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Shiva Kasiviswanathan

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7 papers

1 author row

TMLR Journal 2024 Journal Article

Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries

Patrick Chao
Patrick Blöbaum
Sapan Kirit Patel
Shiva Kasiviswanathan

We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available. Utilizing the recent developments in diffusion models, we introduce diffusion-based causal models (DCM) to learn causal mechanisms, that generate unique latent encodings. These encodings enable us to directly sample under interventions and perform abduction for counterfactuals. Diffusion models are a natural fit here, since they can encode each node to a latent representation that acts as a proxy for exogenous noise. Our empirical evaluations demonstrate significant improvements over existing state-of-the-art methods for answering causal queries. Furthermore, we provide theoretical results that offer a methodology for analyzing counterfactual estimation in general encoder-decoder models, which could be useful in settings beyond our proposed approach.

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NeurIPS Conference 2023 Conference Paper

Debiasing Conditional Stochastic Optimization

Lie He
Shiva Kasiviswanathan

In this paper, we study the conditional stochastic optimization (CSO) problem which covers a variety of applications including portfolio selection, reinforcement learning, robust learning, causal inference, etc. The sample-averaged gradient of the CSO objective is biased due to its nested structure, and therefore requires a high sample complexity for convergence. We introduce a general stochastic extrapolation technique that effectively reduces the bias. We show that for nonconvex smooth objectives, combining this extrapolation with variance reduction techniques can achieve a significantly better sample complexity than the existing bounds. Additionally, we develop new algorithms for the finite-sum variant of the CSO problem that also significantly improve upon existing results. Finally, we believe that our debiasing technique has the potential to be a useful tool for addressing similar challenges in other stochastic optimization problems.

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NeurIPS Conference 2022 Conference Paper

Uplifting Bandits

Yu-Guan Hsieh
Shiva Kasiviswanathan
Branislav Kveton

We introduce a new multi-armed bandit model where the reward is a sum of multiple random variables, and each action only alters the distributions of some of these variables. Upon taking an action, the agent observes the realizations of all variables. This model is motivated by marketing campaigns and recommender systems, where the variables represent outcomes on individual customers, such as clicks. We propose UCB-style algorithms that estimate the uplifts of the actions over a baseline. We study multiple variants of the problem, including when the baseline and affected variables are unknown, and prove sublinear regret bounds for all of these. In addition, we provide regret lower bounds that justify the necessity of our modeling assumptions. Experiments on synthetic and real-world datasets demonstrate the benefit of methods that estimate the uplifts over policies that do not use this structure.

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NeurIPS Conference 2021 Conference Paper

Collaborative Causal Discovery with Atomic Interventions

Raghavendra Addanki
Shiva Kasiviswanathan

We introduce a new Collaborative Causal Discovery problem, through which we model a common scenario in which we have multiple independent entities each with their own causal graph, and the goal is to simultaneously learn all these causal graphs. We study this problem without the causal sufficiency assumption, using Maximal Ancestral Graphs (MAG) to model the causal graphs, and assuming that we have the ability to actively perform independent single vertex (or atomic) interventions on the entities. If the $M$ underlying (unknown) causal graphs of the entities satisfy a natural notion of clustering, we give algorithms that leverage this property and recovers all the causal graphs using roughly logarithmic in $M$ number of atomic interventions per entity. These are significantly fewer than $n$ atomic interventions per entity required to learn each causal graph separately, where $n$ is the number of observable nodes in the causal graph. We complement our results with a lower bound and discuss various extensions of our collaborative setting.

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AAAI Conference 2018 Conference Paper

Verifying Properties of Binarized Deep Neural Networks

Nina Narodytska
Shiva Kasiviswanathan
Leonid Ryzhyk
Mooly Sagiv
Toby Walsh

Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binarized Neural Networks, using the well-developed means of Boolean satisﬁability. Our main contribution is a construction that creates a representation of a binarized neural network as a Boolean formula. Our encoding is the ﬁrst exact Boolean representation of a deep neural network. Using this encoding, we leverage the power of modern SAT solvers along with a proposed counterexample-guided search procedure to verify various properties of these networks. A particular focus will be on the critical property of robustness to adversarial perturbations. For this property, our experimental results demonstrate that our approach scales to medium-size deep neural networks used in image classiﬁcation tasks. To the best of our knowledge, this is the ﬁrst work on verifying properties of deep neural networks using an exact Boolean encoding of the network.

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AAAI Conference 2015 Conference Paper

Online Dictionary Learning on Symmetric Positive Definite Manifolds with Vision Applications

Shengping Zhang
Shiva Kasiviswanathan
Pong Yuen
Mehrtash Harandi

Symmetric Positive Deﬁnite (SPD) matrices in the form of region covariances are considered rich descriptors for images and videos. Recent studies suggest that exploiting the Riemannian geometry of the SPD manifolds could lead to improved performances for vision applications. For tasks involving processing large-scale and dynamic data in computer vision, the underlying model is required to progressively and efﬁciently adapt itself to the new and unseen observations. Motivated by these requirements, this paper studies the problem of online dictionary learning on the SPD manifolds. We make use of the Stein divergence to recast the problem of online dictionary learning on the manifolds to a problem in Reproducing Kernel Hilbert Spaces, for which, we develop efﬁcient algorithms by taking into account the geometric structure of the SPD manifolds. To our best knowledge, our work is the ﬁrst study that provides a solution for online dictionary learning on the SPD manifolds. Empirical results on both large-scale image classiﬁcation task and dynamic video processing tasks validate the superior performance of our approach as compared to several state-of-the-art algorithms.

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NeurIPS Conference 2012 Conference Paper

Online L1-Dictionary Learning with Application to Novel Document Detection

Shiva Kasiviswanathan
Huahua Wang
Arindam Banerjee
Prem Melville

Given their pervasive use, social media, such as Twitter, have become a leading source of breaking news. A key task in the automated identification of such news is the detection of novel documents from a voluminous stream of text documents in a scalable manner. Motivated by this challenge, we introduce the problem of online L1-dictionary learning where unlike traditional dictionary learning, which uses squared loss, the L1-penalty is used for measuring the reconstruction error. We present an efficient online algorithm for this problem based on alternating directions method of multipliers, and establish a sublinear regret bound for this algorithm. Empirical results on news-stream and Twitter data, shows that this online L1-dictionary learning algorithm for novel document detection gives more than an order of magnitude speedup over the previously known batch algorithm, without any significant loss in quality of results. Our algorithm for online L1-dictionary learning could be of independent interest.

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