementations • 4 Mar 2020 • Chenlin Meng, Yang song, Jiaming Song, Stefano Ermon

Iterative Gaussianization is a fixed-point iteration procedure that can transform any continuous random vector into a Gaussian one.

1 code implementation • 2 Mar 2020 • Chenhao Niu, Yang song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon

In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (a. k. a., the score function).

1 code implementation • ICML 2020 • Rui Shu, Tung Nguyen, Yin-Lam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui

High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.

2 code implementations • CVPR 2020 • Burak Uzkent, Stefano Ermon

While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some applications, e. g. remote sensing, they can be much more expensive to acquire.

no code implementations • ICLR 2020 • Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon

We propose a new framework for reasoning about information in complex systems.

1 code implementation • 10 Feb 2020 • Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon

Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning.

no code implementations • 5 Feb 2020 • Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon

Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources.

1 code implementation • 14 Dec 2019 • Vishnu Sarukkai, Anirudh Jain, Burak Uzkent, Stefano Ermon

In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.

2 code implementations • 9 Dec 2019 • Burak Uzkent, Christopher Yeh, Stefano Ermon

Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images.

1 code implementation • NeurIPS 2019 • Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei W. Koh, Stefano Ermon

Learning representations that accurately capture long-range dependencies in sequential inputs --- including text, audio, and genomic data --- is a key problem in deep learning.

Ranked #2 on Audio Super-Resolution on VCTK Multi-Speaker

no code implementations • 30 Nov 2019 • Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell Gordon, Stefano Ermon

Generative models have made immense progress in recent years, particularly in their ability to generate high quality images.

1 code implementation • NeurIPS 2019 • Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon

Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics.

1 code implementation • ICML 2020 • Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon

Real-world datasets are often biased with respect to key demographic factors such as race and gender.

1 code implementation • 22 Oct 2019 • Jiaming Song, Stefano Ermon

Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions.

1 code implementation • ICLR 2020 • Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole

Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning.

no code implementations • 21 Oct 2019 • Jiaming Song, Yang song, Stefano Ermon

Based on this insight, we propose to exploit in-batch dependencies for OoD detection.

1 code implementation • ICLR 2020 • Jiaming Song, Stefano Ermon

Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables.

1 code implementation • ICML 2020 • Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch.

no code implementations • 25 Sep 2019 • Shengjia Zhao, Yang song, Stefano Ermon

Our defense draws inspiration from differential privacy, and is based on intentionally adding noise to the classifier's outputs to limit the attacker's knowledge about the parameters.

no code implementations • 25 Sep 2019 • Kun Ho Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

Informally, CDIL is the process of learning how to perform a task optimally, given demonstrations of the task in a distinct domain.

1 code implementation • NeurIPS 2019 • Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon

Critically, our model can infer rewards for new, structurally-similar tasks from a single demonstration.

Ranked #1 on MuJoCo Games on Point Maze

1 code implementation • 14 Sep 2019 • Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei Koh, Stefano Ermon

Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning.

Ranked #2 on Audio Super-Resolution on Voice Bank corpus (VCTK)

1 code implementation • 30 Jul 2019 • Lantao Yu, Jiaming Song, Stefano Ermon

Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification.

1 code implementation • NeurIPS 2019 • Yang Song, Chenlin Meng, Stefano Ermon

To demonstrate their flexibility, we show that our invertible neural networks are competitive with ResNets on MNIST and CIFAR-10 classification.

Ranked #4 on Image Generation on MNIST

8 code implementations • NeurIPS 2019 • Yang Song, Stefano Ermon

We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching.

Ranked #56 on Image Generation on CIFAR-10

2 code implementations • NeurIPS 2019 • Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon

A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions.

1 code implementation • 19 Jun 2019 • Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon

Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning.

no code implementations • ICLR 2019 • Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon

Partial differential equations (PDEs) are widely used across the physical and computational sciences.

1 code implementation • ICLR Workshop DeepGenStruct 2019 • Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon

Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain.

5 code implementations • 17 May 2019 • Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon

However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions.

3 code implementations • 7 May 2019 • Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon

Despite recent progress in computer vision, finegrained interpretation of satellite images remains challenging because of a lack of labeled training data.

no code implementations • 5 May 2019 • Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, David Lobell, Marshall Burke, Stefano Ermon

Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries.

no code implementations • 4 May 2019 • Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon

Millions of people worldwide are absent from their country's census.

no code implementations • 20 Apr 2019 • Xiao Chen, Thomas Navidi, Stefano Ermon, Ram Rajagopal

Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data.

no code implementations • ICLR Workshop DeepGenStruct 2019 • Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon

A standard technique to correct this bias is by importance weighting samples from the model by the likelihood ratio under the model and true distributions.

1 code implementation • ICLR 2019 • Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon

Sorting input objects is an important step in many machine learning pipelines.

no code implementations • 27 Feb 2019 • Rui Shu, Hung H. Bui, Jay Whang, Stefano Ermon

The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up to large datasets.

no code implementations • 13 Feb 2019 • Anthony Perez, Swetava Ganguli, Stefano Ermon, George Azzari, Marshall Burke, David Lobell

Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world.

1 code implementation • 5 Feb 2019 • Mike Wu, Kristy Choi, Noah Goodman, Stefano Ermon

Despite the recent success in probabilistic modeling and their applications, generative models trained using traditional inference techniques struggle to adapt to new distributions, even when the target distribution may be closely related to the ones seen during training.

1 code implementation • 29 Jan 2019 • Sang Michael Xie, Stefano Ermon

Many machine learning tasks require sampling a subset of items from a collection based on a parameterized distribution.

1 code implementation • 23 Jan 2019 • Chi-Sing Ho, Neal Jean, Catherine A. Hogan, Lena Blackmon, Stefanie S. Jeffrey, Mark Holodniy, Niaz Banaei, Amr A. E. Saleh, Stefano Ermon, Jennifer Dionne

By amassing the largest known dataset of bacterial Raman spectra, we are able to apply state-of-the-art deep learning approaches to identify 30 of the most common bacterial pathogens from noisy Raman spectra, achieving antibiotic treatment identification accuracies of 99. 0$\pm$0. 1%.

no code implementations • 26 Dec 2018 • Aditya Grover, Stefano Ermon

We treat the low-dimensional projections as noisy latent representations of an autoencoder and directly learn both the acquisition (i. e., encoding) and amortized recovery (i. e., decoding) procedures.

Dimensionality Reduction Unsupervised Representation Learning

3 code implementations • 11 Dec 2018 • Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon

Learning data representations that are transferable and are fair with respect to certain protected attributes is crucial to reducing unfair decisions while preserving the utility of the data.

1 code implementation • NeurIPS 2018 • Aditya Grover, Tudor Achim, Stefano Ermon

Several algorithms for solving constraint satisfaction problems are based on survey propagation, a variational inference scheme used to obtain approximate marginal probability estimates for variable assignments.

1 code implementation • 19 Nov 2018 • Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon

For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes.

2 code implementations • NeurIPS 2018 • Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon

In high dimensional settings, density estimation algorithms rely crucially on their inductive bias.

1 code implementation • 5 Oct 2018 • Mike Wu, Noah Goodman, Stefano Ermon

Stochastic optimization techniques are standard in variational inference algorithms.

no code implementations • 27 Sep 2018 • Kristy Choi, Kedar Tatwawadi, Tsachy Weissman, Stefano Ermon

For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes.

no code implementations • 19 Sep 2018 • Evan Sheehan, Burak Uzkent, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon

Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data.

1 code implementation • NeurIPS 2018 • Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon

Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal.

no code implementations • 24 Jul 2018 • Rishi Sharma, Shane Barratt, Stefano Ermon, Vijay Pande

We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation.

2 code implementations • ICML 2018 • Manik Dhar, Aditya Grover, Stefano Ermon

In compressed sensing, a small number of linear measurements can be used to reconstruct an unknown signal.

1 code implementation • ICML 2018 • Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon

Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems.

2 code implementations • 18 Jun 2018 • Shengjia Zhao, Jiaming Song, Stefano Ermon

A large number of objectives have been proposed to train latent variable generative models.

no code implementations • 3 Jun 2018 • Barak Oshri, Annie Hu, Peter Adelson, Xiao Chen, Pascaline Dupas, Jeremy Weinstein, Marshall Burke, David Lobell, Stefano Ermon

Our best models predict infrastructure quality with AUROC scores of 0. 881 on Electricity, 0. 862 on Sewerage, 0. 739 on Piped Water, and 0. 786 on Roads using Landsat 8.

1 code implementation • 27 May 2018 • Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon

Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws.

1 code implementation • NeurIPS 2018 • Neal Jean, Sang Michael Xie, Stefano Ermon

Large amounts of labeled data are typically required to train deep learning models.

no code implementations • NeurIPS 2018 • Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon

In this paper, we leverage the fact that VAEs rely on amortized inference and propose techniques for amortized inference regularization (AIR) that control the smoothness of the inference model.

1 code implementation • NeurIPS 2018 • Yang Song, Rui Shu, Nate Kushman, Stefano Ermon

Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier.

3 code implementations • 8 May 2018 • Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, Stefano Ermon

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks.

no code implementations • 5 Apr 2018 • Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon

Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates.

1 code implementation • CVPR 2018 • Lijie Fan, Wenbing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang

Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks.

Ranked #36 on Action Recognition on UCF101

no code implementations • 29 Mar 2018 • Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicholas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon

We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback.

1 code implementation • 28 Mar 2018 • Aditya Grover, Aaron Zweig, Stefano Ermon

Graphs are a fundamental abstraction for modeling relational data.

Ranked #43 on Node Classification on Pubmed

2 code implementations • ICML 2018 • Yang Song, Jiaming Song, Stefano Ermon

An appealing property of the natural gradient is that it is invariant to arbitrary differentiable reparameterizations of the model.

2 code implementations • ICLR 2018 • Rui Shu, Hung H. Bui, Hirokazu Narui, Stefano Ermon

Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.

1 code implementation • 27 Jan 2018 • Jonathan Kuck, Ashish Sabharwal, Stefano Ermon

Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size.

no code implementations • ICLR 2018 • Daniel Levy, Danlu Chan, Stefano Ermon

In this work, we present LSH Softmax, a method to perform sub-linear learning and inference of the softmax layer in the deep learning setting.

no code implementations • ICLR 2018 • Shengjia Zhao, Jiaming Song, Stefano Ermon

A variety of learning objectives have been recently proposed for training generative models.

no code implementations • ICLR 2018 • Volodymyr Kuleshov, Shantanu Thakoor, Tingfung Lau, Stefano Ermon

Modern machine learning algorithms are often susceptible to adversarial examples — maliciously crafted inputs that are undetectable by humans but that fool the algorithm into producing undesirable behavior.

no code implementations • 10 Dec 2017 • Stephan Eismann, Stefan Bartzsch, Stefano Ermon

Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically.

no code implementations • 21 Nov 2017 • Daniel Levy, Stefano Ermon

Our method is applicable to both discrete and continuous action spaces, when competing pathwise methods are limited to the latter.

no code implementations • 15 Nov 2017 • Huaiyang Zhong, Xiaocheng Li, David Lobell, Stefano Ermon, Margaret L. Brandeau

Eradicating hunger and malnutrition is a key development goal of the 21st century.

no code implementations • 10 Nov 2017 • Anthony Perez, Christopher Yeh, George Azzari, Marshall Burke, David Lobell, Stefano Ermon

Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce.

no code implementations • NeurIPS 2017 • Volodymyr Kuleshov, Stefano Ermon

Many problems in machine learning are naturally expressed in the language of undirected graphical models.

1 code implementation • ICLR 2018 • Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman

Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models.

1 code implementation • 23 Aug 2017 • Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, Shahrokh Valaee

This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers.

4 code implementations • 2 Aug 2017 • Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon

We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks.

Ranked #3 on Audio Super-Resolution on Voice Bank corpus (VCTK)

no code implementations • ICML 2017 • Shengjia Zhao, Jiaming Song, Stefano Ermon

In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn.

no code implementations • 11 Jul 2017 • Stephen Mussmann, Daniel Levy, Stefano Ermon

Inference in log-linear models scales linearly in the size of output space in the worst-case.

3 code implementations • NeurIPS 2017 • Jiaming Song, Shengjia Zhao, Stefano Ermon

We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties.

6 code implementations • 7 Jun 2017 • Shengjia Zhao, Jiaming Song, Stefano Ermon

A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models.

2 code implementations • 24 May 2017 • Aditya Grover, Manik Dhar, Stefano Ermon

Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood.

4 code implementations • NeurIPS 2017 • Yunzhu Li, Jiaming Song, Stefano Ermon

The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal.

no code implementations • 7 Mar 2017 • Jiaming Song, Russell Stewart, Shengjia Zhao, Stefano Ermon

Advances in neural network based classifiers have transformed automatic feature learning from a pipe dream of stronger AI to a routine and expected property of practical systems.

2 code implementations • 28 Feb 2017 • Shengjia Zhao, Jiaming Song, Stefano Ermon

We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound.

3 code implementations • 27 Feb 2017 • Shengjia Zhao, Jiaming Song, Stefano Ermon

In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn.

1 code implementation • 27 Feb 2017 • Aditya Grover, Stefano Ermon

We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes.

1 code implementation • AAAI 2017 2017 • Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon

Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.

no code implementations • NeurIPS 2016 • Aditya Grover, Stefano Ermon

We provide a new approach for learning latent variable models based on optimizing our new bounds on the log-likelihood.

no code implementations • NeurIPS 2016 • Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon

A key challenge in sequential decision problems is to determine how many samples are needed for an agent to make reliable decisions with good probabilistic guarantees.

no code implementations • NeurIPS 2016 • Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman

Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them.

no code implementations • 18 Sep 2016 • Russell Stewart, Stefano Ermon

In many machine learning applications, labeled data is scarce and obtaining more labels is expensive.

no code implementations • 13 Jul 2016 • Volodymyr Kuleshov, Stefano Ermon

Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems.

14 code implementations • NeurIPS 2016 • Jonathan Ho, Stefano Ermon

Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal.

no code implementations • 26 May 2016 • Jonathan Ho, Jayesh K. Gupta, Stefano Ermon

In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations.

no code implementations • 5 Oct 2015 • Lun-Kai Hsu, Tudor Achim, Stefano Ermon

We show that information projections can be combined with random projections to obtain provable guarantees on the quality of the approximation obtained, regardless of the complexity of the original model.

1 code implementation • 1 Oct 2015 • Michael Xie, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon

We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction.

no code implementations • 17 Aug 2015 • Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman

The ability to represent complex high dimensional probability distributions in a compact form is one of the key insights in the field of graphical models.

no code implementations • 27 Nov 2014 • Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla Gomes, Bart Selman, Robert B. van Dover

Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining.

no code implementations • NeurIPS 2013 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model.

no code implementations • 26 Sep 2013 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

Many probabilistic inference tasks involve summations over exponentially large sets.

no code implementations • NeurIPS 2012 • Stefano Ermon, Ashish Sabharwal, Bart Selman, Carla P. Gomes

Given a probabilistic graphical model, its density of states is a function that, for any likelihood value, gives the number of configurations with that probability.

no code implementations • NeurIPS 2011 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function.

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