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Chandra Reddy

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9 papers
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9

UAI Conference 2024 Conference Paper

Identifying Homogeneous and Interpretable Groups for Conformal Prediction

  • Natalia Martinez Gil
  • Dhaval Patel 0002
  • Chandra Reddy
  • Giridhar Ganapavarapu
  • Roman Vaculín
  • Jayant Kalagnanam

Conformal prediction methods are a tool for uncertainty quantification of a model’s prediction, providing a model-agnostic and distribution-free statistical wrapper that generates prediction intervals/sets for a given model with finite sample generalization guarantees. However, these guarantees hold only on average, or conditioned on the output values of the predictor or on a set of predefined groups, which a-priori may not relate to the prediction task at hand. We propose a method to learn a generalizable partition function of the input space (or representation mapping) into interpretable groups of varying sizes where the non-conformity scores - a measure of discrepancy between prediction and target - are as homogeneous as possible when conditioned to the group. The learned partition can be integrated with any of the group conditional conformal approaches to produce conformal sets with group conditional guarantees on the discovered regions. Since these learned groups are expressed as strictly a function of the input, they can be used for downstream tasks such as data collection or model selection. We show the effectiveness of our method in reducing worst case group coverage outcomes in a variety of datasets.

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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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AAAI Conference 2022 Conference Paper

Interpretable Clustering via Multi-Polytope Machines

  • Connor Lawless
  • Jayant Kalagnanam
  • Lam M Nguyen
  • Dzung Phan
  • Chandra Reddy

Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use as a tool for subgroup discovery and description - few stateof-the-art algorithms provide any rationale or description behind the clusters found. We propose a novel approach for interpretable clustering that both clusters data points and constructs polytopes around the discovered clusters to explain them. Our framework allows for additional constraints on the polytopes - including ensuring that the hyperplanes constructing the polytope are axis-parallel or sparse with integer coefficients. We formulate the problem of constructing clusters via polytopes as a Mixed-Integer Non-Linear Program (MINLP). To solve our formulation we propose a two phase approach where we first initialize clusters and polytopes using alternating minimization, and then use coordinate descent to boost clustering performance. We benchmark our approach on a suite of synthetic and real world clustering problems, where our algorithm outperforms state of the art interpretable and non-interpretable clustering algorithms.

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AAAI Conference 2015 Conference Paper

Towards Cognitive Automation of Data Science

  • Alain Biem
  • Maria Butrico
  • Mark Feblowitz
  • Tim Klinger
  • Yuri Malitsky
  • Kenney Ng
  • Adam Perer
  • Chandra Reddy

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e. g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.

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SAT Conference 2013 Conference Paper

Snappy: A Simple Algorithm Portfolio

  • Horst Samulowitz
  • Chandra Reddy
  • Ashish Sabharwal
  • Meinolf Sellmann

Abstract Algorithm portfolios try to combine the strength of individual algorithms to tackle a problem instance at hand with the most suitable technique. In the context of SAT the effectiveness of such approaches is often demonstrated at the SAT Competitions. In this paper we show that a competitive algorithm portfolio can be designed in an extremely simple fashion. In fact, the algorithm portfolio we present does not require any offline learning nor knowledge of any complex Machine Learning tools. We hope that the utter simplicity of our approach combined with its effectiveness will make algorithm portfolios accessible by a broader range of researchers including SAT and CSP solver developers.

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