no code implementations • ICML 2020 • Kinjal Basu, Amol Ghoting, Rahul Mazumder, Yao Pan
Experiments on real-world data show that our proposed LP solver, ECLIPSE, can solve problems with $10^{12}$ decision variables -- well beyond the capabilities of current solvers.
1 code implementation • 7 May 2024 • Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Shivdeep Singh, Manish Sethi, Xuan-Hong Dang, Pengyuan Li, Kun-Lung Wu, Syed Zawad, Andrew Coleman, Matthew White, Mark Lewis, Raju Pavuluri, Yan Koyfman, Boris Lublinsky, Maximilien de Bayser, Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Yi Zhou, Chris Johnson, Aanchal Goyal, Hima Patel, Yousaf Shah, Petros Zerfos, Heiko Ludwig, Asim Munawar, Maxwell Crouse, Pavan Kapanipathi, Shweta Salaria, Bob Calio, Sophia Wen, Seetharami Seelam, Brian Belgodere, Carlos Fonseca, Amith Singhee, Nirmit Desai, David D. Cox, Ruchir Puri, Rameswar Panda
Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously.
1 code implementation • 15 Mar 2024 • Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger
To tackle these issues, in this paper, we present EXPLORER which is an exploration-guided reasoning agent for textual reinforcement learning.
1 code implementation • 23 Feb 2024 • Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis A. Lastras
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.
no code implementations • 12 Oct 2023 • Maxwell Crouse, Ibrahim Abdelaziz, Ramon Astudillo, Kinjal Basu, Soham Dan, Sadhana Kumaravel, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Luis Lastras
We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e. g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent.
no code implementations • 30 May 2023 • Joaquin Quiñonero-Candela, Yuwen Wu, Brian Hsu, Sakshi Jain, Jen Ramos, Jon Adams, Robert Hallman, Kinjal Basu
Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed.
no code implementations • 15 Mar 2023 • Yankai Zeng, Abhiramon Rajasekharan, Parth Padalkar, Kinjal Basu, Joaquín Arias, Gopal Gupta
To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human and provide help to humans based on truly understanding human utterances.
no code implementations • 3 Feb 2023 • Brian Hsu, Xiaotong Chen, Ying Han, Hongseok Namkoong, Kinjal Basu
We demonstrate our framework with a case study on predictive parity.
no code implementations • 24 Aug 2022 • Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu
The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature.
no code implementations • 10 Feb 2022 • David Durfee, Aman Gupta, Kinjal Basu
We introduce the notion of heterogeneous calibration that applies a post-hoc model-agnostic transformation to model outputs for improving AUC performance on binary classification tasks.
1 code implementation • 4 Feb 2022 • Preetam Nandy, Xiufan Yu, Wanjun Liu, Ye Tu, Kinjal Basu, Shaunak Chatterjee
In this paper, we propose a generalization of tree-based approaches to tackle multiple discrete and continuous-valued treatments.
no code implementations • 21 Dec 2021 • Dhruva Pendharkar, Kinjal Basu, Farhad Shakerin, Gopal Gupta
The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system.
no code implementations • AAAI Workshop CLeaR 2022 • Kinjal Basu, Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Kartik Talamadupula, Tim Klinger, Murray Campbell, Mrinmaya Sachan, Gopal Gupta
These rules are learned in an online manner and applied with an ASP solver to predict an action for the agent.
Inductive logic programming Natural Language Understanding +2
no code implementations • 17 Oct 2021 • Suraj Kothawade, Vinaya Khandelwal, Kinjal Basu, Huaduo Wang, Gopal Gupta
That is, while machine learning technology is good for observing and automatically understanding the surroundings of an automobile, driving decisions are better automated via commonsense reasoning rather than machine learning.
no code implementations • 11 Oct 2021 • Kinjal Basu, Huaduo Wang, Nancy Dominguez, Xiangci Li, Fang Li, Sarat Chandra Varanasi, Gopal Gupta
We present the philosophy behind CASPR's design as well as details of its implementation.
no code implementations • 17 Sep 2021 • Fang Li, Huaduo Wang, Kinjal Basu, Elmer Salazar, Gopal Gupta
We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot.
no code implementations • 10 Sep 2021 • Brendan Hall, Sarat Chandra Varanasi, Jan Fiedor, Joaquín Arias, Kinjal Basu, Fang Li, Devesh Bhatt, Kevin Driscoll, Elmer Salazar, Gopal Gupta
We also show how answer set programming (ASP) and its query-driven implementation s(CASP) can be used to directly realize the event calculus model of the requirements.
1 code implementation • 9 Mar 2021 • Rohan Ramanath, S. Sathiya Keerthi, Yao Pan, Konstantin Salomatin, Kinjal Basu
We develop an intuition guided by theoretical and empirical analysis to show that when these instances follow certain structures, a large majority of the projections lie on vertices of the polytopes.
no code implementations • 27 Jan 2021 • Kinjal Basu, Sarat Varanasi, Farhad Shakerin, Joaquin Arias, Gopal Gupta
Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research.
no code implementations • 22 Sep 2020 • Kinjal Basu, Sarat Chandra Varanasi, Farhad Shakerin, Gopal Gupta
We introduce a general semantics-based framework for natural language QA and also describe the SQuARE system, an application of this framework.
no code implementations • 23 Jun 2020 • Kinjal Basu, Cyrus DiCiccio, Heloise Logan, Noureddine El Karoui
Incorporating fairness while building such systems is crucial and can have a deep social and economic impact (applications include job recommendations, recruiters searching for candidates, etc.).
no code implementations • 19 Jun 2020 • Preetam Nandy, Cyrus DiCiccio, Divya Venugopalan, Heloise Logan, Kinjal Basu, Noureddine El Karoui
Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society.
no code implementations • 18 Sep 2019 • Kinjal Basu
Our research is focused on making a human-like question answering system which can answer rationally.
no code implementations • 8 Jun 2019 • Kinjal Basu, Preetam Nandy
In this paper, we focus on the problem of stochastic optimization where the objective function can be written as an expectation function over a closed convex set.
1 code implementation • 29 Jan 2019 • Ye Tu, Kinjal Basu, Cyrus DiCiccio, Romil Bansal, Preetam Nandy, Padmini Jaikumar, Shaunak Chatterjee
In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization.
no code implementations • NeurIPS 2017 • Kinjal Basu, Ankan Saha, Shaunak Chatterjee
We consider the problem of solving a large-scale Quadratically Constrained Quadratic Program.
no code implementations • 18 May 2017 • Kinjal Basu, Souvik Ghosh
We consider the problem of global optimization of a function over a continuous domain.
no code implementations • 13 Feb 2016 • Kinjal Basu, Shaunak Chatterjee, Ankan Saha
Ranking items to be recommended to users is one of the main problems in large scale social media applications.
no code implementations • 9 Feb 2016 • Kinjal Basu, Ankan Saha, Shaunak Chatterjee
Multi-objective optimization (MOO) is a well-studied problem for several important recommendation problems.