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Found 49 papers, 31 papers with code
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data.
Leaving Reality to Imagination: Robust Classification via Generated Datasets
We find that Imagenet classifiers trained on real data augmented with generated data achieve higher accuracy and effective robustness than standard training and popular augmentation strategies in the presence of natural distribution shifts.
ClimaX: A foundation model for weather and climate
Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere.
Masked Autoencoding for Scalable and Generalizable Decision Making
We are interested in learning scalable agents for reinforcement learning that can learn from large-scale, diverse sequential data similar to current large vision and language models.
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
For this setting, we develop and study a simple meta-algorithmic pipeline that learns an inverse dynamics model on the labelled data to obtain proxy-labels for the unlabelled data, followed by the use of any offline RL algorithm on the true and proxy-labelled trajectories.
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning
We study CWBC in the context of RvS (Emmons et al., 2021) and Decision Transformers (Chen et al., 2021), and show that CWBC significantly boosts their performance on various benchmarks.
Matching Normalizing Flows and Probability Paths on Manifolds
Continuous Normalizing Flows (CNFs) are a class of generative models that transform a prior distribution to a model distribution by solving an ordinary differential equation (ODE).
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
We propose Transformer Neural Processes (TNPs), a new member of the NP family that casts uncertainty-aware meta learning as a sequence modeling problem.
Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction
We augment the output of the well-known NWP model CFSv2 with deep learning to create a hybrid model that improves short-range global precipitation at 1-, 2-, and 3-day lead times.
Generative Pretraining for Black-Box Optimization
Many problems in science and engineering involve optimizing an expensive black-box function over a high-dimensional space.
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness.
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning
The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal.
It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation
Training such agents efficiently requires automatic generation of a goal curriculum.
Online Decision Transformer
Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via approaches similar to large-scale language modeling.
BARACK: Partially Supervised Group Robustness With Guarantees
Theoretically, we provide generalization bounds for our approach in terms of the worst-group performance, which scale with respect to both the total number of training points and the number of training points with group labels.
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
We propose Bayesian Causal Discovery Nets (BCD Nets), a variational inference framework for estimating a distribution over DAGs characterizing a linear-Gaussian SEM.
Frame Averaging for Invariant and Equivariant Network Design
For example, Euclidean motion invariant/equivariant graph or point cloud neural networks.
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals.
CAGE: Probing Causal Relationships in Deep Generative Models
Deep generative models excel at generating complex, high-dimensional data, often exhibiting impressive generalization beyond the training distribution.
Moser Flow: Divergence-based Generative Modeling on Manifolds
MF also produces a CNF via a solution to the change-of-variable formula, however differently from other CNF methods, its model (learned) density is parameterized as the source (prior) density minus the divergence of a neural network (NN).
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
We introduce the "inverse bandit" problem of estimating the rewards of a multi-armed bandit instance from observing the learning process of a low-regret demonstrator.
Rotation Invariant Graph Neural Networks using Spin Convolutions
We introduce a novel approach to modeling angular information between sets of neighboring atoms in a graph neural network.
Ranked #3 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data
Such a decomposition can dynamically control the reliability of information derived from the online and offline data and the use of pretrained neural networks permits scalability to large offline datasets.
Decision Transformer: Reinforcement Learning via Sequence Modeling
In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling.
Ranked #42 on
Atari Games
on Atari 2600 Pong
(using extra training data)
Pretrained Transformers as Universal Computation Engines
We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning -- in particular, without finetuning of the self-attention and feedforward layers of the residual blocks.
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling.
Robust Imitation via Decision-Time Planning
The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal.
PiRank: Scalable Learning To Rank via Differentiable Sorting
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.
Reset-Free Lifelong Learning with Skill-Space Planning
We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL based on planning in an abstract space of higher-order skills.
Permutation Invariant Graph Generation via Score-Based Generative Modeling
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).
Fair Generative Modeling via Weak Supervision
Real-world datasets are often biased with respect to key demographic factors such as race and gender.
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
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.
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain.
Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting
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.
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Sorting input objects is an important step in many machine learning pipelines.
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
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.
Learning Controllable Fair Representations
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.
Streamlining Variational Inference for Constraint Satisfaction Problems
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.
Neural Joint Source-Channel Coding
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.
Modeling Sparse Deviations for Compressed Sensing using Generative Models
In compressed sensing, a small number of linear measurements can be used to reconstruct an unknown signal.
Learning Policy Representations in Multiagent Systems
Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems.
Variational Rejection Sampling
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.
Best arm identification in multi-armed bandits with delayed feedback
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.
Graphite: Iterative Generative Modeling of Graphs
Graphs are a fundamental abstraction for modeling relational data.
Ranked #8 on
Link Prediction
on Citeseer
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood.
Boosted Generative Models
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.
Variational Bayes on Monte Carlo Steroids
We provide a new approach for learning latent variable models based on optimizing our new bounds on the log-likelihood.
node2vec: Scalable Feature Learning for Networks
Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Contextual Symmetries in Probabilistic Graphical Models
We extend previous work on exploiting symmetries in the MCMC framework to the case of contextual symmetries.
