23 citations · 56 across the 4 of their papers we have counts for
6 papers
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei +6
Autonomous cyber-physical agents and systems play an increasingly large role in our lives. To ensure that agents behave in ways aligned with the values of the societies in which th…
Envy-Free Classification
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu +1
In classic fair division problems such as cake cutting and rent division, envy-freeness requires that each individual (weakly) prefer his allocation to anyone else's. On a conceptu…
Loss Functions, Axioms, and Peer Review
Ritesh Noothigattu, Nihar B. Shah, Ariel D. Procaccia
It is common to see a handful of reviewers reject a highly novel paper, because they view, say, extensive experiments as far more important than novelty, whereas the community as a…
A Voting-Based System for Ethical Decision Making
Ritesh Noothigattu, Snehalkumar 'Neil' S. Gaikwad, Edmond Awad +4
We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice. In a nutshell, we propose to learn a model of societal p…
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Ankit Anand, Ritesh Noothigattu, Parag Singla +1
Lifted inference algorithms commonly exploit symmetries in a probabilistic graphical model (PGM) for efficient inference. However, existing algorithms for Boolean-valued domains ca…
Weighted Voting Via No-Regret Learning
Nika Haghtalab, Ritesh Noothigattu, Ariel D. Procaccia
Voting systems typically treat all voters equally. We argue that perhaps they should not: Voters who have supported good choices in the past should be given higher weight than vote…