72 citations · 107 across the 15 of their papers we have counts for
15 papers
Learning from Interval Targets
Rattana Pukdee, Ziqi Ke, Chirag Gupta
We study the problem of regression with interval targets, where only upper and lower bounds on target values are available in the form of intervals. This problem arises when the ex…
Does Calibration Affect Human Actions?
Meir Nizri, Amos Azaria, Chirag Gupta +1
Calibration has been proposed as a way to enhance the reliability and adoption of machine learning classifiers. We study a particular aspect of this proposal: how does calibrating…
Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies
Terrance Liu, Shuyi Wang, Daniel Preotiuc-Pietro +2
While large language models (LLMs) achieve strong performance on text-to-SQL parsing, they sometimes exhibit unexpected failures in which they are confidently incorrect. Building t…
OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework
Cheng-Wei Ching, Chirag Gupta, Zi Huang +1
Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA…
Parity Calibration
Youngseog Chung, Aaron Rumack, Chirag Gupta
In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather th…
Online Platt Scaling with Calibeating
Chirag Gupta, Aaditya Ramdas
We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that…
Faster online calibration without randomization: interval forecasts and the power of two choices
Chirag Gupta, Aaditya Ramdas
We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998),…
Top-label calibration and multiclass-to-binary reductions
Chirag Gupta, Aaditya Ramdas
A multiclass classifier is said to be top-label calibrated if the reported probability for the predicted class -- the top-label -- is calibrated, conditioned on the top-label. This…
Distribution-free calibration guarantees for histogram binning without sample splitting
Chirag Gupta, Aaditya K. Ramdas
We prove calibration guarantees for the popular histogram binning (also called uniform-mass binning) method of Zadrozny and Elkan [2001]. Histogram binning has displayed strong pra…
Modern Machine and Deep Learning Systems as a way to achieve Man-Computer Symbiosis
Chirag Gupta
Man-Computer Symbiosis (MCS) was originally envisioned by the famous computer pioneer J.C.R. Licklider in 1960, as a logical evolution of the then inchoate relationship between com…
Distribution-free binary classification: prediction sets, confidence intervals and calibration
Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas
We study three notions of uncertainty quantification -- calibration, confidence intervals and prediction sets -- for binary classification in the distribution-free setting, that is…
Self learning robot using real-time neural networks
Chirag Gupta, Chikita Nangia, Chetan Kumar
With the advancements in high volume, low precision computational technology and applied research on cognitive artificially intelligent heuristic systems, machine learning solution…
Shallow Encoder Deep Decoder (SEDD) Networks for Image Encryption and Decryption
Chirag Gupta
This paper explores a new framework for lossy image encryption and decryption using a simple shallow encoder neural network E for encryption, and a complex deep decoder neural netw…
Nested conformal prediction and quantile out-of-bag ensemble methods
Chirag Gupta, Arun K. Kuchibhotla, Aaditya K. Ramdas
Conformal prediction is a popular tool for providing valid prediction sets for classification and regression problems, without relying on any distributional assumptions on the data…
Path Length Bounds for Gradient Descent and Flow
Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
We derive bounds on the path length of gradient descent (GD) and gradient flow (GF) curves for various classes of smooth convex and nonconvex functions. Among other results, we…