Aniruddha R. Joshi

dblp:348/6600 · DBLP profile ↗
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4ranked-venue papers
1as first author
4since 2021 · last 2026
0000-0003-1884-7894ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 4 · 1 first-author · 4 since 2021Theory of computation · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
YearPublicationVenuePosition
2026 Program Synthesis for Non-linear Real Arithmetic: Going Beyond Realizability
abstract
Abstract We study the problem of synthesizing programs from non-linear real arithmetic () specifications. Existing techniques, such as syntax-guided synthesis (), fail to synthesize programs when the specification is unrealizable . We argue this is unsatisfactory in many situations, and aim to synthesize programs from arbitrary specifications, such that for any input, the synthesized program either produces outputs satisfying the specification or reports non-existence of any such output. To avoid rounding errors inherent in floating-point arithmetic, we restrict our programs to work on rational inputs and outputs. We first show that our variant of the synthesis problem is as hard as a long-standing open problem in number theory, and that synthesizing loop-free programs from arbitrary NRA specifications with rational inputs and outputs is impossible in general. Second, we present a sound and complete synthesis algorithm for the case where the specification involves a single output variable. We also show that for realizable specifications, a program generated by for (real inputs and outputs) serves as a solution to our problem, where inputs and outputs are rationals. Third, we provide a sound (but necessarily incomplete) synthesis algorithm for the general case of specifications. We have implemented our approach in a prototype tool called that solves many benchmarks beyond the reach of state-of-the-art SyGuS tools, even when we render the specifications realizable.
S. Akshay 0001, Supratik Chakraborty, R. Govind 0001, Aniruddha R. Joshi
IJCAR (1)4
2025 Locally Pareto-Optimal Interpretations for Black-Box Machine Learning Models
Aniruddha R. Joshi, Supratik Chakraborty, S. Akshay 0001, Shetal Shah, Hazem Torfah, Sanjit A. Seshia
ATVA1
2023 A Unified Model for Real-Time Systems: Symbolic Techniques and Implementation
abstract
Abstract In this paper, we consider a model of generalized timed automata (GTA) with two kinds of clocks, history and future, that can express many timed features succinctly, including timed automata, event-clock automata with and without diagonal constraints, and automata with timers. Our main contribution is a new simulation-based zone algorithm for checking reachability in this unified model. While such algorithms are known to exist for timed automata, and have recently been shown for event-clock automata without diagonal constraints, this is the first result that can handle event-clock automata with diagonal constraints and automata with timers. We also provide a prototype implementation for our model and show experimental results on several benchmarks. To the best of our knowledge, this is the first effective implementation not just for our unified model, but even just for automata with timers or for event-clock automata (with predicting clocks) without going through a costly translation via timed automata. Last but not least, beyond being interesting in their own right, generalized timed automata can be used for model-checking event-clock specifications over timed automata models.
S. Akshay 0001, Paul Gastin, R. Govind 0001, Aniruddha R. Joshi, B. Srivathsan
CAV (1)4
2023 Learning Monitor Ensembles for Operational Design Domains
Hazem Torfah, Aniruddha R. Joshi, Shetal Shah, S. Akshay 0001, Supratik Chakraborty, Sanjit A. Seshia
RV2