Author name cluster

Paramita Koley

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2025 Conference Paper

ExPERT: Modeling Human Behavior Under External Stimuli Aware Personalized MTPP

Subhendu Khatuya
Ritvik Vij
Paramita Koley
Samik Datta
Niloy Ganguly

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.

PDF Details DOI

AAAI Conference 2020 Conference Paper

Regression under Human Assistance

Abir De
Paramita Koley
Niloy Ganguly
Manuel Gomez-Rodriguez

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.

PDF Details