Marked Temporal Point Process (MTPP) -- the de-facto sequence model for continuous-time event sequences -- historically employed for modeling human-generated action sequences, lack awareness of external stimuli. In this study, we propose a novel framework developed over Transformer Hawkes Process (THP) to incorporate external stimuli in a domain-agnostic manner. Furthermore, we integrate personalization into our framework by employing language model-based representations of user and event descriptions, which is essential for modeling human-generated action sequences. Towards evaluating the efficacy, we put together a comprehensive benchmark comprising 5 datasets (2 novel additions, and 3 repurposed from existing open datasets) harvested from several domains, spanning education, e-commerce, online payment, and discussion forum. On average, we achieve 9.35% gain in type-prediction accuracy and 7.38% reduction in time-prediction RMSE across all datasets over SOTA MTPP baselines. We demonstrate the superior performance of our proposed model through extensive ablations and showcasing its ability to capture complex combinations of external stimuli in a synthetic set up.

Soham Poddar, Paramita Koley, Janardan Misra, Niloy Ganguly, Saptarshi Ghosh. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

In this paper, we consider the problem of global change-point detection in event sequence data, where both the event distributions and change-points are assumed to be unknown. For this problem, we propose a Log-likelihood Ratio based Global Change-point Detector, which observes the entire sequence and detects a prespecified number of change-points. Based on the Transformer Hawkes Process (THP), a well-known neural TPP framework, we develop DCPD, a differentiable change-point detector, along with maintaining distinct intensity and mark predictor for each partition. Further, we propose a sliding-window-based extension of DCPD to improve its scalability in terms of the number of events or change-points with minor sacrifices in performance. Experiments on synthetic datasets explore the effects of run-time, relative complexity, and other aspects of distributions on various properties of our changepoint detectors, namely robustness, detection accuracy, scalability, etc. under controlled environments. Finally, we perform experiments on six real-world temporal event sequences collected from diverse domains like health, geographical regions, etc., and show that our methods either outperform or perform comparably with the baselines.

This article investigates the dynamics of competition among organizations with unequal expertise. Multiagent reinforcement learning (MARL) has been used to simulate and understand the impact of various incentive schemes designed to offset such inequality. We design Touch-Mark, a game based on well-known multiagent particle environment, where two teams (weak and strong) with unequal but changing skill levels compete against each other. For training such a game, we propose a novel controller-assisted MARL algorithm C-MADDPG, which empowers each agent with an ensemble of policies along with a supervised controller that by selectively partitioning the sample space and triggers intelligent role division among the teammates. Using C-MADDPG as an underlying framework, we propose an incentive scheme for the weak team such that the final rewards of both teams become the same. We find that despite the incentive, the final reward of the weak team falls short of the strong team. On inspecting, we realize that an overall incentive scheme for the weak team does not incentivize the weaker agents within that team to learn and improve. To offset this, we now specially incentivize the weaker player to learn and, as a result, observe that the weak team beyond an initial phase performs at par with the stronger team. The final goal of this article has been to formulate a dynamic incentive scheme that continuously balances the reward of the two teams. This is achieved by devising an incentive scheme enriched with an RL agent, which takes minimum information from the environment.

The networked opinion diffusion in online social networks is often governed by the two genres of opinions— endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.

Decisions are increasingly taken by both humans and machine learning models. However, machine learning models are currently trained for full automation—they are not aware that some of the decisions may still be taken by humans. In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels. More specifically, we first introduce the problem of ridge regression under human assistance and show that it is NP-hard. Then, we derive an alternative representation of the corresponding objective function as a difference of nondecreasing submodular functions. Building on this representation, we further show that the objective is nondecreasing and satisfies α-submodularity, a recently introduced notion of approximate submodularity. These properties allow a simple and efficient greedy algorithm to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from two important applications—medical diagnosis and content moderation—demonstrate that the greedy algorithm beats several competitive baselines.

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a 'maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS_GM), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.