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Shankar Sastry

Shankar Sastry appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Machine LearningMultiagent Systemsquant-phSystems and ControlComputer Science and Game TheoryComputer Visionecon.THeess.SYArtificial Intelligenceecon.GNNetworking and Internet Architectureq-fin.EC

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Published work

12 published item(s)

preprint2026arXiv

TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos

We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides $140+$ hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featuring multimodal annotations like high-quality camera calibrations, precise 3D ball positions, ball spin, time segmentation, and 3D human meshes over time. This rich data provides a new foundation for virtual replay, in-depth player analysis, and robot learning. The dataset's combination of scale and precision is achieved through a novel reconstruction pipeline. Prior methods first partition a game sequence into individual shot segments based on the 2D ball track, and only then attempt reconstruction. However, 2D-based time segmentation collapses under occlusion and varied camera viewpoints, preventing reliable reconstruction. We invert this paradigm by first lifting the entire unsegmented 2D ball track to 3D through a learned lifting network. This 3D trajectory then allows us to reliably perform time segmentation. The learned lifting network also infers the ball's spin, handles unreliable ball detections, and successfully reconstructs the ball trajectory in cases of high occlusion. This lift-first design is necessary, as our pipeline is the only method capable of reconstructing table tennis gameplay from general-view broadcast monocular videos. We demonstrate the dataset's fidelity through two downstream tasks: estimating the racket's pose \& velocity at impact, and training a generative model of competitive rallies.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Competing Bandits in Time Varying Matching Markets

We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match. In particular, we consider the setting where one side of the market, the arms, has fixed known set of preferences over the other side, the players. While this problem has been studied when the players have fixed but unknown preferences, in this work we study the problem of how to learn when the preferences of the players are time varying and unknown. Our contribution is a methodology that can handle any type of preference structure and variation scenario. We show that, with the proposed algorithm, each player receives a uniform sub-linear regret of {$\widetilde{\mathcal{O}}(L^{1/2}_TT^{1/2})$} up to the number of changes in the underlying preferences of the agents, $L_T$. Therefore, we show that the optimal rates for single-agent learning can be achieved in spite of the competition up to a difference of a constant factor. We also discuss extensions of this algorithm to the case where the number of changes need not be known a priori.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets

We study the problem of online learning in competitive settings in the context of two-sided matching markets. In particular, one side of the market, the agents, must learn about their preferences over the other side, the firms, through repeated interaction while competing with other agents for successful matches. We propose a class of decentralized, communication- and coordination-free algorithms that agents can use to reach to their stable match in structured matching markets. In contrast to prior works, the proposed algorithms make decisions based solely on an agent's own history of play and requires no foreknowledge of the firms' preferences. Our algorithms are constructed by splitting up the statistical problem of learning one's preferences, from noisy observations, from the problem of competing for firms. We show that under realistic structural assumptions on the underlying preferences of the agents and firms, the proposed algorithms incur a regret which grows at most logarithmically in the time horizon. Our results show that, in the case of matching markets, competition need not drastically affect the performance of decentralized, communication and coordination free online learning algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Inducing Social Optimality in Games via Adaptive Incentive Design

How can a social planner adaptively incentivize selfish agents who are learning in a strategic environment to induce a socially optimal outcome in the long run? We propose a two-timescale learning dynamics to answer this question in both atomic and non-atomic games. In our learning dynamics, players adopt a class of learning rules to update their strategies at a faster timescale, while a social planner updates the incentive mechanism at a slower timescale. In particular, the update of the incentive mechanism is based on each player's externality, which is evaluated as the difference between the player's marginal cost and the society's marginal cost in each time step. We show that any fixed point of our learning dynamics corresponds to the optimal incentive mechanism such that the corresponding Nash equilibrium also achieves social optimality. We also provide sufficient conditions for the learning dynamics to converge to a fixed point so that the adaptive incentive mechanism eventually induces a socially optimal outcome. Finally, we demonstrate that the sufficient conditions for convergence are satisfied in a variety of games, including (i) atomic networked quadratic aggregative games, (ii) atomic Cournot competition, and (iii) non-atomic network routing games.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Simultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization

Simultaneous Localization and Mapping (SLAM) algorithms perform visual-inertial estimation via filtering or batch optimization methods. Empirical evidence suggests that filtering algorithms are computationally faster, while optimization methods are more accurate. This work presents an optimization-based framework that unifies these approaches, and allows users to flexibly implement different design choices, e.g., the number and types of variables maintained in the algorithm at each time. We prove that filtering methods correspond to specific design choices in our generalized framework. We then reformulate the Multi-State Constrained Kalman Filter (MSCKF), implement the reformulation on challenging image sequence datasets in simulation, and contrast its performance with that of sliding window based filters. Using these results, we explain the relative performance characteristics of these two classes of algorithms in the context of our algorithm. Finally, we illustrate that under different design choices, the empirical performance of our algorithm interpolates between those of state-of-the-art approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Approximate Subspace-Sparse Recovery with Corrupted Data via Constrained $\ell_1$-Minimization

High-dimensional data often lie in low-dimensional subspaces corresponding to different classes they belong to. Finding sparse representations of data points in a dictionary built using the collection of data helps to uncover low-dimensional subspaces and address problems such as clustering, classification, subset selection and more. In this paper, we address the problem of recovering sparse representations for noisy data points in a dictionary whose columns correspond to corrupted data lying close to a union of subspaces. We consider a constrained $\ell_1$-minimization and study conditions under which the solution of the proposed optimization satisfies the approximate subspace-sparse recovery condition. More specifically, we show that each noisy data point, perturbed from a subspace by a noise of the magnitude of $\varepsilon$, will be reconstructed using data points from the same subspace with a small error of the order of $O(\varepsilon)$ and that the coefficients corresponding to data points in other subspaces will be sufficiently small, \ie, of the order of $O(\varepsilon)$. We do not impose any randomness assumption on the arrangement of subspaces or distribution of data points in each subspace. Our framework is based on a novel generalization of the null-space property to the setting where data lie in multiple subspaces, the number of data points in each subspace exceeds the dimension of the subspace, and all data points are corrupted by noise. Moreover, assuming a random distribution for data points, we further show that coefficients from the desired support not only reconstruct a given point with high accuracy, but also have sufficiently large values, \ie, of the order of $O(1)$.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Safe Sequential Path Planning of Multi-Vehicle Systems via Double-Obstacle Hamilton-Jacobi-Isaacs Variational Inequality

We consider the problem of planning trajectories for a group of $N$ vehicles, each aiming to reach its own target set while avoiding danger zones of other vehicles. The analysis of problems like this is extremely important practically, especially given the growing interest in utilizing unmanned aircraft systems for civil purposes. The direct solution of this problem by solving a single-obstacle Hamilton-Jacobi-Isaacs (HJI) variational inequality (VI) is numerically intractable due to the exponential scaling of computation complexity with problem dimensionality. Furthermore, the single-obstacle HJI VI cannot directly handle situations in which vehicles do not have a common scheduled arrival time. Instead, we perform sequential path planning by considering vehicles in order of priority, modeling higher-priority vehicles as time-varying obstacles for lower-priority vehicles. To do this, we solve a double-obstacle HJI VI which allows us to obtain the reach-avoid set, defined as the set of states from which a vehicle can reach its target while staying within a time-varying state constraint set. From the solution of the double-obstacle HJI VI, we can also extract the latest start time and the optimal control for each vehicle. This is a first application of the double-obstacle HJI VI which can handle systems with time-varying dynamics, target sets, and state constraint sets, and results in computation complexity that scales linearly, as opposed to exponentially, with the number of vehicles in consideration.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Complete Characterization of Mixing Time for the Continuous Quantum Walk on the Hypercube with Markovian Decoherence Model

The n-dimensional hypercube quantum random walk (QRW) is a particularily appealing example of a quantum walk because it has a natural implementation on a register on $n$ qubits. However, any real implementation will encounter decoherence effects due to interactions with uncontrollable degrees of freedom. We present a complete characterization of the mixing properties of the hypercube QRW under a physically relevant Markovian decoherence model. In the local decoherence model considered the non-unitary dynamics are modeled as a sum of projections on individual qubits to an arbitrary direction on the Bloch sphere. We prove that there is always classical (asymptotic) mixing in this model and specify the conditions under which instantaneous mixing \textit{always} exists. And we show that the latter mixing property, as well as the classical mixing time, depend heavily on the exact environmental interaction and its strength. Therefore, algorithmic applications of the QRW on the hypercube, if they intend to employ mixing properties, need to consider both the walk dynamics and the precise decoherence model.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Generalized Principal Component Analysis (GPCA)

This paper presents an algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a data point give normal vectors to the subspace passing through the point. When the number of subspaces is known, we show that these polynomials can be estimated linearly from data; hence, subspace segmentation is reduced to classifying one point per subspace. We select these points optimally from the data set by minimizing certain distance function, thus dealing automatically with moderate noise in the data. A basis for the complement of each subspace is then recovered by applying standard PCA to the collection of derivatives (normal vectors). Extensions of GPCA that deal with data in a high- dimensional space and with an unknown number of subspaces are also presented. Our experiments on low-dimensional data show that GPCA outperforms existing algebraic algorithms based on polynomial factorization and provides a good initialization to iterative techniques such as K-subspaces and Expectation Maximization. We also present applications of GPCA to computer vision problems such as face clustering, temporal video segmentation, and 3D motion segmentation from point correspondences in multiple affine views.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Multi-path Routing Metrics for Reliable Wireless Mesh Routing Topologies

Several emerging classes of applications that run over wireless networks have a need for mathematical models and tools to systematically characterize the reliability of the network. We propose two metrics for measuring the reliability of wireless mesh routing topologies, one for flooding and one for unicast routing. The Flooding Path Probability (FPP) metric measures the end-to-end packet delivery probability when each node broadcasts a packet after hearing from all its upstream neighbors. The Unicast Retransmission Flow (URF) metric measures the end-to-end packet delivery probability when a relay node retransmits a unicast packet on its outgoing links until it receives an acknowledgement or it tries all the links. Both metrics rely on specific packet forwarding models, rather than heuristics, to derive explicit expressions of the end-to-end packet delivery probability from individual link probabilities and the underlying connectivity graph. We also propose a distributed, greedy algorithm that uses the URF metric to construct a reliable routing topology. This algorithm constructs a Directed Acyclic Graph (DAG) from a weighted, undirected connectivity graph, where each link is weighted by its success probability. The algorithm uses a vector of decreasing reliability thresholds to coordinate when nodes can join the routing topology. Simulations demonstrate that, on average, this algorithm constructs a more reliable topology than the usual minimum hop DAG.

0 citations0 reviewsNo code linkedOpen access
preprint2007arXiv

On the Quantum Circuit Complexity Equivalence

Nielsen \cite{Nielsen05} recently asked the following question: "What is the minimal size quantum circuit required to exactly implement a specified $% \mathit{n}$-qubit unitary operation $U$, without the use of ancilla qubits?" Nielsen was able to prove that a lower bound on the minimal size circuit is provided by the length of the geodesic between the identity $I$ and $U$, where the length is defined by a suitable Finsler metric on $SU(2^{n})$. We prove that the minimum circuit size that simulates $U$ is in linear relation with the geodesic length and simulation parameters, for the given Finsler structure $F$. As a corollary we prove the highest lower bound of $O(\frac{% n^{4}}{p}d_{F_{p}}^{2}(I,U)L_{F_{p}}(I,\tilde{U})) $and the lowest upper bound of $Ω(n^{4}d_{F_{p}}^{3}(I,U))$, for the standard simulation technique. Therefore, our results show that by standard simulation one can not expect a better then $n^{2}$ times improvement in the upper bound over the result from Nielsen, Dowling, Gu and Doherty \cite{Nielsen06}. Moreover, our equivalence result can be applied to the arbitrary path on the manifold including the one that is generated adiabatically.

0 citations0 reviewsNo code linkedOpen access
preprint2002arXiv

A geometric theory of non-local two-qubit operations

We study non-local two-qubit operations from a geometric perspective. By applying a Cartan decomposition to su(4), we find that the geometric structure of non-local gates is a 3-Torus. We derive the invariants for local transformations, and connect these local invariants to the coordinates of the 3-Torus. Since different points on the 3-Torus may correspond to the same local equivalence class, we use the Weyl group theory to reduce the symmetry. We show that the local equivalence classes of two-qubit gates are in one-to-one correspondence with the points in a tetrahedron except on the base. We then study the properties of perfect entanglers, that is, the two-qubit operations that can generate maximally entangled states from some initially separable states. We provide criteria to determine whether a given two-qubit gate is a perfect entangler and establish a geometric description of perfect entanglers by making use of the tetrahedral representation of non-local gates. We find that exactly half the non-local gates are perfect entanglers. We also investigate the non-local operations generated by a given Hamiltonian. We first study the gates that can be directly generated by a Hamiltonian. Then we explicitly construct a quantum circuit that contains at most three non-local gates generated by a two-body interaction Hamiltonian, together with at most four local gates generated by single qubit terms. We prove that such a quantum circuit can simulate any arbitrary two-qubit gate exactly, and hence it provides an efficient implementation of universal quantum computation and simulation.

0 citations0 reviewsNo code linkedOpen access