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Found 30 papers, 2 papers with code
Predicting Fairness of ML Software Configuration
However, the precision depends on the ML training algorithm, dataset, and protected attributes.
Integrating Explanations in Learning LTL Specifications from Demonstrations
This paper investigates whether recent advances in Large Language Models (LLMs) can assist in translating human explanations into a format that can robustly support learning Linear Temporal Logic (LTL) from demonstrations.
Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis
This study investigates various approaches to using Large Language Models (LLMs) for Text-to-SQL program synthesis, focusing on the outcomes and insights derived.
Assume-Guarantee Reinforcement Learning
We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel.
Omega-Regular Decision Processes
Regular decision processes (RDPs) are a subclass of non-Markovian decision processes where the transition and reward functions are guarded by some regular property of the past (a lookback).
On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software
Due to the ever-increasing complexity of income tax laws in the United States, the number of US taxpayers filing their taxes using tax preparation software (henceforth, tax software) continues to increase.
A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs
Linear temporal logic (LTL) and omega-regular objectives -- a superset of LTL -- have seen recent use as a way to express non-Markovian objectives in reinforcement learning.
Omega-Regular Reward Machines
Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success.
Closure Certificates
A barrier certificate, defined over the states of a dynamical system, is a real-valued function whose zero level set characterizes an inductively verifiable state invariant separating reachable states from unsafe ones.
Logic in Computer Science Systems and Control Systems and Control
Policy Synthesis and Reinforcement Learning for Discounted LTL
The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objectives for reinforcement learning (RL).
Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks
Guided by the quantitative fairness, we present a causal debugging framework to localize inadequately trained layers and neurons responsible for fairness defects.
Reinforcement Learning for Omega-Regular Specifications on Continuous-Time MDP
We consider CTMDP environments against the learning objectives expressed as omega-regular languages.
Reinforcement Learning with Depreciating Assets
A basic assumption of traditional reinforcement learning is that the value of a reward does not change once it is received by an agent.
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems
A key contribution is to leverage the convergence results for adversarial RL (minimax Q-learning) on finite stochastic arenas to provide control strategies maximizing the probability of satisfaction over the network of continuous-space systems.
Recursive Reinforcement Learning
Recursion is the fundamental paradigm to finitely describe potentially infinite objects.
Alternating Good-for-MDP Automata
The surprising answer is that we have to pay significantly less when we instead expand the good-for-MDP property to alternating automata: like the nondeterministic GFM automata obtained from deterministic Rabin automata, the alternating good-for-MDP automata we produce from deterministic Streett automata are bi-linear in the the size of the deterministic automaton and its index, and can therefore be exponentially more succinct than minimal nondeterministic B\"uchi automata.
Fairness-aware Configuration of Machine Learning Libraries
This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs.
Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning
The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies.
Mungojerrie: Reinforcement Learning of Linear-Time Objectives
Reinforcement learning synthesizes controllers without prior knowledge of the system.
Model-free Reinforcement Learning for Branching Markov Decision Processes
We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs).
Controller Synthesis for Omega-Regular and Steady-State Specifications
Given a Markov decision process (MDP) and a linear-time ($\omega$-regular or LTL) specification, the controller synthesis problem aims to compute the optimal policy that satisfies the specification.
Verification of Hyperproperties for Uncertain Dynamical Systems via Barrier Certificates
These verification conditions are then discharged by synthesizing so-called augmented barrier certificates, which provide certain safety guarantees for the underlying system.
Detecting and Understanding Real-World Differential Performance Bugs in Machine Learning Libraries
On a set of micro-benchmarks, we show that our approach outperforms state-of-the-art fuzzers in finding inputs to characterize the differential performance.
Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning
A key contribution of the paper is to leverage the classical convergence results for reinforcement learning on finite MDPs and provide control strategies maximizing the probability of satisfaction over unknown, continuous-space MDPs while providing probabilistic closeness guarantees.
Efficient Detection and Quantification of Timing Leaks with Neural Networks
As demonstrated in our experiments, both of these tasks are feasible in practice --- making the approach a significant improvement over the state-of-the-art side channel detectors and quantifiers.
Quantitative Mitigation of Timing Side Channels
In contrast to the existing mitigation approaches, we show that in the functional-observation threat model, SCHMIT is scalable and able to maximize confidentiality under the performance overhead bound.
Data-Driven Debugging for Functional Side Channels
On the realistic programs, we show the scalability of FUCHSIA in analyzing functional side channels in Java programs with thousands of methods.
Differential Performance Debugging with Discriminant Regression Trees
We propose a data-driven technique based on discriminant regression tree (DRT) learning problem where the goal is to discriminate among different classes of inputs.
Discriminating Traces with Time
What properties about the internals of a program explain the possible differences in its overall running time for different inputs?
Proceedings of the The First Workshop on Verification and Validation of Cyber-Physical Systems
The first International Workshop on Verification and Validation of Cyber-Physical Systems (V2CPS-16) was held in conjunction with the 12th International Conference on integration of Formal Methods (iFM 2016) in Reykjavik, Iceland.
