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Found 47 papers, 21 papers with code
Robust Black Box Explanations Under Distribution Shift
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
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.
Generating Programmatic Referring Expressions via Program Synthesis
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.
Counterfactual Explanations for Natural Language Interfaces
A key challenge facing natural language interfaces is enabling users to understand the capabilities of the underlying system.
Towards PAC Multi-Object Detection and Tracking
Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation.
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
We formulate expert intervention as allowing the agent to execute option templates before learning an implementation.
Understanding Robust Generalization in Learning Regular Languages
Currently, deep neural networks struggle to generalize robustly to such shifts in the data distribution.
SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching
We propose State Matching Offline DIstribution Correction Estimation (SMODICE), a novel and versatile algorithm for offline imitation learning (IL) via state-occupancy matching.
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
Further, CAP adaptively tunes this penalty during training using true cost feedback from the environment.
Safely Bridging Offline and Online Reinforcement Learning
A key challenge to deploying reinforcement learning in practice is exploring safely.
Synthesizing Machine Learning Programs with PAC Guarantees via Statistical Sketching
We study the problem of synthesizing programs that include machine learning components such as deep neural networks (DNNs).
PAC Synthesis of Machine Learning Programs
We study the problem of synthesizing programs that include machine learning components such as deep neural networks (DNNs).
Synthesizing Video Trajectory Queries
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.
Sequential Covariate Shift Detection Using Classifier Two-Sample Tests
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.
Robust Generalization of Quadratic Neural Networks via Function Identification
We study this problem from the perspective of the statistical concept of parameter identification.
Improving Human Decision-Making with Machine Learning
Focusing on sequential decision-making, we design a novel machine learning algorithm that conveys its insights to humans in the form of interpretable "tips".
Conservative Offline Distributional Reinforcement Learning
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.
Compositional Reinforcement Learning from Logical Specifications
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
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
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.
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments
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.
Neurosymbolic Transformers for Multi-Agent Communication
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
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).
Likelihood-Based Diverse Sampling for Trajectory Forecasting
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.
Robust and Stable Black Box Explanations
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
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.
PAC Confidence Predictions for Deep Neural Network Classifiers
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
We propose a novel hierarchical reinforcement learning framework for control with continuous state and action spaces.
A Composable Specification Language for Reinforcement Learning Tasks
Reinforcement learning is a promising approach for learning control policies for robot tasks.
Synthesizing Programmatic Policies that Inductively Generalize
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.
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
Our algorithm uses importance weighting to correct for the shift from the training to the real-world distribution.
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
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.
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations
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
Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks.
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics
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.
Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive Shielding
Reinforcement learning is a promising approach to synthesizing policies for challenging robotics tasks.
Algorithms for Fairness in Sequential Decision Making
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.
Learning Interpretable Models with Causal Guarantees
We propose a framework for learning interpretable models from observational data that can be used to predict individual treatment effects (ITEs).
Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems
Reinforcement learning is a promising approach to learning robotics controllers.
Learning Neurosymbolic Generative Models via Program Synthesis
Significant strides have been made toward designing better generative models in recent years.
Probabilistic Verification of Fairness Properties via Concentration
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
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.
Interpretability via Model Extraction
The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions.
Interpreting Blackbox Models via Model Extraction
Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions.
Synthesizing Program Input Grammars
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
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.
Zero-Shot Learning Through Cross-Modal Transfer
This work introduces a model that can recognize objects in images even if no training data is available for the objects.
