research
∙
08/30/2023
Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models
Aligning large language models (LLMs) with human values and intents crit...
share
research
∙
07/04/2023
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
Modeling weather and climate is an essential endeavor to understand the ...
research
∙
06/12/2023
Diffusion Models for Black-Box Optimization
The goal of offline black-box optimization (BBO) is to optimize an expen...
research
∙
06/09/2023
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
Reinforcement learning presents an attractive paradigm to reason about s...
research
∙
04/30/2023
Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL
The goal of multi-objective reinforcement learning (MORL) is to learn po...
research
∙
03/06/2023
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
Multimodal contrastive pretraining has been used to train multimodal rep...
research
∙
02/05/2023
Leaving Reality to Imagination: Robust Classification via Generated Datasets
Recent research on robustness has revealed significant performance gaps ...
research
∙
01/24/2023
ClimaX: A foundation model for weather and climate
Most state-of-the-art approaches for weather and climate modeling are ba...
research
∙
11/23/2022
Masked Autoencoding for Scalable and Generalizable Decision Making
We are interested in learning scalable agents for reinforcement learning...
research
∙
10/12/2022
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Natural agents can effectively learn from multiple data sources that dif...
research
∙
10/11/2022
ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning
The goal of offline reinforcement learning (RL) is to learn near-optimal...
research
∙
07/11/2022
Matching Normalizing Flows and Probability Paths on Manifolds
Continuous Normalizing Flows (CNFs) are a class of generative models tha...
research
∙
07/09/2022
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
Neural Processes (NPs) are a popular class of approaches for meta-learni...
research
∙
06/22/2022
Generative Pretraining for Black-Box Optimization
Many problems in science and engineering involve optimizing an expensive...
research
∙
05/28/2022
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Recent advances in contrastive representation learning over paired image...
research
∙
04/07/2022
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning
The goal of imitation learning is to mimic expert behavior from demonstr...
research
∙
02/22/2022
It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation
We are interested in training general-purpose reinforcement learning age...
research
∙
02/11/2022
Online Decision Transformer
Recent work has shown that offline reinforcement learning (RL) can be fo...
research
∙
12/31/2021
BARACK: Partially Supervised Group Robustness With Guarantees
While neural networks have shown remarkable success on classification ta...
research
∙
12/06/2021
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
A structural equation model (SEM) is an effective framework to reason ov...
research
∙
08/18/2021
Moser Flow: Divergence-based Generative Modeling on Manifolds
We are interested in learning generative models for complex geometries d...
research
∙
06/28/2021
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
We introduce the "inverse bandit" problem of estimating the rewards of a...
research
∙
06/17/2021
Rotation Invariant Graph Neural Networks using Spin Convolutions
Progress towards the energy breakthroughs needed to combat climate chang...
research
∙
06/02/2021
Decision Transformer: Reinforcement Learning via Sequence Modeling
We present a framework that abstracts Reinforcement Learning (RL) as a s...
research
∙
06/02/2021
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data
The goal of Multi-task Bayesian Optimization (MBO) is to minimize the nu...
research
∙
03/09/2021
Pretrained Transformers as Universal Computation Engines
We investigate the capability of a transformer pretrained on natural lan...
research
∙
12/12/2020
PiRank: Learning To Rank via Differentiable Sorting
A key challenge with machine learning approaches for ranking is the gap ...
research
∙
12/07/2020
Reset-Free Lifelong Learning with Skill-Space Planning
The objective of lifelong reinforcement learning (RL) is to optimize age...
research
∙
03/02/2020
Permutation Invariant Graph Generation via Score-Based Generative Modeling
Learning generative models for graph-structured data is challenging beca...
research
∙
10/26/2019
Fair Generative Modeling via Weak Supervision
Real-world datasets are often biased with respect to key demographic fac...
research
∙
06/23/2019
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
A learned generative model often produces biased statistics relative to ...
research
∙
05/30/2019
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given unpaired data from multiple domains, a key challenge is to efficie...
research
∙
03/21/2019
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Sorting input objects is an important step in many machine learning pipe...
research
∙
12/26/2018
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
The goal of statistical compressive sensing is to efficiently acquire an...
research
∙
12/11/2018
Learning Controllable Fair Representations
Learning data representations that are transferable and fair with respec...
research
∙
11/24/2018
Streamlining Variational Inference for Constraint Satisfaction Problems
Several algorithms for solving constraint satisfaction problems are base...
research
∙
07/04/2018
Modeling Sparse Deviations for Compressed Sensing using Generative Models
In compressed sensing, a small number of linear measurements can be used...
research
∙
06/17/2018
Learning Policy Representations in Multiagent Systems
Modeling agent behavior is central to understanding the emergence of com...
research
∙
04/05/2018
Variational Rejection Sampling
Learning latent variable models with stochastic variational inference is...
research
∙
03/29/2018
Best arm identification in multi-armed bandits with delayed feedback
We propose a generalization of the best arm identification problem in st...
research
∙
03/28/2018
Graphite: Iterative Generative Modeling of Graphs
Graphs are a fundamental abstraction for modeling relational data. Howev...
research
∙
05/24/2017
Flow-GAN: Bridging implicit and prescribed learning in generative models
Evaluating the performance of generative models for unsupervised learnin...
research
∙
02/27/2017
Boosted Generative Models
We propose a new approach for using unsupervised boosting to create an e...
research
∙
07/03/2016
node2vec: Scalable Feature Learning for Networks
Prediction tasks over nodes and edges in networks require careful effort...
research
∙
06/30/2016