Search Results for author: Stefano Ermon

Found 169 papers, 89 papers with code

Bridging the Gap Between f-GANs and Wasserstein GANs

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.

Density Ratio Estimation Image Generation

Reliable Decisions with Threshold Calibration

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.

BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery

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.

Causal Discovery Stochastic Optimization +1

PiRank: Scalable Learning To Rank via Differentiable Sorting

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.

Learning-To-Rank

HyperSPNs: Compact and Expressive Probabilistic Circuits

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.

Density Estimation

D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation

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.

Conditional Image Generation Image Manipulation +1

Density Ratio Estimation via Infinitesimal Classification

no code implementations22 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.

Classification Density Ratio Estimation +1

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

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.

Computed Tomography (CT)

Estimating High Order Gradients of the Data Distribution by Denoising

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.

Denoising Image Generation

SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning

1 code implementation8 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.

A Unified Framework for Multi-distribution Density Ratio Estimation

no code implementations29 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.

Density Ratio Estimation Representation Learning

Equivariant Neural Network for Factor Graphs

no code implementations29 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.

On the Opportunities and Risks of Foundation Models

1 code implementation16 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.

Transfer Learning

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

1 code implementation2 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).

GAN inversion Image Generation

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration

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.

Decision Making

Multi-Agent Imitation Learning with Copulas

no code implementations10 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.

Imitation Learning

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

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.

Image Generation Imputation +1

Featurized Density Ratio Estimation

1 code implementation5 Jul 2021 Kristy Choi, Madeline Liao, Stefano Ermon

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

Data Augmentation Density Ratio Estimation +1

IQ-Learn: Inverse soft-Q Learning for Imitation

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.

Decision Making Imitation Learning +1

Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis

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.

Object Counting Super-Resolution

Temporal Predictive Coding For Model-Based Planning In Latent Space

3 code implementations14 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.

Model-based Reinforcement Learning Representation Learning

D2C: Diffusion-Denoising Models for Few-shot Conditional Generation

1 code implementation12 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.

Conditional Image Generation Denoising +2

Improving Compositionality of Neural Networks by Decoding Representations to Inputs

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.

Fairness Out-of-Distribution Detection

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information

1 code implementation19 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).

Experimental Design Gaussian Processes +1

On the Critical Role of Conventions in Adaptive Human-AI Collaboration

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.

Hybrid Mutual Information Lower-bound Estimators for Representation Learning

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.

Representation Learning

Anytime Sampling for Autoregressive Models via Ordered Autoencoding

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.

Audio Generation

Localized Calibration: Metrics and Recalibration

no code implementations22 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.

Decision Making

Neural Network Compression for Noisy Storage Devices

no code implementations15 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.

Neural Network Compression

Negative Data Augmentation

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.

Action Recognition Anomaly Detection +7

Maximum Likelihood Training of Score-Based Diffusion Models

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.

Data Augmentation Image Generation

Understanding Classifiers with Generative Models

no code implementations1 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.

Two-sample testing

H-divergence: A Decision-Theoretic Discrepancy Measure for Two Sample Tests

no code implementations1 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.

Non-Markovian Predictive Coding For Planning In Latent Space

no code implementations1 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.

Model-based Reinforcement Learning Representation Learning

Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients

no code implementations30 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.

Decision Making

PiRank: Learning To Rank via Differentiable Sorting

1 code implementation12 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.

Learning-To-Rank

Score-Based Generative Modeling through Stochastic Differential Equations

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.

Colorization Image Inpainting +1

Efficient Conditional Pre-training for Transfer Learning

no code implementations20 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.

Transfer Learning

Geography-Aware Self-Supervised Learning

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.

Contrastive Learning Image Classification +3

Autoregressive Score Matching

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.

Density Estimation Image Denoising +2

Probabilistic Circuits for Variational Inference in Discrete Graphical Models

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).

Variational Inference

Imitation with Neural Density Models

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.

Density Estimation Imitation Learning

Denoising Diffusion Implicit Models

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.

Denoising Image Interpolation

Understanding Classifier Mistakes with Generative Models

no code implementations5 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.

Two-sample testing

Privacy Preserving Recalibration under Domain Shift

no code implementations21 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.

Multi-label Contrastive Predictive Coding

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.

Knowledge Distillation Multi-class Classification +3

Efficient Learning of Generative Models via Finite-Difference Score Matching

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.

Unsupervised Calibration under Covariate Shift

no code implementations29 Jun 2020 Anusri Pampari, Stefano Ermon

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

Decision Making Domain Adaptation

Experience Replay with Likelihood-free Importance Weights

1 code implementation23 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.

OpenAI Gym

A Framework for Sample Efficient Interval Estimation with Control Variates

1 code implementation18 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.

Individual Calibration with Randomized Forecasting

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.

Decision Making Fairness

Improved Techniques for Training Score-Based Generative Models

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.

Image Generation

Predicting Livelihood Indicators from Community-Generated Street-Level Imagery

1 code implementation15 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.

Efficient Poverty Mapping using Deep Reinforcement Learning

no code implementations7 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.

Object Detection

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

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.

Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks

no code implementations11 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.

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks

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.

Training Deep Energy-Based Models with f-Divergence Minimization

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.

Gaussianization Flows

3 code implementations4 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.

Permutation Invariant Graph Generation via Score-Based Generative Modeling

1 code implementation2 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).

Graph Generation

Predictive Coding for Locally-Linear Control

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.

Decision Making

Learning When and Where to Zoom with Deep Reinforcement Learning

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.

Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving

1 code implementation10 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.

Generating Interpretable Poverty Maps using Object Detection in Satellite Images

no code implementations5 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.

Feature Importance Object Detection

Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks

1 code implementation14 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.

Cloud Removal Image Generation +1

Efficient Object Detection in Large Images using Deep Reinforcement Learning

2 code implementations9 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.

Object Detection

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.

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.

Audio Super-Resolution Super-Resolution +1

Approximating Human Judgment of Generated Image Quality

no code implementations30 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.

Approximating the Permanent by Sampling from Adaptive Partitions

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.

Fair Generative Modeling via Weak Supervision

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.

Image Generation

Bridging the Gap Between $f$-GANs and Wasserstein GANs

1 code implementation22 Oct 2019 Jiaming Song, Stefano Ermon

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

Image Generation

Weakly Supervised Disentanglement with Guarantees

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.

Understanding the Limitations of Variational Mutual Information Estimators

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.

Domain Adaptive Imitation Learning

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.

Imitation Learning

Towards Certified Defense for Unrestricted Adversarial Attacks

no code implementations25 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.

Adversarial Attack

Cross Domain Imitation Learning

no code implementations25 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.

Imitation Learning

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations

1 code implementation14 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.

Audio Super-Resolution Super-Resolution +1

Multi-Agent Adversarial Inverse Reinforcement Learning

1 code implementation30 Jul 2019 Lantao Yu, Jiaming Song, Stefano Ermon

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

MintNet: Building Invertible Neural Networks with Masked Convolutions

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.

Image Generation

Generative Modeling by Estimating Gradients of the Data Distribution

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.

Image Inpainting

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

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.

Data Augmentation

Calibrated Model-Based Deep Reinforcement Learning

1 code implementation19 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.

Model-based Reinforcement Learning

Learning Neural PDE Solvers with Convergence Guarantees

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.

Sliced Score Matching: A Scalable Approach to Density and Score Estimation

5 code implementations17 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.

Variational Inference

Learning to Interpret Satellite Images in Global Scale Using Wikipedia

3 code implementations7 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.

Predicting Economic Development using Geolocated Wikipedia Articles

no code implementations5 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.

Distributed generation of privacy preserving data with user customization

no code implementations20 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.

Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting

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.

Data Augmentation

Training Variational Autoencoders with Buffered Stochastic Variational Inference

no code implementations27 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.

Latent Variable Models Variational Inference

Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty

no code implementations13 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.

Meta-Amortized Variational Inference and Learning

1 code implementation5 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.

Density Estimation Image Classification +1

Reparameterizable Subset Sampling via Continuous Relaxations

1 code implementation29 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.

Feature Selection Stochastic Optimization

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

1 code implementation23 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%.

Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization

no code implementations26 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

Learning Controllable Fair Representations

3 code implementations11 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.

Fairness

Streamlining Variational Inference for Constraint Satisfaction Problems

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.

Variational Inference

Neural Joint Source-Channel Coding

1 code implementation19 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.

NECST: Neural Joint Source-Channel Coding

no code implementations27 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.

Learning to Interpret Satellite Images Using Wikipedia

no code implementations19 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.

Multi-Agent Generative Adversarial Imitation Learning

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.

Imitation Learning

Improved Training with Curriculum GANs

no code implementations24 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.

Curriculum Learning Image Generation

Modeling Sparse Deviations for Compressed Sensing using Generative Models

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.

The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models

2 code implementations18 Jun 2018 Shengjia Zhao, Jiaming Song, Stefano Ermon

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

Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning

no code implementations3 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.

Fine-tuning Spatial Interpolation

Adversarial Constraint Learning for Structured Prediction

1 code implementation27 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.

Pose Estimation Structured Prediction +2

Amortized Inference Regularization

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.

Density Estimation Representation Learning

Constructing Unrestricted Adversarial Examples with Generative Models

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.

Tile2Vec: Unsupervised representation learning for spatially distributed data

3 code implementations8 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.

General Classification Unsupervised Representation Learning

Variational Rejection Sampling

no code implementations5 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.

Latent Variable Models Variational Inference

End-to-End Learning of Motion Representation for Video Understanding

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.

Action Recognition Optical Flow Estimation +1

Best arm identification in multi-armed bandits with delayed feedback

no code implementations29 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.

Hyperparameter Optimization Multi-Armed Bandits

Accelerating Natural Gradient with Higher-Order Invariance

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.

A DIRT-T Approach to Unsupervised Domain Adaptation

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.

Unsupervised Domain Adaptation

Approximate Inference via Weighted Rademacher Complexity

1 code implementation27 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.

LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures

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.

Language Modelling

Adversarial Examples for Natural Language Classification Problems

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.

Classification Fake News Detection +2

Shape optimization in laminar flow with a label-guided variational autoencoder

no code implementations10 Dec 2017 Stephan Eismann, Stefan Bartzsch, Stefano Ermon

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

Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces

no code implementations21 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.

Acrobot Imitation Learning

Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning

no code implementations10 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.

PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

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.

Two-sample testing

A Survey of Human Activity Recognition Using WiFi CSI

1 code implementation23 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.

Activity Recognition

Audio Super Resolution using Neural Networks

4 code implementations2 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.

Audio Super-Resolution

Learning Hierarchical Features from Deep Generative Models

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.

Hierarchical structure Latent Variable Models

A-NICE-MC: Adversarial Training for MCMC

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.

InfoVAE: Information Maximizing Variational Autoencoders

6 code implementations7 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.

Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models

2 code implementations24 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.

InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

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.

Imitation Learning

On the Limits of Learning Representations with Label-Based Supervision

no code implementations7 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.

Representation Learning Transfer Learning

Towards Deeper Understanding of Variational Autoencoding Models

2 code implementations28 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.

Learning Hierarchical Features from Generative Models

3 code implementations27 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.

Hierarchical structure Latent Variable Models

Boosted Generative Models

1 code implementation27 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.

Density Estimation General Classification

Variational Bayes on Monte Carlo Steroids

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.

Latent Variable Models

Adaptive Concentration Inequalities for Sequential Decision Problems

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.

Two-sample testing

Solving Marginal MAP Problems with NP Oracles and Parity Constraints

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.

Decision Making

Label-Free Supervision of Neural Networks with Physics and Domain Knowledge

no code implementations18 Sep 2016 Russell Stewart, Stefano Ermon

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

Estimating Uncertainty Online Against an Adversary

no code implementations13 Jul 2016 Volodymyr Kuleshov, Stefano Ermon

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

General Classification Medical Diagnosis +1

Generative Adversarial Imitation Learning

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.

Imitation Learning

Model-Free Imitation Learning with Policy Optimization

no code implementations26 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.

Imitation Learning

Tight Variational Bounds via Random Projections and I-Projections

no code implementations5 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.

Variational Inference

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

1 code implementation1 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.

Transfer Learning

Variable Elimination in the Fourier Domain

no code implementations17 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.

Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery

no code implementations27 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.

Embed and Project: Discrete Sampling with Universal Hashing

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.

Combinatorial Optimization

Density Propagation and Improved Bounds on the Partition Function

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.

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