no code implementations • 6 Aug 2024 • Lekai Chen, Ashutosh Trivedi, Alvaro Velasquez

The emergence of intelligence in large language models (LLMs) has inspired investigations into their integration into automata learning.

no code implementations • 22 May 2024 • Alireza Nadali, Ashutosh Trivedi, Majid Zamani

Control barrier certificates have proven effective in formally guaranteeing the safety of the control systems.

no code implementations • 29 Apr 2024 • Salvador Robles Herrera, Verya Monjezi, Vladik Kreinovich, Ashutosh Trivedi, Saeid Tizpaz-Niari

However, the precision depends on the ML training algorithm, dataset, and protected attributes.

no code implementations • 3 Apr 2024 • Ashutosh Gupta, John Komp, Abhay Singh Rajput, Krishna Shankaranarayanan, Ashutosh Trivedi, Namrita Varshney

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.

1 code implementation • 22 Jan 2024 • Richard Roberson, Gowtham Kaki, Ashutosh Trivedi

This study investigates various approaches to using Large Language Models (LLMs) for Text-to-SQL program synthesis, focusing on the outcomes and insights derived.

no code implementations • 15 Dec 2023 • Milad Kazemi, Mateo Perez, Fabio Somenzi, Sadegh Soudjani, Ashutosh Trivedi, Alvaro Velasquez

We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel.

no code implementations • 14 Dec 2023 • Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak

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).

no code implementations • 20 Nov 2023 • Dananjay Srinivas, Rohan Das, Saeid Tizpaz-Niari, Ashutosh Trivedi, Maria Leonor Pacheco

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.

no code implementations • 18 Oct 2023 • Mateo Perez, Fabio Somenzi, Ashutosh Trivedi

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.

no code implementations • 14 Aug 2023 • Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak

Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success.

no code implementations • 27 May 2023 • Vishnu Murali, Ashutosh Trivedi, Majid Zamani

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

no code implementations • 26 May 2023 • Rajeev Alur, Osbert Bastani, Kishor Jothimurugan, Mateo Perez, Fabio Somenzi, Ashutosh Trivedi

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).

no code implementations • 9 Apr 2023 • Verya Monjezi, Ashutosh Trivedi, Gang Tan, Saeid Tizpaz-Niari

Guided by the quantitative fairness, we present a causal debugging framework to localize inadequately trained layers and neurons responsible for fairness defects.

no code implementations • 16 Mar 2023 • Amin Falah, Shibashis Guha, Ashutosh Trivedi

We consider CTMDP environments against the learning objectives expressed as omega-regular languages.

no code implementations • 27 Feb 2023 • Taylor Dohmen, Ashutosh Trivedi

A basic assumption of traditional reinforcement learning is that the value of a reward does not change once it is received by an agent.

no code implementations • 6 Aug 2022 • Abolfazl Lavaei, Mateo Perez, Milad Kazemi, Fabio Somenzi, Sadegh Soudjani, Ashutosh Trivedi, Majid Zamani

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.

no code implementations • 23 Jun 2022 • Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak

Recursion is the fundamental paradigm to finitely describe potentially infinite objects.

no code implementations • 6 May 2022 • Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak

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.

2 code implementations • 13 Feb 2022 • Saeid Tizpaz-Niari, Ashish Kumar, Gang Tan, Ashutosh Trivedi

This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs.

no code implementations • 9 Jul 2021 • Taylor Dohmen, Noah Topper, George Atia, Andre Beckus, Ashutosh Trivedi, Alvaro Velasquez

The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies.

no code implementations • 16 Jun 2021 • Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak

Reinforcement learning synthesizes controllers without prior knowledge of the system.

no code implementations • 12 Jun 2021 • Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak

We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs).

no code implementations • 5 Jun 2021 • Alvaro Velasquez, Ismail Alkhouri, Andre Beckus, Ashutosh Trivedi, George Atia

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.

no code implementations • 12 May 2021 • Mahathi Anand, Vishnu Murali, Ashutosh Trivedi, Majid Zamani

These verification conditions are then discharged by synthesizing so-called augmented barrier certificates, which provide certain safety guarantees for the underlying system.

no code implementations • 3 Jun 2020 • Saeid Tizpaz-Niari, Pavol Cerný, Ashutosh Trivedi

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.

no code implementations • 2 Mar 2020 • Abolfazl Lavaei, Fabio Somenzi, Sadegh Soudjani, Ashutosh Trivedi, Majid Zamani

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.

no code implementations • 23 Jul 2019 • Saeid Tizpaz-Niari, Pavol Cerny, Sriram Sankaranarayanan, Ashutosh Trivedi

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.

no code implementations • 21 Jun 2019 • Saeid Tizpaz-Niari, Pavol Cerny, Ashutosh Trivedi

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.

no code implementations • 30 Aug 2018 • Saeid Tizpaz-Niari, Pavol Cerny, Ashutosh Trivedi

On the realistic programs, we show the scalability of FUCHSIA in analyzing functional side channels in Java programs with thousands of methods.

no code implementations • 11 Nov 2017 • Saeid Tizpaz-Niari, Pavol Cerny, Bor-Yuh Evan Chang, Ashutosh Trivedi

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.

no code implementations • 23 Feb 2017 • Saeid Tizpaz-Niari, Pavol Cerny, Bor-Yuh Evan Chang, Sriram Sankaranarayanan, Ashutosh Trivedi

What properties about the internals of a program explain the possible differences in its overall running time for different inputs?

no code implementations • 13 Dec 2016 • Mehdi Kargahi, Ashutosh Trivedi

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.

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