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Pankaj Dayama

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NeurIPS Conference 2024 Conference Paper

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

Vijay Ekambaram
Arindam Jati
Pankaj Dayama
Sumanta Mukherjee
Nam H. Nguyen
Wesley M. Gifford
Chandra Reddy
Jayant Kalagnanam

Large pre-trained models excel in zero/few-shot learning for language and vision tasks but face challenges in multivariate time series (TS) forecasting due to diverse data characteristics. Consequently, recent research efforts have focused on developing pre-trained TS forecasting models. These models, whether built from scratch or adapted from large language models (LLMs), excel in zero/few-shot forecasting tasks. However, they are limited by slow performance, high computational demands, and neglect of cross-channel and exogenous correlations. To address this, we introduce Tiny Time Mixers (TTM), a compact model (starting from 1M parameters) with effective transfer learning capabilities, trained exclusively on public TS datasets. TTM, based on the light-weight TSMixer architecture, incorporates innovations like adaptive patching, diverse resolution sampling, and resolution prefix tuning to handle pre-training on varied dataset resolutions with minimal model capacity. Additionally, it employs multi-level modeling to capture channel correlations and infuse exogenous signals during fine-tuning. TTM outperforms existing popular benchmarks in zero/few-shot forecasting by (4-40\%), while reducing computational requirements significantly. Moreover, TTMs are lightweight and can be executed even on CPU-only machines, enhancing usability and fostering wider adoption in resource-constrained environments. The model weights for reproducibility and research use are available at https: //huggingface. co/ibm/ttm-research-r2/, while enterprise-use weights under the Apache license can be accessed as follows: the initial TTM-Q variant at https: //huggingface. co/ibm-granite/granite-timeseries-ttm-r1, and the latest variants (TTM-B, TTM-E, TTM-A) weights are available at https: //huggingface. co/ibm-granite/granite-timeseries-ttm-r2. The source code for the TTM model along with the usage scripts are available at https: //github. com/ibm-granite/granite-tsfm/tree/main/tsfm_public/models/tinytimemixer

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AAMAS Conference 2019 Conference Paper

Risk Averse Reinforcement Learning for Mixed Multi-agent Environments

D. Sai Koti Reddy
Amrita Saha
Srikanth G. Tamilselvam
Priyanka Agrawal
Pankaj Dayama

Most real world applications of multi-agent systems, need to keep a balance between maximizing the rewards and minimizing the risks. In this work we consider a popular risk measure, variance of return (VOR), as a constraint in the agent’s policy learning algorithm in the mixed cooperative and competitive environments. We present a multi-timescale actor critic method for risk sensitive Markov games where the risk is modeled as a VOR constraint. We also show that the risk-averse policies satisfy the desired risk constraint without compromising much on the overall reward for a popular task.

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AAMAS Conference 2012 Conference Paper

Optimal Incentive Timing Strategies for Product Marketing on Social Networks

Pankaj Dayama
Aditya Karnik
Yadati Narahari

We consider the problem of devising incentive strategies for viral marketing of a product. In particular, we assume that the seller can influence penetration of the product by offering two programs: a) direct incentives to potential buyers (\emph{influence}) and b) referral rewards for customers who influence potential buyers to make the purchase (\emph{exploit connections}). The problem is to determine the optimal timing of these programs over a finite time horizon. In contrast to algorithmic perspective popular in the literature, we take a mean-field approach and formulate the problem as a continuous-time deterministic optimal control problem. We show that the optimal strategy for the seller has a simple structure and can take both forms, namely, \emph{influence-and-exploit} and \emph{exploit-and-influence}. We also show that in some cases it may optimal for the seller to deploy incentive programs mostly for low degree nodes. We support our theoretical results through numerical studies and provide practical insights by analyzing various scenarios.

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AAAI Conference 2012 Conference Paper

Threats and Trade-Offs in Resource Critical Crowdsourcing Tasks Over Networks

Swaprava Nath
Pankaj Dayama
Dinesh Garg
Y. Narahari
James Zou

In recent times, crowdsourcing over social networks has emerged as an active tool for complex task execution. In this paper, we address the problem faced by a planner to incentivize agents in the network to execute a task and also help in recruiting other agents for this purpose. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner’s goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We define a set of fairness properties that should be ideally satisfied by a crowdsourcing mechanism. We prove that no mechanism can satisfy all these properties simultaneously. We relax some of these properties and define their approximate counterparts. Under appropriate approximate fairness criteria, we obtain a non-trivial family of payment mechanisms. Moreover, we provide precise characterizations of cost critical and time critical mechanisms.

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