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Srinivasan Parthasarathy

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12 papers

1 author row

TMLR Journal 2025 Journal Article

Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs

Pranav Maneriker
Aditya T. Vadlamani
Anutam Srinivasan
Yuntian He
Ali Payani
Srinivasan Parthasarathy

Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has built upon this approach for conformal graph prediction. The nascent nature of these explorations has led to conflicting choices for implementations, baselines, and method evaluation. In this work, we analyze the design choices made in the literature and discuss the tradeoffs associated with existing methods. Building on the existing implementations for existing methods, we introduce techniques to scale existing methods to large-scale graph datasets without sacrificing performance. Our theoretical and empirical results justify our recommendations for future scholarship in graph conformal prediction.

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IS Journal 2024 Journal Article

Grounding From an AI and Cognitive Science Lens

Goonmeet Bajaj
Valerie L. Shalin
Srinivasan Parthasarathy
Amit Sheth

Grounding is a challenging problem, requiring a formal definition and different levels of abstraction. This article explores grounding from both cognitive science and machine learning perspectives. It identifies the subtleties of grounding, its significance for collaborative agents, and similarities and differences in grounding approaches in both communities. The article examines the potential of neurosymbolic approaches tailored for grounding tasks, showcasing how they can more comprehensively address grounding. Finally, we discuss areas for further exploration and development in grounding.

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TMLR Journal 2022 Journal Article

Benchmarking and Analyzing Unsupervised Network Representation Learning and the Illusion of Progress

Saket Gurukar
Priyesh Vijayan
Srinivasan Parthasarathy
Balaraman Ravindran
Aakash Srinivasan
Goonmeet Bajaj
Chen Cai
Moniba Keymanesh

A number of methods have been developed for unsupervised network representation learning -- ranging from classical methods based on the graph spectra to recent random walk based methods and from deep learning based methods to matrix factorization based methods. Each new study inevitably seeks to establish the relative superiority of the proposed method over others. The lack of a standard assessment protocol and benchmark suite often leave practitioners wondering if a new idea represents a significant scientific advance. In this work, we articulate a clear and pressing need to systematically and rigorously benchmark such methods. Our overall assessment -- a result of a careful benchmarking of 15 methods for unsupervised network representation learning on 16 non-attributed graphs (several with different characteristics) - is that many recently proposed improvements are somewhat of an illusion when assessed through the lens of downstream tasks such as link prediction and node classification. Specifically, we find that several proposed improvements are marginal at best and that aspects of many of these datasets often render such small differences insignificant, especially when viewed from a rigorous statistical lens. A more detailed analysis of our results identify several new insights: first, we find that classical methods, often dismissed or not considered by recent efforts, can compete on certain types of datasets if they are tuned appropriately; second, we find that from a qualitative standpoint, a couple of methods based on matrix factorization offer a small but not always consistent advantage over alternative methods; third, no single method completely outperforms other embedding methods on both node classification and link prediction tasks. Finally, we also present several analysis that reveals settings under which certain algorithms perform well (e.g., the role of neighborhood context and dataset properties that impact performance). An important outcome of this study is the benchmark and evaluation protocol, which practitioners may find useful for future research in this area.

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JAIR Journal 2021 Journal Article

A Tight Bound for Stochastic Submodular Cover

Lisa Hellerstein
Devorah Kletenik
Srinivasan Parthasarathy

We show that the Adaptive Greedy algorithm of Golovin and Krause achieves an approximation bound of (ln(Q/η)+1) for Stochastic Submodular Cover: here Q is the “goal value” and η is the minimum gap between Q and any attainable utility value Q' 2. A bound of 56(ln(Q/η)+1) is implied by work of Im et al. Other bounds for the problem depend on quantities other than Q and η. Our bound restores the original bound claimed by Golovin and Krause, generalizing the well-known (ln m + 1) approximation bound on the greedy algorithm for the classical Set Cover problem, where m is the size of the ground set.

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IJCAI Conference 2021 Conference Paper

Open Intent Extraction from Natural Language Interactions (Extended Abstract)

Nikhita Vedula
Nedim Lipka
Pranav Maneriker
Srinivasan Parthasarathy

Accurately discovering user intents from their written or spoken language plays a critical role in natural language understanding and automated dialog response. Most existing research models this as a classification task with a single intent label per utterance. Going beyond this formulation, we define and investigate a new problem of open intent discovery. It involves discovering one or more generic intent types from text utterances, that may not have been encountered during training. We propose a novel, domain-agnostic approach, OPINE, which formulates the problem as a sequence tagging task in an open-world setting. It employs a CRF on top of a bidirectional LSTM to extract intents in a consistent format, subject to constraints among intent tag labels. We apply multi-headed self-attention and adversarial training to effectively learn dependencies between distant words, and robustly adapt our model across varying domains. We also curate and release an intent-annotated dataset of 25K real-life utterances spanning diverse domains. Extensive experiments show that OPINE outperforms state-of-art baselines by 5-15% F1 score.

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IJCAI Conference 2020 Conference Paper

EndCold: An End-to-End Framework for Cold Question Routing in Community Question Answering Services

Jiankai Sun
Jie Zhao
Huan Sun
Srinivasan Parthasarathy

Routing newly posted questions (a. k. a cold questions) to potential answerers with suitable expertise in Community Question Answering sites (CQAs) is an important and challenging task. The existing methods either focus only on embedding the graph structural information and are less effective for newly posted questions, or adopt manually engineered feature vectors that are not as representative as the graph embedding methods. Therefore, we propose to address the challenge of leveraging heterogeneous graph and textual information for cold question routing by designing an end-to-end framework that jointly learns CQA node embeddings and finds best answerers for cold questions. We conducted extensive experiments to confirm the usefulness of incorporating the textual information from question tags and demonstrate that an end-2-end framework can achieve promising performances on routing newly posted questions asked by both existing users and newly registered users.

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AAAI Conference 2019 Conference Paper

ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation

Jiankai Sun
Bortik Bandyopadhyay
Armin Bashizade
Jiongqian Liang
P. Sadayappan
Srinivasan Parthasarathy

Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e. g. , question, answer, user. Asymmetric transitivity is an essential property of directed graphs, since it can play an important role in downstream graph inference and analysis. Question difficulty and user expertise follow the characteristic of asymmetric transitivity. Maintaining such properties, while reducing the graph to a lower dimensional vector embedding space, has been the focus of much recent research. In this paper, we tackle the challenge of directed graph embedding with asymmetric transitivity preservation and then leverage the proposed embedding method to solve a fundamental task in CQAs: how to appropriately route and assign newly posted questions to users with the suitable expertise and interest in CQAs. The technique incorporates graph hierarchy and reachability information naturally by relying on a nonlinear transformation that operates on the core reachability and implicit hierarchy within such graphs. Subsequently, the methodology levers a factorization-based approach to generate two embedding vectors for each node within the graph, to capture the asymmetric transitivity. Extensive experiments show that our framework consistently and significantly outperforms the state-of-the-art baselines on three diverse realworld tasks: link prediction, and question difficulty estimation and expert finding in online forums like Stack Exchange. Particularly, our framework can support inductive embedding learning for newly posted questions (unseen nodes during training), and therefore can properly route and assign these kinds of questions to experts in CQAs.

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IJCAI Conference 2019 Conference Paper

Optimal Exploitation of Clustering and History Information in Multi-armed Bandit

Djallel Bouneffouf
Srinivasan Parthasarathy
Horst Samulowitz
Martin Wistuba

We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms. The historical observations can contain any number of instances for each arm, and the pre-clustering information is a fixed clustering of arms provided as part of the input. We develop a variety of algorithms which incorporate this offline information effectively during the online exploration phase and derive their regret bounds. In particular, we develop the META algorithm which effectively hedges between two other algorithms: one which uses both historical observations and clustering, and another which uses only the historical observations. The former outperforms the latter when the clustering quality is good, and vice-versa. Extensive experiments on synthetic and real world datasets on Warafin drug dosage and web server selectionfor latency minimization validate our theoretical insights and demonstrate that META is a robust strategy for optimally exploiting the pre-clustering information.

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AAAI Conference 2019 Short Paper

Symmetrization for Embedding Directed Graphs

Jiankai Sun
Srinivasan Parthasarathy

In this paper, we propose to solve the directed graph embedding problem via a two stage approach: in the first stage, the graph is symmetrized in one of several possible ways, and in the second stage, the so-obtained symmetrized graph is embeded using any state-of-the-art (undirected) graph embedding algorithm. Note that it is not the objective of this paper to propose a new (undirected) graph embedding algorithm or discuss the strengths and weaknesses of existing ones; all we are saying is that whichever be the suitable graph embedding algorithm, it will fit in the above proposed symmetrization framework.

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NeurIPS Conference 2017 Conference Paper

Adaptive Bayesian Sampling with Monte Carlo EM

Anirban Roychowdhury
Srinivasan Parthasarathy

We present a novel technique for learning the mass matrices in samplers obtained from discretized dynamics that preserve some energy function. Existing adaptive samplers use Riemannian preconditioning techniques, where the mass matrices are functions of the parameters being sampled. This leads to significant complexities in the energy reformulations and resultant dynamics, often leading to implicit systems of equations and requiring inversion of high-dimensional matrices in the leapfrog steps. Our approach provides a simpler alternative, by using existing dynamics in the sampling step of a Monte Carlo EM framework, and learning the mass matrices in the M step with a novel online technique. We also propose a way to adaptively set the number of samples gathered in the E step, using sampling error estimates from the leapfrog dynamics. Along with a novel stochastic sampler based on Nos\'{e}-Poincar\'{e} dynamics, we use this framework with standard Hamiltonian Monte Carlo (HMC) as well as newer stochastic algorithms such as SGHMC and SGNHT, and show strong performance on synthetic and real high-dimensional sampling scenarios; we achieve sampling accuracies comparable to Riemannian samplers while being significantly faster.

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IJCAI Conference 2017 Conference Paper

Fast Change Point Detection on Dynamic Social Networks

Yu Wang
Aniket Chakrabarti
David Sivakoff
Srinivasan Parthasarathy

A number of real world problems in many domains (e. g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.

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IJCAI Conference 2005 Conference Paper

Mining Spatial Object Associations for Scientific Data

Hui Yang
Srinivasan Parthasarathy
Sameep

In this paper, we present efficient algorithms to discover spatial associations among features extracted from scientific datasets. In contrast to previous work in this area, features are modeled as geometric objects rather than points. We define multiple distance metrics that take into account objects’ extent. We have developed algorithms to discover two types of spatial association patterns in scientific data. We present experimental results to demonstrate the efficacy of our approach on real datasets drawn from the bioinformatic domain. We also highlight the importance of the discovered patterns by integrating the underlying domain knowledge.

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