Trading Prophets: How to Trade Multiple Stocks Optimally
Surbhi Rajput, Ashish Chiplunkar, and Rohit Vaish

How an online algorithm, that buys or sells stocks based only on current prices, can compete with an offline prophet that knows all prices beforehand.

Charging Electric Vehicles Fairly and Efficiently
Ramsundar Anandanarayanan, Swaprava Nath, and Rohit Vaish

Fairly allocating homogeneous divisible resources among agents with different consumption rates and arrival and departure times.

Fair Interval Scheduling of Indivisible Chores
Sarfaraz Equbal, Rohit Gurjar, Yatharth Kumar, Swaprava Nath, and Rohit Vaish
arXiv

How to fairly allocate resources under conflict constraints?

Fair and Efficient Completion of Indivisible Goods
Ayumi Igarashi, Vishwa Prakash, and Rohit Vaish

When some resources are pre-assigned, how should the remaining resources be distributed to make the final allocation fair and efficient?

Capacity Modification in the Stable Matching Problem
Salil Gokhale, Shivika Narang, Samarth Singla, and Rohit Vaish
AAMAS 2024
arXiv

In the many-to-one stable matching problem, how can the capacities on the "many" side be modified to achieve desirable outcomes as stable solutions of the modified problem?

Maximizing Nash Social Welfare under Two-Sided Preferences
Pallavi Jain and Rohit Vaish
AAAI 2024
arXiv

Finding a many-to-one matching that maximizes Nash social welfare.

Tight Approximations for Graphical House Allocation
Hadi Hosseini, Andrew McGregor, Rik Sengupta, Rohit Vaish, and Vignesh Viswanathan
AAMAS 2024
arXiv

Approximation algorithms for the graphical house allocation studied in the AAMAS 2023 paper. Also, Ramanujan graphs and a cool connection between graph cuts and bitstrings.

The Price of Equity with Binary Valuations and Few Agent Types
Umang Bhaskar, Neeldhara Misra, Aditi Sethia, and Rohit Vaish
SAGT 2023
Conference paper / arXiv

How much welfare loss does fairness impose? The paper looks at a worst-case quantification called price of fairness for the class of generalized mean welfare measures and provides fine-grained bounds in terms of the number of agent types.

Hide, Not Seek: Perceived Fairness in Envy-Free Allocations of Indivisible Goods
Hadi Hosseini, Joshua Kavner, Sujoy Sikdar, Rohit Vaish, and Lirong Xia
arXiv

Do real-world users consider approximately envy-free allocations fair?

Best-of-Both-Worlds Allocations with Fair Shares and Almost Envy-Freeness
Telikepalli Kavitha and Rohit Vaish

The paper studies the best-of-both-worlds framework for fair allocations of indivisible goods, and constructs randomized allocations that are ex-ante approximately proportional and simultaneously satisfy envy-based and fair share-based ex-post guarantees.

Towards Fair Allocation in Social Commerce Platforms
Anjali Gupta, Shreyans J Nagori, Abhijnan Chakraborty, Rohit Vaish, Sayan Ranu, Prajit Prashant Nadkarni, Narendra Varma Dasararaju, and Muthusamy Chelliah
WWW 2023
Conference paper

Fair division under two-sided cardinality constraints.

Graphical House Allocation
Hadi Hosseini, Justin Payan, Rik Sengupta, Rohit Vaish, and Vignesh Viswanathan
AAMAS 2023
Conference paper / arXiv

How to assign a set of numbers to the vertices of a graph so as to minimize the sum of absolute differences along the edges?

Fairly Dividing Mixtures of Goods and Chores under Lexicographic Preferences
Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, and Lirong Xia
AAMAS 2023
Conference paper / arXiv

When agents have lexicographic preferences over mixtures of indivisible goods and chores, an EFX allocation could fail to exist.

Semi-Popular Matchings and Copeland Winners
Telikepalli Kavitha and Rohit Vaish
AAMAS 2023
Conference paper / arXiv

The paper applies voting theory to the study of matchings-under-preferences, and proposes a relaxation of popularity called weak Copeland matching that is guaranteed to exist in general roommates instances with weak preferences and admits an FPRAS.

Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation
Haris Aziz, Rupert Freeman, Nisarg Shah, and Rohit Vaish
Operations Research
Journal paper (combines the EC 2020 paper by Freeman, Shah, and Vaish and the WINE 2020 paper by Aziz)

A polynomial-time algorithm for finding a distribution over EF1 allocations that is envy-free in expectation.

Two for One & One for All: Two-Sided Manipulation in Matching Markets
Hadi Hosseini, Fatima Umar, and Rohit Vaish
IJCAI 2022
Conference paper / arXiv

The paper gives a polynomial-time algorithm for computing the optimal joint strategy for a man-woman pair who want to help the woman under the deferred acceptance algorithm.

Equitable Division of a Path
Chinmay Sonar, Neeldhara Misra, P R Vaidyanathan, and Rohit Vaish
COMSOC 2021
arXiv

This paper gives an algorithm that, given an arrangement of indivisible items on a path and any ordering of agents, computes an EQ1 allocation consistent with the given ordering in which agents receive connected bundles and with the highest egalitarian welfare.

Making Group Decisions from Natural Language-Based Preferences
Farhad Mohsin, Lei Luo, Wufei Ma, Inwon Kang, Zhibing Zhao, Ao Liu, Rohit Vaish, and Lirong Xia
COMSOC 2021
Workshop paper

How to make group decisions when agents' preferences are specified not as rankings or numerical utilities but in natural language.

On Approximate Envy-Freeness for Indivisible Chores and Mixed Resources
Umang Bhaskar, A R Sricharan, and Rohit Vaish
APPROX 2021
Conference paper / arXiv / Video (17 min) from GAIW 2021

Resolving arbitrary envy cycles could fail to achieve EF1 even for additive chores. The paper proposes the top-trading envy cycle algorithm that guarantees EF1 for monotone chores.

Accomplice Manipulation of the Deferred Acceptance Algorithm
Hadi Hosseini, Fatima Umar, and Rohit Vaish
IJCAI 2021
Conference paper / arXiv / Video (5 min) on IITD CSE Channel

Under the deferred acceptance algorithm, an agent on the proposed-to side is typically better off asking a proposer (a.k.a., an accomplice) to manipulate on her behalf rather than manipulating on her own.

Fair and Efficient Allocations under Lexicographic Preferences
Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, and Lirong Xia
AAAI 2021
Conference paper / arXiv

Characterizing allocations that are envy-free up to any good (EFX) and Pareto optimal (PO) under lexicographic preferences.

Representative Proxy Voting
Elliot Anshelevich, Zack Fitzsimmons, Rohit Vaish, and Lirong Xia
AAAI 2021
Conference paper / arXiv / Video (18 min) from AAAI 2021

Computing proxy arrangements that fairly represent a continuum of voters in a one-dimensional setting.

Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation
Rupert Freeman, Nisarg Shah, and Rohit Vaish
EC 2020 (superseded by journal version at Operations Research)
Conference paper / arXiv

A polynomial-time algorithm for finding a distribution over EF1 allocations that is envy-free in expectation.

Fair Division through Information Withholding
Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, and Lirong Xia
AAAI 2020
Conference paper / arXiv

What is the smallest number of goods that need to be concealed in order to achieve envy-freeness?

Equitable Allocations of Indivisible Chores
Rupert Freeman, Sujoy Sikdar, Rohit Vaish, and Lirong Xia
AAMAS 2020
Conference paper / arXiv

A pseudopolynomial-time algorithm for finding an allocation that is equitable up to one chore (EQ1) and Pareto optimal (PO).

Stable Fractional Matchings
Ioannis Caragiannis, Aris Filos-Ratsikas, Panagiotis Kanellopoulos, and Rohit Vaish
EC 2019 and Artificial Intelligence
Conference paper / Journal paper / arXiv / Video (14 min) from EC 2019 /
Video (30 mins) from COMSOC video seminar

The paper studies computational aspects of the stable matching problem when agents have cardinal preferences and one seeks a fractional solution with high social welfare.

Equitable Allocations of Indivisible Goods
Rupert Freeman, Sujoy Sikdar, Rohit Vaish, and Lirong Xia
IJCAI 2019
Conference paper / arXiv

This paper studies approximately equitable and economically efficient allocations of indivisible goods.

Minimizing Time-to-Rank: A Learning and Recommendation Approach
Haoming Li, Sujoy Sikdar, Rohit Vaish, Junming Wang, Lirong Xia, and Chaonan Ye
IJCAI 2019
Conference paper / arXiv

The paper develops a framework for optimizing the time it takes a user to transform a given ranking into a desired ranking via drag-and-drop operations.

Greedy Algorithms for Maximizing Nash Social Welfare
Siddharth Barman, Sanath Kumar Krishnamurthy, and Rohit Vaish
AAMAS 2018   (Best Paper Nomination)
Conference paper / arXiv

A polynomial-time algorithm for maximizing Nash social welfare under binary valuations.

Finding Fair and Efficient Allocations
Siddharth Barman, Sanath Kumar Krishnamurthy, and Rohit Vaish
EC 2018
Conference paper / arXiv

An algorithmic framework for finding an allocation that is envy-free up to one good (EF1) and Pareto optimal (PO), and a 1.45-approximation algorithm for Nash social welfare.

Opting Into Optimal Matchings
Avrim Blum, Ioannis Caragiannis, Nika Haghtalab, Ariel Procaccia, Eviatar Procaccia, and Rohit Vaish
SODA 2017
Conference paper / arXiv

The paper studies a semi-random model for kidney exchange with a worst-case compatibility graph and a uniformly random assignment of patient-donor pairs to hospitals.

Manipulating Gale-Shapley Algorithm: Preserving Stability and Remaining Inconspicuous
Rohit Vaish and Dinesh Garg
IJCAI 2017
Conference paper

Under the deferred acceptance algorithm, optimal manipulation by an agent on the proposed-to side is inconspicuous and stability-preserving.

Analysis of Thompson Sampling for Stochastic Sleeping Bandits
Aritra Chatterjee, Ganesh Ghalme, Shweta Jain, Rohit Vaish, and Yadati Narahari
UAI 2017
Conference paper

Analyzing the Thomson sampling algorithm for stochastic multiarmed bandits when not all arms are available in each round.

On the Computational Hardness of Manipulating Pairwise Voting Rules
Rohit Vaish, Neeldhara Misra, Shivani Agarwal, and Avrim Blum
AAMAS 2016
Conference paper / Full version

In elections where each voter's preferences are given by a set of pairwise comparisons, no "reasonable" voting rule can prevent tactical voting, but NP-hardness comes to the rescue.

On the Parameterized Complexity of Manipulating Pairwise Voting Rules
Rohit Vaish and Neeldhara Misra
EXPLORE 2016
Workshop paper

This paper undertakes a parameterized complexity analysis of the manipulation problem studied in our AAMAS 2016 paper.

On the Statistical Consistency of Plug-in Classifiers for Non-Decomposable Performance Measures
Harikrishna Narasimhan, Rohit Vaish, and Shivani Agarwal
NeurIPS 2014
Conference paper

The paper shows that plug-in algorithms are statistically consistent for non-decomposable performance measures (e.g., F-measure).