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.
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.
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.
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.
no code implementations • 15 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.
no code implementations • 25 Feb 2022 • Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani, Edgar Dobriban, James Weimer, Insup Lee, Julia Parish-Morris
We formulate expert intervention as allowing the agent to execute option templates before learning an implementation.
no code implementations • 20 Feb 2022 • Soham Dan, Osbert Bastani, Dan Roth
Currently, deep neural networks struggle to generalize robustly to such shifts in the data distribution.
1 code implementation • 4 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.
1 code implementation • 14 Dec 2021 • Yecheng Jason Ma, Andrew Shen, Osbert Bastani, Dinesh Jayaraman
Further, CAP adaptively tunes this penalty during training using true cost feedback from the environment.
1 code implementation • 25 Oct 2021 • Wanqiao Xu, Kan Xu, Hamsa Bastani, Osbert Bastani
A key challenge to deploying reinforcement learning in practice is exploring safely.
no code implementations • 11 Oct 2021 • Osbert Bastani
We study the problem of synthesizing programs that include machine learning components such as deep neural networks (DNNs).
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).
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.
no code implementations • 29 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.
no code implementations • 22 Sep 2021 • Kan Xu, Hamsa Bastani, Osbert Bastani
We study this problem from the perspective of the statistical concept of parameter identification.
no code implementations • 19 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".
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.
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.
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).
no code implementations • 18 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.
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.
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.
no code implementations • 1 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).
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.
no code implementations • 12 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.
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.
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.
no code implementations • 29 Oct 2020 • Kishor Jothimurugan, Osbert Bastani, Rajeev Alur
We propose a novel hierarchical reinforcement learning framework for control with continuous state and action spaces.
1 code implementation • NeurIPS 2019 • Kishor Jothimurugan, Rajeev Alur, Osbert Bastani
Reinforcement learning is a promising approach for learning control policies for robot tasks.
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.
no code implementations • 29 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.
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.
no code implementations • 15 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.
no code implementations • 25 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.
no code implementations • 24 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.
1 code implementation • 25 May 2019 • Osbert Bastani
Reinforcement learning is a promising approach to synthesizing policies for challenging robotics tasks.
1 code implementation • 24 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.
no code implementations • 24 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).
no code implementations • 24 Jan 2019 • Osbert Bastani
Reinforcement learning is a promising approach to learning robotics controllers.
no code implementations • ICLR Workshop drlStructPred 2019 • Halley Young, Osbert Bastani, Mayur Naik
Significant strides have been made toward designing better generative models in recent years.
1 code implementation • 2 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.
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.
no code implementations • 29 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.
no code implementations • 23 May 2017 • Osbert Bastani, Carolyn Kim, Hamsa Bastani
Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions.
1 code implementation • 5 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
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.
2 code implementations • NeurIPS 2013 • Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng
This work introduces a model that can recognize objects in images even if no training data is available for the objects.