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Yash Pote

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

Instance Dependent Testing of Samplers Using Interval Conditioning

Rishiraj Bhattacharyya
Sourav Chakraborty
Yash Pote
Uddalok Sarkar
Sayantan Sen

Sampling algorithms play a pivotal role in probabilistic AI. However, verifying if a sampler program indeed samples from the claimed distribution is a notoriously hard problem. Provably correct testers like Barbarik,Teq,Flash, Cubeprobe for testing of different kinds of samplers were proposed only in the last few years. All these testers focus on the worst-case efficiency, and do not support verification of samplers over infinite domains, a case occurring frequently in Astronomy, Finance, Network Security etc. In this work, we design the first tester of samplers with instance-dependent efficiency, allowing us to test samplers over natural numbers. Our tests are developed via a novel distance estimation algorithm between an unknown and a known probability distribution using an 'interval conditioning' framework. The core technical contribution is a new connection with probability mass estimation of a continuous distribution. The practical gains are also substantial—our experiments establish up to 1000× speedup over state-of-the-art testers.

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IJCAI Conference 2025 Conference Paper

Learning Probabilistic Temporal Logic Specifications for Stochastic Systems

Rajarshi Roy
Yash Pote
Dave Parker
Marta Kwiatkowska

There has been substantial progress in the inference of formal behavioural specifications from sample trajectories, for example using Linear Temporal Logic (LTL). However, these techniques cannot handle specifications that correctly characterise systems with stochastic behaviour, which occur commonly in reinforcement learning and formal verification. We consider the passive learning problem of inferring a Boolean combination of probabilistic LTL (PLTL) formulas from a set of Markov chains, classified as either positive or negative. We propose a novel learning algorithm that infers concise PLTL specifications, leveraging grammar-based enumeration, search heuristics, probabilistic model checking and Boolean set-cover procedures. We demonstrate the effectiveness of our algorithm in two use cases: learning from policies induced by RL algorithms and learning from variants of a probabilistic model. In both cases, our method automatically and efficiently extracts PLTL specifications that succinctly characterize the temporal differences between the policies or model variants.

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AAAI Conference 2025 Conference Paper

Towards Real-Time Approximate Counting

Yash Pote
Kuldeep S. Meel
Jiong Yang

Model counting is the task of counting the number of satisfying assignments of a Boolean formula. Since counting is intractable in general, most applications use (ε, δ)-approximations, where the output is within a (1+ε)-factor of the count with probability at least 1-δ. Many demanding applications make thousands of counting queries, and the state-of-the-art approximate counter, ApproxMC, makes hundreds of calls to SAT solvers to answer a single approximate counting query. The sheer number of SAT calls, poses a significant challenge to the existing approaches. In this work, we propose an approximation scheme, ApproxMC7, that is tailored to such demanding applications with low time limits. Compared to ApproxMC, ApproxMC7 makes 14× fewer SAT calls while providing the same guarantees as ApproxMC in the constant-factor regime. In an evaluation over 2,247 instances, ApproxMC7 solved 271 more and achieved a 2× speedup against ApproxMC.

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AAAI Conference 2024 Conference Paper

Testing Self-Reducible Samplers

Rishiraj Bhattacharyya
Sourav Chakraborty
Yash Pote
Uddalok Sarkar
Sayantan Sen

Samplers are the backbone of the implementations of any randomized algorithm. Unfortunately, obtaining an efficient algorithm to test the correctness of samplers is very hard to find. Recently, in a series of works, testers like Barbarik, Teq, Flash for testing of some particular kinds of samplers, like CNF-samplers and Horn-samplers, were obtained. However, their techniques have a significant limitation because one can not expect to use their methods to test for other samplers, such as perfect matching samplers or samplers for sampling linear extensions in posets. In this paper, we present a new testing algorithm that works for such samplers and can estimate the distance of a new sampler from a known sampler (say, the uniform sampler). Testing the identity of distributions is the heart of testing the correctness of samplers. This paper's main technical contribution is developing a new distance estimation algorithm for distributions over high-dimensional cubes using the recently proposed subcube conditioning sampling model. Given subcube conditioning access to an unknown distribution P, and a known distribution Q defined over an n-dimensional Boolean hypercube, our algorithm CubeProbeEst estimates the variation distance between P and Q within additive error using subcube conditional samples from P. Following the testing-via-learning paradigm, we also get a tester that distinguishes between the cases when P and Q are close or far in variation distance with high probability using subcube conditional samples. This estimation algorithm CubeProbeEst in the subcube conditioning sampling model helps us to design the first tester for self-reducible samplers. The correctness of the tester is formally proved. Moreover, we implement CubeProbeEst to test the quality of three samplers for sampling linear extensions in posets.

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NeurIPS Conference 2022 Conference Paper

On Scalable Testing of Samplers

Yash Pote
Kuldeep S Meel

In this paper we study the problem of testing of constrained samplers over high-dimensional distributions with $(\varepsilon, \eta, \delta)$ guarantees. Samplers are increasingly used in a wide range of safety-critical ML applications, and hence the testing problem has gained importance. For $n$-dimensional distributions, the existing state-of-the-art algorithm, $\mathsf{Barbarik2}$, has a worst case query complexity of exponential in $n$ and hence is not ideal for use in practice. Our primary contribution is an exponentially faster algorithm, $\mathsf{Barbarik3}$, that has a query complexity linear in $n$ and hence can easily scale to larger instances. We demonstrate our claim by implementing our algorithm and then comparing it against $\mathsf{Barbarik2}$. Our experiments on the samplers $\mathsf{wUnigen3}$ and $\mathsf{wSTS}$, find that $\mathsf{Barbarik3}$ requires $10\times$ fewer samples for $\mathsf{wUnigen3}$ and $450\times$ fewer samples for $\mathsf{wSTS}$ as compared to $\mathsf{Barbarik2}$.

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IJCAI Conference 2021 Conference Paper

Partition Function Estimation: A Quantitative Study

Durgesh Agrawal
Yash Pote
Kuldeep S. Meel

Probabilistic graphical models have emerged as a powerful modeling tool for several real-world scenarios where one needs to reason under uncertainty. A graphical model's partition function is a central quantity of interest, and its computation is key to several probabilistic reasoning tasks. Given the #P-hardness of computing the partition function, several techniques have been proposed over the years with varying guarantees on the quality of estimates and their runtime behavior. This paper seeks to present a survey of 18 techniques and a rigorous empirical study of their behavior across an extensive set of benchmarks. Our empirical study draws up a surprising observation: exact techniques are as efficient as the approximate ones, and therefore, we conclude with an optimistic view of opportunities for the design of approximate techniques with enhanced scalability. Motivated by the observation of an order of magnitude difference between the Virtual Best Solver and the best performing tool, we envision an exciting line of research focused on the development of portfolio solvers.

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IJCAI Conference 2019 Conference Paper

Phase Transition Behavior of Cardinality and XOR Constraints

Yash Pote
Saurabh Joshi
Kuldeep S. Meel

The runtime performance of modern SAT solvers is deeply connected to the phase transition behavior of CNF formulas. While CNF solving has witnessed significant runtime improvement over the past two decades, the same does not hold for several other classes such as the conjunction of cardinality and XOR constraints, denoted as CARD-XOR formulas. The problem of determining satisfiability of CARD-XOR formulas is a fundamental problem with wide variety of applications ranging from discrete integration in the field of artificial intelligence to maximum likelihood decoding in coding theory. The runtime behavior of random CARD-XOR formulas is unexplored in prior work. In this paper, we present the first rigorous empirical study to characterize the runtime behavior of 1-CARD-XOR formulas. We show empirical evidence of a surprising phase-transition that follows a non-linear tradeoff between CARD and XOR constraints.

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