b'Aditya Grover'

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...

0 Hritik Bansal, et al. ∙

research

∙ 07/04/2023

ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling

Modeling weather and climate is an essential endeavor to understand the ...

0 Tung Nguyen, et al. ∙

research

∙ 06/12/2023

Diffusion Models for Black-Box Optimization

The goal of offline black-box optimization (BBO) is to optimize an expen...

0 Siddarth Krishnamoorthy, et al. ∙

research

∙ 06/09/2023

Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models

Reinforcement learning presents an attractive paradigm to reason about s...

0 Siyan Zhao, et al. ∙

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...

0 Baiting Zhu, et al. ∙

research

∙ 03/06/2023

CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning

Multimodal contrastive pretraining has been used to train multimodal rep...

0 Hritik Bansal, et al. ∙

research

∙ 02/05/2023

Leaving Reality to Imagination: Robust Classification via Generated Datasets

Recent research on robustness has revealed significant performance gaps ...

0 Hritik Bansal, et al. ∙

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...

0 Tung Nguyen, et al. ∙

research

∙ 11/23/2022

Masked Autoencoding for Scalable and Generalizable Decision Making

We are interested in learning scalable agents for reinforcement learning...

0 Fangchen Liu, et al. ∙

research

∙ 10/12/2022

Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories

Natural agents can effectively learn from multiple data sources that dif...

0 Qinqing Zheng, et al. ∙

research

∙ 10/11/2022

ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning

The goal of offline reinforcement learning (RL) is to learn near-optimal...

0 Tung Nguyen, et al. ∙

research

∙ 07/11/2022

Matching Normalizing Flows and Probability Paths on Manifolds

Continuous Normalizing Flows (CNFs) are a class of generative models tha...

12 Heli Ben-Hamu, et al. ∙

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...

0 Tung Nguyen, et al. ∙

research

∙ 06/22/2022

Generative Pretraining for Black-Box Optimization

Many problems in science and engineering involve optimizing an expensive...

0 Siddarth Krishnamoorthy, et al. ∙

research

∙ 05/28/2022

CyCLIP: Cyclic Contrastive Language-Image Pretraining

Recent advances in contrastive representation learning over paired image...

0 Shashank Goel, et al. ∙

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...

0 Carl Qi, et al. ∙

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...

0 Yuqing Du, et al. ∙

research

∙ 02/11/2022

Online Decision Transformer

Recent work has shown that offline reinforcement learning (RL) can be fo...

0 Qinqing Zheng, et al. ∙

research

∙ 12/31/2021

BARACK: Partially Supervised Group Robustness With Guarantees

While neural networks have shown remarkable success on classification ta...

4 Nimit Sohoni, et al. ∙

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...

0 Chris Cundy, et al. ∙

research

∙ 08/18/2021

Moser Flow: Divergence-based Generative Modeling on Manifolds

We are interested in learning generative models for complex geometries d...

0 Noam Rozen, et al. ∙

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...

3 Wenshuo Guo, et al. ∙

research

∙ 06/17/2021

Rotation Invariant Graph Neural Networks using Spin Convolutions

Progress towards the energy breakthroughs needed to combat climate chang...

46 Muhammed Shuaibi, et al. ∙

research

∙ 06/02/2021

Decision Transformer: Reinforcement Learning via Sequence Modeling

We present a framework that abstracts Reinforcement Learning (RL) as a s...

0 Lili Chen, et al. ∙

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...

0 Kourosh Hakhamaneshi, et al. ∙

research

∙ 03/09/2021

Pretrained Transformers as Universal Computation Engines

We investigate the capability of a transformer pretrained on natural lan...

0 Kevin Lu, et al. ∙

research

∙ 12/12/2020

PiRank: Learning To Rank via Differentiable Sorting

A key challenge with machine learning approaches for ranking is the gap ...

0 Robin Swezey, et al. ∙

research

∙ 12/07/2020

Reset-Free Lifelong Learning with Skill-Space Planning

The objective of lifelong reinforcement learning (RL) is to optimize age...

0 Kevin Lu, et al. ∙

research

∙ 03/02/2020

Permutation Invariant Graph Generation via Score-Based Generative Modeling

Learning generative models for graph-structured data is challenging beca...

18 Chenhao Niu, et al. ∙

research

∙ 10/26/2019

Fair Generative Modeling via Weak Supervision

Real-world datasets are often biased with respect to key demographic fac...

25 Aditya Grover, et al. ∙

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 ...

8 Aditya Grover, et al. ∙

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...

6 Aditya Grover, et al. ∙

research

∙ 03/21/2019

Stochastic Optimization of Sorting Networks via Continuous Relaxations

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

8 Aditya Grover, et al. ∙

research

∙ 12/26/2018

Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization

The goal of statistical compressive sensing is to efficiently acquire an...

10 Aditya Grover, et al. ∙

research

∙ 12/11/2018

Learning Controllable Fair Representations

Learning data representations that are transferable and fair with respec...

12 Jiaming Song, et al. ∙

research

∙ 11/24/2018

Streamlining Variational Inference for Constraint Satisfaction Problems

Several algorithms for solving constraint satisfaction problems are base...

0 Aditya Grover, et al. ∙

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...

2 Manik Dhar, et al. ∙

research

∙ 06/17/2018

Learning Policy Representations in Multiagent Systems

Modeling agent behavior is central to understanding the emergence of com...

0 Aditya Grover, et al. ∙

research

∙ 04/05/2018

Variational Rejection Sampling

Learning latent variable models with stochastic variational inference is...

0 Aditya Grover, et al. ∙

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...

0 Aditya Grover, et al. ∙

research

∙ 03/28/2018

Graphite: Iterative Generative Modeling of Graphs

Graphs are a fundamental abstraction for modeling relational data. Howev...

0 Aditya Grover, et al. ∙

research

∙ 05/24/2017

Flow-GAN: Bridging implicit and prescribed learning in generative models

Evaluating the performance of generative models for unsupervised learnin...

0 Aditya Grover, et al. ∙

research

∙ 02/27/2017

Boosted Generative Models

We propose a new approach for using unsupervised boosting to create an e...

0 Aditya Grover, et al. ∙

research

∙ 07/03/2016

node2vec: Scalable Feature Learning for Networks

Prediction tasks over nodes and edges in networks require careful effort...

0 Aditya Grover, et al. ∙

research

∙ 06/30/2016

Contextual Symmetries in Probabilistic Graphical Models

An important approach for efficient inference in probabilistic graphical...

0 Ankit Anand, et al. ∙

Aditya Grover

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