SUBHRO DAS

DEEP LEARNING ARCHITECTURE FOR MULTIVARIATE TIME SERIES

POST-HOC UNCERTAINTY QUANTIFICATION FOR MACHINE LEARNING SYSTEMS

RELIABLE GRADIENT-FREE AND LIKELIHOOD-FREE PROMPT TUNING

SENSITIVE ATTRIBUTE DRIVEN PREDICTIVE MODELING

NEURAL NETWORK-BASED DYNAMICAL SYSTEM MODELING FOR CONTRASTIVELY LEARNED CONSERVATION LAWS

Multivariate Skill Demand Forecasting System

GENERAL AND ROBUST DISTRIBUTED LINEAR FILTERING AND PREDICTION WITH OPTIMAL GAIN

MULTIVARIABLE TIME-SERIES FEATURE EXTRACTION

PROVIDING TRAINED REINFORCEMENT LEARNING SYSTEMS

SENSOR CALIBRATION USING DISTRIBUTED STOCHASTIC PARAMETER ESTIMATION

ADVERSARIAL ATTACKS FOR IMPROVING COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING SYSTEMS

CERTIFICATION-BASED ROBUST TRAINING BY REFINING DECISION BOUNDARY

IDENTIFYING SKILL ADJACENCIES AND SKILL GAPS FOR GENERATING RESKILLING RECOMMENDATIONS AND EXPLAINABILITY

FAIR SELECTIVE CLASSIFICATION VIA A VARIATIONAL MUTUAL INFORMATION UPPER BOUND FOR IMPOSING SUFFICIENCY

ONLINE OPTIMAL CONTROL UNDER CONSTRAINTS

RAPID DEVELOPMENT OF USER INTENT AND ANALYTIC SPECIFICATION IN COMPLEX DATA SPACES

LEVERAGING DYNAMICAL PRIORS FOR SYMBOLIC MAPPINGS IN SAFE REINFORCEMENT LEARNING

Skills and tasks demand forecasting

LATENT BIO-SIGNAL ESTIMATION USING BIO-SIGNAL DETECTORS

Graph convolutional networks for video grounding

Formally safe symbolic reinforcement learning on visual inputs

Personalized patient engagement in care management using explainable behavioral phenotypes

Data protection distributed learning

Input data correction