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Sharath Chandra Raparthy

Alumni

Publications

Jailbreak Distillation: Renewable Safety Benchmarking
Jingyu Zhang
Ahmed Elgohary
Xiawei Wang
A S M Iftekhar
Ahmed Magooda
Benjamin Van Durme
Daniel Khashabi
Kyle Jackson
JBDistill Benchmark JBDistill Benchmark
Marah Ihab Abdin
Jyoti Aneja
Harkirat Singh Behl
Sébastien Bubeck
Ronen Eldan
S. Gunasekar
Michael Harrison
Russell J. Hewett
Mojan Javaheripi
Piero Kauffmann
James R. Lee … (see 484 more)
Yin Tat Lee
Yuanzhi Li
Weishung Liu
C. C. T. Mendes
Anh Nguyen
Eric Price
Gustavo de Rosa
Olli Saarikivi
Adil Salim
Tim Beyer
Sophie Xhonneux
Simon Geisler
Gauthier Gidel
Leo Schwinn
Stephan Günnemann. 2025
Blake Bullwinkel
Amanda Minnich
Shiven Chawla
Gary Lopez
Martin Pouliot
Whitney Maxwell
Patrick Chao
Edoardo Debenedetti
Alexander Robey
Maksym Andriushchenko
Francesco Croce
Vikash Sehwag
Edgar Dobriban
Nicolas Flammarion
George J. Pappas
Florian Tramèr
Hamed Hassani
Eric Wong
Jailbreakbench
Zora Che
Stephen Casper
Robert Kirk
Anirudh Satheesh
Stewart Slocum
Lev E McKinney
Rohit Gandikota
Aidan Ewart
Domenic Rosati
Zichu Wu
Zikui Cai
Daya Guo
Dejian Yang
Haowei Zhang
Jun-Mei Song
Ruoyu Zhang
Runxin Xu
Qihao Zhu
Shirong Ma
Peiyi Wang
Xiaoling Bi
Xiaokang Zhang
Xingkai Yu
Yu Wu
Z. F. Wu
Zhibin Gou
Zhihong Shao
Zhuoshu Li
Ziyi Gao
A. Liu
Bing Xue
Bingxuan Wang
Bo WU
Bei Feng
Chenggang Lu
Chenggang Zhao
Chengqi Deng
Chenyu Zhang
C. Ruan
Damai Dai
Deli Chen
Dong-Li Ji
Erhang Li
Fangyun Lin
Fucong Dai
Fuli Luo
Guangbo Hao
Guanting Chen
Guowei Li
Han Bao
Hanwei Xu
Haocheng Wang
Honghui Ding
Huajian Xin
Huazuo Gao
Hui Qu
Hui Li
Jianzhong Guo
Jiashi Li
Jiawei Wang
Jingchang Chen
Jingyang Yuan
Junjie Qiu
Junlong Li
Jinbo Cai
Jia Ni
Jian Liang
Jin Chen
Kai Dong
Kai Hu
Kaige Gao
Kang Guan
Kexin Huang
Kuai Yu
Lean Wang
Lecong Zhang
Liang Zhao
Litong Wang
Liyue Zhang
Lei Xu
Leyi Xia
Mingchuan Zhang
Minghua Zhang
Min Tang
Meng Li
Miaojun Wang
Mingming Li
Ning Tian
Panpan Huang
Meng Wang
Qiancheng Wang
Qinyu Chen
Qiushi Du
Ruiqi Ge
Ruisong Zhang
Ruizhe Pan
Runji Wang
R. J. Chen
Rong Jin
Ruyi Chen
Shanghao Lu
Shangyan Zhou
Shanhuang Chen
Shengfeng Ye
Shiyu Wang
Shuiping Yu
Shunfeng Zhou
Shuting Pan
S. S. Li
Shuang Zhou
Shao-Ping Wu
Tao Yun
Tian Pei
Tianyu Sun
T. Wang
Wangding Zeng
Wanjia Zhao
Wen Liu
Wenfeng Liang
Wenjun Gao
Wen-Xuan Yu
Wentao Zhang
Wei Xiao
Wei An
Xiaodong Liu
Xiaohan Wang
Xiaokang Chen
Xiaotao Nie
Xin Cheng
Jian Li
Xinfeng Xie
Xingchao Liu
Xinyu Yang
Xinyuan Li
Xuecheng Su
Xuheng Lin
Xiangyu Jin
Xi-Cheng Shen
Xiaosha Chen
Xiaowen Sun
Xiaoxi-ang Wang
Xinnan Song
Xinyi Zhou
Xianzu Wang
Xinxia Shan
Y. K. Li
Y. Q. Wang
Y. X. Wei
Yang Zhang
Yan-Hong Xu
Yao Zhao
Yaofeng Sun
Yaohui Wang
Yi Yu
Yichao Zhang
Yifan Shi
Yi Xiong
Ying He
Yishi Piao
Yisong Wang
Yi Chern Tan
Yiyang Ma
Yiyuan Liu
Yongqiang Guo
Yuan Ou
Yuduan Wang
Yue Gong
Yuheng Zou
Yuzi He
Yunfan Xiong
Yuxiang Luo
Yuxiang You
Yu-mei You
Yuxuan Liu
Yuyang Zhou
Y. X. Zhu
Yanping Huang
Yaohui Li
Yang Li
Yi Zheng
Yunxiang Ma
Ying Tang
Yukun Zha
Yuting Yan
Z. Z. Ren
Zehui Ren
Zhangli Sha
Zhe Fu
Zhean Xu
Zhenda Xie
Zhengyan Zhang
Zhewen Hao
Zhicheng Ma
Zhigang Yan
Zhiyu Wu
Zihui Gu
Zijia Zhu
Zijun Liu
Zi-An Li
Ziwei Xie
Ziyang Song
Deep Ganguli
Liane Lovitt
Jackson Kernion
Amanda Askell
Yuntao Bai
Saurav Kadavath
Benjamin Mann
Ethan Perez
Nicholas Schiefer
Kamal Ndousse
Andy Jones
Sam Bowman
Anna Chen
Tom Con-erly
Nova Dassarma
Dawn Drain
Nelson Elhage Sheer
Stanislav Fort
Zac Hatfield-Dodds
T. Henighan
Danny Hernandez
Tristan Hume
Josh Jacobson
Scott Johnston
Shauna Kravec
Catherine Olsson
Sam Ringer
Eli Tran-Johnson
Dario Amodei
Tom Brown
Nicholas Joseph
Sam McCandlish
Chris Olah
Jared Kaplan
Jack Clark. 2022. Red
Aaron Grattafiori
Abhimanyu Dubey
Abhinav Jauhri
Abhinav Pandey
Abhishek Kadian
Ahmad Al-Dahle
Aiesha Letman
Akhil Mathur
Alan Schel-ten
Alex Vaughan
Amy Yang
Angela Fan
Anirudh Goyal
A. Hartshorn
Aobo Yang
Archi Mitra
Archie Sravankumar
Artem Korenev
Arthur Hinsvark
Arun Rao
Aston Zhang
Aurelien Ro-driguez
Austen Gregerson
Ava Spataru
Baptiste Rozière
Bethany Biron
Binh Tang
Bobbie Chern
Charlotte Caucheteux
Chaya Nayak
Chloe Bi
Chris Marra
Chris McConnell
Christian Keller
Christophe Touret
Chunyang Wu
Corinne Wong
Cris-tian Cantón Ferrer
Cyrus Nikolaidis
Damien Al-lonsius
Daniel Song
Danielle Pintz
Danny Livshits
Danny Wyatt
David Esiobu
Dhruv Choudhary
Dhruv Mahajan 0001
Diego Garcia-Olano
Diego Perino
Dieuwke Hupkes
Egor Lakomkin
Ehab A. AlBadawy
Elina Lobanova
Emily Dinan
Eric Michael Smith
Filip Radenovic
Francisco Guzmán
Frank Zhang
Gabriele Synnaeve
Gabrielle Lee
Georgia Lewis
G. Thattai
Graeme Nail
Gregoire Mi-alon
Guan Pang
Guillem Cucurell
Hailey Nguyen
Han-nah Korevaar
Hu Xu
Hugo Touvron
Imanol Iliyan Zarov
Arrieta Ibarra
Is-abel Kloumann
Ishan Misra
Ivan Evtimov
Jack Zhang
Jade Copet
Jaewon Lee
Jan Geffert
Jana Vranes
Jason Park
Jay Mahadeokar
Jeet Shah
Jelmer van der Linde
Jennifer Billock
Jenny Hong
Jenya Lee
Jeremy Fu
J. Fu
Jianfeng Chi
Jianyu Huang
Jiawen Liu
Jie Wang
Jiecao Yu
Joanna Bitton
Joe Spisak
Jongsoo Park
Joseph Rocca
J. Johnstun
Joshua Saxe
Junteng Jia
Kalyan Vasuden Alwala
Karthik Prasad
Kartikeya Upasani
Kate Plawiak
Keqian Li
Kenneth Heafield
Kevin R. Stone
Khalid El-Arini
Krithika Iyer
Kshitiz Malik
Kuen-ley Chiu
Kunal Bhalla
Kushal Lakhotia
Lauren Rantala-Yeary
Laurens van der Maaten
Lawrence Chen
Liang Tan
Liz Jenkins
Louis Martin
Lovish Madaan
Lubo Malo
Lukas Blecher
Lukas Landzaat
Luke de Oliveira
Madeline Muzzi
Mahesh Pasupuleti
Mannat Singh
Manohar Paluri
Marcin Kardas
Maria Tsimpoukelli
Mathew Oldham
Mathieu Rita
Maya Pavlova
Melanie Kam-badur
Mike Lewis
Mitesh Min Si
Kumar Singh
Mona Hassan
Naman Goyal
Narjes Torabi
Niko-lay Bashlykov
Nikolay Bogoychev
Niladri S. Chatterji
Ning Zhang
Olivier Duchenne
Onur Çelebi
Patrick Alrassy
Petar Pengwei Li
Peter Weng
Prajjwal Bhargava
Pratik Dubal
Punit Praveen Krishnan
Singh Koura
Puxin Xu
Qing He
Qingxiao Dong
Ragavan Srinivasan
Raj Ganapathy
Ramon Calderer
Ricardo Silveira Cabral
Robert Stojnic
Roberta Raileanu
Rohan Maheswari
Rohit Girdhar
Rohit Patel
Ro-main Sauvestre
Ron-nie Polidoro
Roshan Sumbaly
Ross Taylor
Ruan Silva
Rui Hou
Rui Wang
S. Hosseini
Sa-hana Chennabasappa
Sanjay Singh
Sean Bell
Seo-hyun Sonia Kim
Sergey Edunov
Shaoliang Nie
Sharan Narang
Sharath Chandra Raparthy
Sheng Shen
Shengye Wan
Shruti Bhosale
Shun Zhang
Simon Van-denhende
Soumya Batra
Spencer Whitman
Sten Sootla
Stephane Collot
Suchin Gururangan
S. Borodinsky
Tamar Herman
Tara Fowler
Tarek Sheasha
Thomas Georgiou
Thomas Scialom
Tobias Speckbacher
Todor Mihaylov
Tong Xiao
Ujjwal Karn
Vedanuj Goswami
Vibhor Gupta
Vignesh Ramanathan
Viktor Kerkez
Vincent Gonguet
Vir-ginie Do
Vish Vogeti
Vitor Albiero
Vladan Petro-vic
Weiwei Chu
Wenhan Xiong
Wenyin Fu
2025-05-27
ArXiv (preprint)
doi.org
arxiv.org
Multi-Objective GFlowNets
Moksh Jain
Sharath Chandra Raparthy
Alex Hernandez-Garcia
Jarrid Rector-Brooks
Yoshua Bengio
Santiago Miret
Emmanuel Bengio
We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learni… (see more)ng such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of some underlying property of interest, making it important to generate diverse candidates to have multiple options for expensive downstream evaluations. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC, which models a family of independent sub-problems defined by a scalarization function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a sequence of sub-problems defined by an acquisition function in an active learning loop. Our experiments on wide variety of synthetic and benchmark tasks demonstrate advantages of the proposed methods in terms of the Pareto performance and importantly, improved candidate diversity, which is the main contribution of this work.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Compositional Attention: Disentangling Search and Retrieval
Sarthak Mittal
Sharath Chandra Raparthy
Irina Rish
Yoshua Bengio
Guillaume Lajoie
Multi-head, key-value attention is the backbone of the widely successful Transformer model and its variants. This attention mechanism uses m… (see more)ultiple parallel key-value attention blocks (called heads), each performing two fundamental computations: (1) search - selection of a relevant entity from a set via query-key interactions, and (2) retrieval - extraction of relevant features from the selected entity via a value matrix. Importantly, standard attention heads learn a rigid mapping between search and retrieval. In this work, we first highlight how this static nature of the pairing can potentially: (a) lead to learning of redundant parameters in certain tasks, and (b) hinder generalization. To alleviate this problem, we propose a novel attention mechanism, called Compositional Attention, that replaces the standard head structure. The proposed mechanism disentangles search and retrieval and composes them in a dynamic, flexible and context-dependent manner through an additional soft competition stage between the query-key combination and value pairing. Through a series of numerical experiments, we show that it outperforms standard multi-head attention on a variety of tasks, including some out-of-distribution settings. Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed. Our proposed mechanism generalizes multi-head attention, allows independent scaling of search and retrieval, and can easily be implemented in lieu of standard attention heads in any network architecture.
2022-04-24
International Conference on Learning Representations (Accept (Spotlight))
doi.org
openreview.net
Continual Learning In Environments With Polynomial Mixing Times
Matthew Riemer
Sharath Chandra Raparthy
Ignacio Cases
Gopeshh Subbaraj
Maximilian Puelma Touzel
Irina Rish
The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios. Yet, the effect of mi… (see more)xing times on learning in continual reinforcement learning (RL) remains underexplored. In this paper, we characterize problems that are of long-term interest to the development of continual RL, which we call scalable MDPs, through the lens of mixing times. In particular, we theoretically establish that scalable MDPs have mixing times that scale polynomially with the size of the problem. We go on to demonstrate that polynomial mixing times present significant difficulties for existing approaches, which suffer from myopic bias and stale bootstrapped estimates. To validate our theory, we study the empirical scaling behavior of mixing times with respect to the number of tasks and task duration for high performing policies deployed across multiple Atari games. Our analysis demonstrates both that polynomial mixing times do emerge in practice and how their existence may lead to unstable learning behavior like catastrophic forgetting in continual learning settings.
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)
doi.org
openreview.net
Curriculum in Gradient-Based Meta-Reinforcement Learning
Bhairav Mehta
Tristan Deleu
Sharath Chandra Raparthy
Christopher Pal
Liam Paull
Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML) have shown strong few-shot performance in supervised and reinforcem… (see more)ent learning settings. However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions. With the wrong curriculum, agents suffer the effects of meta-overfitting, shallow adaptation, and adaptation instability. In this work, we begin by highlighting intriguing failure cases of gradient-based meta-RL and show that task distributions can wildly affect algorithmic outputs, stability, and performance. To address this problem, we leverage insights from recent literature on domain randomization and propose meta Active Domain Randomization (meta-ADR), which learns a curriculum of tasks for gradient-based meta-RL in a similar as ADR does for sim2real transfer. We show that this approach induces more stable policies on a variety of simulated locomotion and navigation tasks. We assess in- and out-of-distribution generalization and find that the learned task distributions, even in an unstructured task space, greatly improve the adaptation performance of MAML. Finally, we motivate the need for better benchmarking in meta-RL that prioritizes \textit{generalization} over single-task adaption performance.
2020-02-18
ArXiv (preprint)
arxiv.org
arxiv.org