Search Results for author:
Found 28 papers, 4 papers with code
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
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.
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
To tackle these issues, in this paper, we present EXPLORER which is an exploration-guided reasoning agent for textual reinforcement learning.
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
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.
Formally Specifying the High-Level Behavior of LLM-Based Agents
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.
Disentangling and Operationalizing AI Fairness at LinkedIn
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.
Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs
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.
An Operational Perspective to Fairness Interventions: Where and How to Intervene
We demonstrate our framework with a case study on predictive parity.
Pushing the limits of fairness impossibility: Who's the fairest of them all?
The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature.
Heterogeneous Calibration: A post-hoc model-agnostic framework for improved generalization
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.
Generalized Causal Tree for Uplift Modeling
In this paper, we propose a generalization of tree-based approaches to tackle multiple discrete and continuous-valued treatments.
An ASP-based Approach to Answering Natural Language Questions for Texts
The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system.
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming
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
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning
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.
CASPR: A Commonsense Reasoning-based Conversational Socialbot
We present the philosophy behind CASPR's design as well as details of its implementation.
DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbots
We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot.
Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programming
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.
Efficient Vertex-Oriented Polytopic Projection for Web-scale Applications
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.
Knowledge-driven Natural Language Understanding of English Text and its Applications
Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research.
SQuARE: Semantics-based Question Answering and Reasoning Engine
We introduce a general semantics-based framework for natural language QA and also describe the SQuARE system, an application of this framework.
A Framework for Fairness in Two-Sided Marketplaces
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.).
Conversational AI : Open Domain Question Answering and Commonsense Reasoning
Our research is focused on making a human-like question answering system which can answer rationally.
Optimal Convergence for Stochastic Optimization with Multiple Expectation Constraints
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.
Personalized Treatment Selection using Causal Heterogeneity
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.
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
We consider the problem of solving a large-scale Quadratically Constrained Quadratic Program.
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization
We consider the problem of global optimization of a function over a continuous domain.
Constrained Multi-Slot Optimization for Ranking Recommendations
Ranking items to be recommended to users is one of the main problems in large scale social media applications.
Large scale multi-objective optimization: Theoretical and practical challenges
Multi-objective optimization (MOO) is a well-studied problem for several important recommendation problems.
