Rohit Singh

AIJ Journal 2024 Journal Article

An extensive study of security games with strategic informants

• Weiran Shen
• Minbiao Han
• Weizhe Chen
• Taoan Huang
• Rohit Singh
• Haifeng Xu
• Fei Fang

TMLR Journal 2023 Journal Article

Causally-guided Regularization of Graph Attention Improves Generalizability

• Alexander P Wu
• Thomas Markovich
• Bonnie Berger
• Nils Yannick Hammerla
• Rohit Singh

Graph attention networks estimate the relational importance of node neighbors to aggregate relevant information over local neighborhoods for a prediction task. However, the inferred attentions are vulnerable to spurious correlations and connectivity in the training data, hampering the generalizability of models. We introduce CAR, a general-purpose regularization framework for graph attention networks. Embodying a causal inference approach based on invariance prediction, CAR aligns the attention mechanism with the causal effects of active interventions on graph connectivity in a scalable manner. CAR is compatible with a variety of graph attention architectures, and we show that it systematically improves generalizability on various node classification tasks. Our ablation studies indicate that CAR hones in on the aspects of graph structure most pertinent to the prediction (e.g., homophily), and does so more effectively than alternative approaches. Finally, we also show that \methodname enhances interpretability of attention coefficients by accentuating node-neighbor relations that point to causal hypotheses.

IJCAI Conference 2020 Conference Paper

When to Follow the Tip: Security Games with Strategic Informants

• Weiran Shen
• Weizhe Chen
• Taoan Huang
• Rohit Singh
• Fei Fang

Although security games have attracted intensive research attention over the past years, few existing works consider how information from local communities would affect the game. In this paper, we introduce a new player -- a strategic informant, who can observe and report upcoming attacks -- to the defender-attacker security game setting. Characterized by a private type, the informant has his utility structure that leads to his strategic behaviors. We model the game as a 3-player extensive-form game and propose a novel solution concept of Strong Stackelberg-perfect Bayesian equilibrium. To compute the optimal defender strategy, we first show that although the informant can have infinitely many types in general, the optimal defense plan can only include a finite (exponential) number of different patrol strategies. We then prove that there exists a defense plan with only a linear number of patrol strategies that achieve the optimal defender's utility, which significantly reduces the computational burden and allows us to solve the game in polynomial time using linear programming. Finally, we conduct extensive experiments to show the effect of the strategic informant and demonstrate the effectiveness of our algorithm.

AAAI Conference 2019 Conference Paper

Deep Reinforcement Learning for Green Security Games with Real-Time Information

• Yufei Wang
• Zheyuan Ryan Shi
• Lantao Yu
• Yi Wu
• Rohit Singh
• Lucas Joppa
• Fei Fang

Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing. However, real-time information such as footprints and agents’ subsequent actions upon receiving the information, e. g. , rangers following the footprints to chase the poacher, have been neglected in previous work. To fill the gap, we first propose a new game model GSG-I which augments GSGs with sequential movement and the vital element of real-time information. Second, we design a novel deep reinforcement learning-based algorithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding attacker. DeDOL is built upon the double oracle framework and the policy-space response oracle, solving a restricted game and iteratively adding best response strategies to it through training deep Q-networks. Exploring the game structure, DeDOL uses domain-specific heuristic strategies as initial strategies and constructs several local modes for efficient and parallelized training. To our knowledge, this is the first attempt to use Deep Q-Learning for security games.

JMLR Journal 2019 Journal Article

Pyro: Deep Universal Probabilistic Programming

• Eli Bingham
• Jonathan P. Chen
• Martin Jankowiak
• Fritz Obermeyer
• Neeraj Pradhan
• Theofanis Karaletsos
• Rohit Singh
• Paul Szerlip

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2019. ( edit, beta )

IJCAI Conference 2018 Conference Paper

Designing the Game to Play: Optimizing Payoff Structure in Security Games

• Zheyuan Ryan Shi
• Ziye Tang
• Long Tran-Thanh
• Rohit Singh
• Fei Fang

We study Stackelberg Security Games where the defender, in addition to allocating defensive resources to protect targets from the attacker, can strategically manipulate the attacker’s payoff under budget constraints in weighted L^p-norm form regarding the amount of change. For the case of weighted L^1-norm constraint, we present (i) a mixed integer linear program-based algorithm with approximation guarantee; (ii) a branch-and-bound based algorithm with improved efficiency achieved by effective pruning; (iii) a polynomial time approximation scheme for a special but practical class of problems. In addition, we show that problems under budget constraints in L^0 and weighted L^\infty-norm form can be solved in polynomial time.

AAAI Conference 2012 Conference Paper

Automatically Generating Algebra Problems

• Rohit Singh
• Sumit Gulwani
• Sriram Rajamani

We propose computer-assisted techniques for helping with pedagogy in Algebra. In particular, given a proof problem p (of the form Left-hand-side-term = Righthand-side-term), we show how to automatically generate problems that are similar to p. We believe that such a tool can be used by teachers in making examinations where they need to test students on problems similar to what they taught in class, and by students in generating practice problems tailored to their specific needs. Our first insight is that we can generalize p syntactically to a query Q that implicitly represents a set of problems [[Q]] (which includes p). Our second insight is that we can explore the space of problems [[Q]] automatically, use classical results from polynomial identity testing to generate only those problems in [[Q]] that are correct, and then use pruning techniques to generate only unique and interesting problems. Our third insight is that with a small amount of manual tuning on the query Q, the user can interactively guide the computer to generate problems of interest to her. We present the technical details of the above mentioned steps, and also describe a tool where these steps have been implemented. We also present an empirical evaluation on a wide variety of problems from various sub-fields of algebra including polynomials, trigonometry, calculus, determinants etc. Our tool is able to generate a rich corpus of similar problems from each given problem; while some of these similar problems were already present in the textbook, several were new!