Osbert Bastani

research

∙ 07/07/2023

TRAC: Trustworthy Retrieval Augmented Chatbot

Although conversational AIs have demonstrated fantastic performance, the...

0 Shuo Li, et al. ∙

research

∙ 05/26/2023

Policy Synthesis and Reinforcement Learning for Discounted LTL

The difficulty of manually specifying reward functions has led to an int...

0 Rajeev Alur, et al. ∙

research

∙ 05/22/2023

TOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching

Standard model-based reinforcement learning (MBRL) approaches fit a tran...

0 Yecheng Jason Ma, et al. ∙

research

∙ 03/28/2023

Ideograph: A Language for Expressing and Manipulating Structured Data

We introduce Ideograph, a language for expressing and manipulating struc...

0 Stephen Mell, et al. ∙

research

∙ 02/17/2023

PAC Prediction Sets for Large Language Models of Code

Prediction sets have recently been shown to be a promising strategy for ...

0 Adam Khakhar, et al. ∙

research

∙ 02/06/2023

Robust Subtask Learning for Compositional Generalization

Compositional reinforcement learning is a promising approach for trainin...

0 Kishor Jothimurugan, et al. ∙

research

∙ 02/03/2023

SPARLING: Learning Latent Representations with Extremely Sparse Activations

Real-world processes often contain intermediate state that can be modele...

0 Kavi Gupta, et al. ∙

research

∙ 11/17/2022

ACon^2: Adaptive Conformal Consensus for Provable Blockchain Oracles

Blockchains with smart contracts are distributed ledger systems which ac...

0 Sangdon Park, et al. ∙

research

∙ 11/15/2022

Decision-Aware Learning for Optimizing Health Supply Chains

We study the problem of allocating limited supply of medical resources i...

0 Tsai-Hsuan Chung, et al. ∙

research

∙ 11/11/2022

Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms

We describe the current content moderation strategy employed by Meta to ...

0 Vashist Avadhanula, et al. ∙

research

∙ 07/06/2022

PAC Prediction Sets for Meta-Learning

Uncertainty quantification is a key component of machine learning models...

0 Sangdon Park, et al. ∙

research

∙ 06/07/2022

How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage Regression

Offline goal-conditioned reinforcement learning (GCRL) promises general-...

27 Yecheng Jason Ma, et al. ∙

research

∙ 06/06/2022

Specification-Guided Learning of Nash Equilibria with High Social Welfare

Reinforcement learning has been shown to be an effective strategy for au...

10 Kishor Jothimurugan, et al. ∙

research

∙ 06/02/2022

Practical Adversarial Multivalid Conformal Prediction

We give a simple, generic conformal prediction method for sequential pre...

11 Osbert Bastani, et al. ∙

research

∙ 04/27/2022

Counterfactual Explanations for Natural Language Interfaces

A key challenge facing natural language interfaces is enabling users to ...

0 George Tolkachev, et al. ∙

research

∙ 04/15/2022

Towards PAC Multi-Object Detection and Tracking

Accurately detecting and tracking multi-objects is important for safety-...

0 Shuo Li, et al. ∙

research

∙ 02/25/2022

Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates

Environments with sparse rewards and long horizons pose a significant ch...

9 Souradeep Dutta, et al. ∙

research

∙ 02/20/2022

Understanding Robust Generalization in Learning Regular Languages

A key feature of human intelligence is the ability to generalize beyond ...

0 Soham Dan, et al. ∙

research

∙ 02/04/2022

SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching

We propose State Matching Offline DIstribution Correction Estimation (SM...

0 Yecheng Jason Ma, et al. ∙

research

∙ 12/14/2021

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Reinforcement Learning (RL) agents in the real world must satisfy safety...

0 Yecheng Jason Ma, et al. ∙

research

∙ 10/25/2021

Safely Bridging Offline and Online Reinforcement Learning

A key challenge to deploying reinforcement learning in practice is explo...

0 Wanqiao Xu, et al. ∙

research

∙ 10/11/2021

Safe Human-Interactive Control via Shielding

Ensuring safety for human-interactive robotics is important due to the p...

0 Jeevana Priya Inala, et al. ∙

research

∙ 10/11/2021

Synthesizing Machine Learning Programs with PAC Guarantees via Statistical Sketching

We study the problem of synthesizing programs that include machine learn...

0 Osbert Bastani, et al. ∙

research

∙ 09/22/2021

Robust Generalization of Quadratic Neural Networks via Function Identification

A key challenge facing deep learning is that neural networks are often n...

0 Kan Xu, et al. ∙

research

∙ 08/19/2021

Improving Human Decision-Making with Machine Learning

A key aspect of human intelligence is their ability to convey their know...

0 Hamsa Bastani, et al. ∙

research

∙ 07/12/2021

Conservative Offline Distributional Reinforcement Learning

Many reinforcement learning (RL) problems in practice are offline, learn...

0 Yecheng Jason Ma, et al. ∙

research

∙ 06/25/2021

Compositional Reinforcement Learning from Logical Specifications

We study the problem of learning control policies for complex tasks give...

14 Kishor Jothimurugan, et al. ∙

research

∙ 06/17/2021

PAC Prediction Sets Under Covariate Shift

An important challenge facing modern machine learning is how to rigorous...

0 Sangdon Park, et al. ∙

research

∙ 04/18/2021

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

Sparse regression has recently been applied to enable transfer learning ...

0 Kan Xu, et al. ∙

research

∙ 04/14/2021

Web Question Answering with Neurosymbolic Program Synthesis

In this paper, we propose a new technique based on program synthesis for...

0 Qiaochu Chen, et al. ∙

research

∙ 03/26/2021

SkyQuery: An Aerial Drone Video Sensing Platform

Video-based sensing from aerial drones, especially small multirotor dron...

0 Favyen Bastani, et al. ∙

research

∙ 02/22/2021

Program Synthesis Guided Reinforcement Learning

A key challenge for reinforcement learning is solving long-horizon plann...

0 Yichen Yang, et al. ∙

research

∙ 01/05/2021

Neurosymbolic Transformers for Multi-Agent Communication

We study the problem of inferring communication structures that can solv...

7 Jeevana Priya Inala, et al. ∙

research

∙ 11/30/2020

Diverse Sampling for Normalizing Flow Based Trajectory Forecasting

For autonomous cars to drive safely and effectively, they must anticipat...

0 Yecheng Jason Ma, et al. ∙

research

∙ 11/12/2020

Robust and Stable Black Box Explanations

As machine learning black boxes are increasingly being deployed in real-...

0 Himabindu Lakkaraju, et al. ∙

research

∙ 11/12/2020

Ensuring Actionable Recourse via Adversarial Training

As machine learning models are increasingly deployed in high-stakes doma...

0 Alexis Ross, et al. ∙

research

∙ 11/02/2020

PAC Confidence Predictions for Deep Neural Network Classifiers

A key challenge for deploying deep neural networks (DNNs) in safety crit...

0 Sangdon Park, et al. ∙

research

∙ 10/29/2020

Abstract Value Iteration for Hierarchical Reinforcement Learning

We propose a novel hierarchical reinforcement learning framework for con...

14 Kishor Jothimurugan, et al. ∙

research

∙ 08/21/2020

A Composable Specification Language for Reinforcement Learning Tasks

Reinforcement learning is a promising approach for learning control poli...

5 Kishor Jothimurugan, et al. ∙

research

∙ 02/29/2020

Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation

Reliable uncertainty estimates are an important tool for helping autonom...

9 Sangdon Park, et al. ∙

research

∙ 12/31/2019

PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

We propose an algorithm combining calibrated prediction and generalizati...

15 Sangdon Park, et al. ∙

research

∙ 12/29/2019

Synthesizing Queries via Interactive Sketching

We propose a novel approach to program synthesis, focusing on synthesizi...

0 Osbert Bastani, et al. ∙

research

∙ 11/15/2019

"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations

As machine learning black boxes are increasingly being deployed in criti...

0 Himabindu Lakkaraju, et al. ∙

research

∙ 10/25/2019

MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding

Reinforcement learning is a promising approach to learning control polic...

0 Wenbo Zhang, et al. ∙

research

∙ 10/24/2019

Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics

This paper proposes a framework for safe reinforcement learning that can...

0 Shuo Li, et al. ∙

research

∙ 07/11/2019

Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints

In this paper, we present a learning approach to goal assignment and tra...

0 Arbaaz Khan, et al. ∙

research

∙ 05/25/2019

Safe Reinforcement Learning via Online Shielding

Reinforcement learning is a promising approach to learning control polic...

0 Osbert Bastani, et al. ∙

research

∙ 02/25/2019

PolyDroid: Learning-Driven Specialization of Mobile Applications

The increasing prevalence of mobile apps has led to a proliferation of r...

0 Brian Heath, et al. ∙

research

∙ 01/24/2019

Learning Interpretable Models with Causal Guarantees

Machine learning has shown much promise in helping improve the quality o...

0 Carolyn Kim, et al. ∙

research

∙ 01/24/2019

Fairness with Dynamics

It has recently been shown that if feedback effects of decisions are ign...

0 Min Wen, et al. ∙

Osbert Bastani

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