Search Results for author: Osbert Bastani

Found 47 papers, 21 papers with code

Robust Black Box Explanations Under Distribution Shift

no code implementations ICML 2020 Himabindu Lakkaraju, Nino Arsov, Osbert Bastani

As machine learning black boxes are increasingly being deployed in real-world applications, there has been a growing interest in developing post hoc explanations that summarize the behaviors of these black box models.

Few-Shot Novel Concept Learning for Semantic Parsing

no code implementations Findings (EMNLP) 2021 Soham Dan, Osbert Bastani, Dan Roth

This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept.

Program Synthesis Semantic Parsing

Generating Programmatic Referring Expressions via Program Synthesis

1 code implementation ICML 2020 Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik

The policy neural network employs a program interpreter that provides immediate feedback on the consequences of the decisions made by the policy, and also takes into account the uncertainty in the symbolic representation of the image.

Enumerative Search

Counterfactual Explanations for Natural Language Interfaces

1 code implementation ACL 2022 George Tolkachev, Stephen Mell, Steve Zdancewic, Osbert Bastani

A key challenge facing natural language interfaces is enabling users to understand the capabilities of the underlying system.

Semantic Parsing

Towards PAC Multi-Object Detection and Tracking

no code implementations15 Apr 2022 Shuo Li, Sangdon Park, Xiayan Ji, Insup Lee, Osbert Bastani

Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation.

Autonomous Navigation Object Detection

Understanding Robust Generalization in Learning Regular Languages

no code implementations20 Feb 2022 Soham Dan, Osbert Bastani, Dan Roth

Currently, deep neural networks struggle to generalize robustly to such shifts in the data distribution.

SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching

1 code implementation4 Feb 2022 Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani

We propose State Matching Offline DIstribution Correction Estimation (SMODICE), a novel and versatile algorithm for offline imitation learning (IL) via state-occupancy matching.

Imitation Learning reinforcement-learning

Safely Bridging Offline and Online Reinforcement Learning

1 code implementation25 Oct 2021 Wanqiao Xu, Kan Xu, Hamsa Bastani, Osbert Bastani

A key challenge to deploying reinforcement learning in practice is exploring safely.


Synthesizing Machine Learning Programs with PAC Guarantees via Statistical Sketching

no code implementations11 Oct 2021 Osbert Bastani

We study the problem of synthesizing programs that include machine learning components such as deep neural networks (DNNs).

Image Classification Learning Theory

PAC Synthesis of Machine Learning Programs

no code implementations NeurIPS Workshop AIPLANS 2021 Osbert Bastani

We study the problem of synthesizing programs that include machine learning components such as deep neural networks (DNNs).

Image Classification Learning Theory

Synthesizing Video Trajectory Queries

no code implementations NeurIPS Workshop AIPLANS 2021 Stephen Mell, Favyen Bastani, Stephan Zdancewic, Osbert Bastani

A key challenge is that queries are difficult for end users to develop: queries must reason about complex spatial and temporal patterns in object trajectories in order to select trajectories of interest, and predicates often include real-valued parameters (e. g., whether two cars are within a certain distance) that can be tedious to manually tune.

Active Learning Object Tracking

Sequential Covariate Shift Detection Using Classifier Two-Sample Tests

no code implementations29 Sep 2021 Sooyong Jang, Sangdon Park, Insup Lee, Osbert Bastani

This problem can naturally be solved using a two-sample test--- i. e., test whether the current test distribution of covariates equals the training distribution of covariates.

Improving Human Decision-Making with Machine Learning

no code implementations19 Aug 2021 Hamsa Bastani, Osbert Bastani, Wichinpong Park Sinchaisri

Focusing on sequential decision-making, we design a novel machine learning algorithm that conveys its insights to humans in the form of interpretable "tips".

Decision Making

Conservative Offline Distributional Reinforcement Learning

1 code implementation NeurIPS 2021 Yecheng Jason Ma, Dinesh Jayaraman, Osbert Bastani

We prove that CODAC learns a conservative return distribution -- in particular, for finite MDPs, CODAC converges to an uniform lower bound on the quantiles of the return distribution; our proof relies on a novel analysis of the distributional Bellman operator.

Distributional Reinforcement Learning Offline RL +2

Compositional Reinforcement Learning from Logical Specifications

1 code implementation NeurIPS 2021 Kishor Jothimurugan, Suguman Bansal, Osbert Bastani, Rajeev Alur

Our approach then incorporates reinforcement learning to learn neural network policies for each edge (sub-task) within a Dijkstra-style planning algorithm to compute a high-level plan in the graph.


PAC Prediction Sets Under Covariate Shift

1 code implementation ICLR 2022 Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani

Our approach focuses on the setting where there is a covariate shift from the source distribution (where we have labeled training examples) to the target distribution (for which we want to quantify uncertainty).

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

no code implementations18 Apr 2021 Kan Xu, Xuanyi Zhao, Hamsa Bastani, Osbert Bastani

To leverage this information, words are typically translated into word embeddings -- vectors that encode the semantic relationships between words -- through unsupervised learning algorithms such as matrix factorization.

Generalization Bounds Learning Word Embeddings +1

Program Synthesis Guided Reinforcement Learning for Partially Observed Environments

1 code implementation NeurIPS 2021 Yichen David Yang, Jeevana Priya Inala, Osbert Bastani, Yewen Pu, Armando Solar-Lezama, Martin Rinard

Our results demonstrate that our approach can obtain the benefits of program-guided reinforcement learning without requiring the user to provide a new guiding program for every new task.

Program Synthesis reinforcement-learning

Neurosymbolic Transformers for Multi-Agent Communication

1 code implementation NeurIPS 2020 Jeevana Priya Inala, Yichen Yang, James Paulos, Yewen Pu, Osbert Bastani, Vijay Kumar, Martin Rinard, Armando Solar-Lezama

We study the problem of inferring communication structures that can solve cooperative multi-agent planning problems while minimizing the amount of communication.

Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints

no code implementations1 Jan 2021 Halley Young, Maxwell Du, Osbert Bastani

We propose a novel approach for incorporating structure in the form of relational constraints between different subcomponents of an example (e. g., lines of a poem or measures of music).

Program Synthesis

Likelihood-Based Diverse Sampling for Trajectory Forecasting

1 code implementation ICCV 2021 Yecheng Jason Ma, Jeevana Priya Inala, Dinesh Jayaraman, Osbert Bastani

We propose Likelihood-Based Diverse Sampling (LDS), a method for improving the quality and the diversity of trajectory samples from a pre-trained flow model.

Trajectory Forecasting

Robust and Stable Black Box Explanations

no code implementations12 Nov 2020 Himabindu Lakkaraju, Nino Arsov, Osbert Bastani

To the best of our knowledge, this work makes the first attempt at generating post hoc explanations that are robust to a general class of adversarial perturbations that are of practical interest.

Learning Models for Actionable Recourse

1 code implementation NeurIPS 2021 Alexis Ross, Himabindu Lakkaraju, Osbert Bastani

As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations.

Decision Making

PAC Confidence Predictions for Deep Neural Network Classifiers

no code implementations ICLR 2021 Sangdon Park, Shuo Li, Insup Lee, Osbert Bastani

In our experiments, we demonstrate that our approach can be used to provide guarantees for state-of-the-art DNNs.

Abstract Value Iteration for Hierarchical Reinforcement Learning

no code implementations29 Oct 2020 Kishor Jothimurugan, Osbert Bastani, Rajeev Alur

We propose a novel hierarchical reinforcement learning framework for control with continuous state and action spaces.

Hierarchical Reinforcement Learning reinforcement-learning

Synthesizing Programmatic Policies that Inductively Generalize

no code implementations ICLR 2020 Jeevana Priya Inala, Osbert Bastani, Zenna Tavares, Armando Solar-Lezama

We show that our algorithm can be used to learn policies that inductively generalize to novel environments, whereas traditional neural network policies fail to do so.

Imitation Learning reinforcement-learning

Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation

no code implementations29 Feb 2020 Sangdon Park, Osbert Bastani, James Weimer, Insup Lee

Our algorithm uses importance weighting to correct for the shift from the training to the real-world distribution.

Unsupervised Domain Adaptation

PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

1 code implementation ICLR 2020 Sangdon Park, Osbert Bastani, Nikolai Matni, Insup Lee

We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i. e., the confidence set for a given input contains the true label with high probability.

Generalization Bounds Learning Theory +2

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

no code implementations15 Nov 2019 Himabindu Lakkaraju, Osbert Bastani

Our work is the first to empirically establish how user trust in black box models can be manipulated via misleading explanations.

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

no code implementations25 Oct 2019 Wenbo Zhang, Osbert Bastani, Vijay Kumar

Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks.

Multi-agent Reinforcement Learning reinforcement-learning

Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics

no code implementations24 Oct 2019 Shuo Li, Osbert Bastani

We build on the idea of model predictive shielding (MPS), where a backup controller is used to override the learned policy as needed to ensure safety.

Learning Theory reinforcement-learning +1

Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive Shielding

1 code implementation25 May 2019 Osbert Bastani

Reinforcement learning is a promising approach to synthesizing policies for challenging robotics tasks.

reinforcement-learning Safe Reinforcement Learning

Algorithms for Fairness in Sequential Decision Making

1 code implementation24 Jan 2019 Min Wen, Osbert Bastani, Ufuk Topcu

It has recently been shown that if feedback effects of decisions are ignored, then imposing fairness constraints such as demographic parity or equality of opportunity can actually exacerbate unfairness.

Decision Making Fairness

Learning Interpretable Models with Causal Guarantees

no code implementations24 Jan 2019 Carolyn Kim, Osbert Bastani

We propose a framework for learning interpretable models from observational data that can be used to predict individual treatment effects (ITEs).

Decision Making

Probabilistic Verification of Fairness Properties via Concentration

1 code implementation2 Dec 2018 Osbert Bastani, Xin Zhang, Armando Solar-Lezama

As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities.


Verifiable Reinforcement Learning via Policy Extraction

1 code implementation NeurIPS 2018 Osbert Bastani, Yewen Pu, Armando Solar-Lezama

While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies.

Imitation Learning Model Compression +1

Interpretability via Model Extraction

no code implementations29 Jun 2017 Osbert Bastani, Carolyn Kim, Hamsa Bastani

The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions.

Model extraction reinforcement-learning

Interpreting Blackbox Models via Model Extraction

no code implementations23 May 2017 Osbert Bastani, Carolyn Kim, Hamsa Bastani

Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions.

Model extraction

Synthesizing Program Input Grammars

1 code implementation5 Aug 2016 Osbert Bastani, Rahul Sharma, Alex Aiken, Percy Liang

We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program.

Programming Languages

Measuring Neural Net Robustness with Constraints

1 code implementation NeurIPS 2016 Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi

Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled.

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