Optimal allocation of a divisible good to strategic buyers. - Sujay Sanghavi; Bruce E. Hajek (CDC, 2004)

Intermediate Performance of Rateless Codes - Sujay Sanghavi (CoRR, 2006)

Adaptive induced fluctuations for multiuser diversity. - Sujay Sanghavi; Bruce E. Hajek (IEEE Trans. Wirel. Commun., 2006)

Decentralized Network Algorithms - Sujay Sanghavi (2006)

Linear programming analysis of loopy belief propagation for weighted matching. - Sujay Sanghavi; Dmitry M. Malioutov; Alan S. Willsky (NIPS, 2007)

Message Passing for Max-weight Independent Set. - Sujay Sanghavi; Devavrat Shah; Alan S. Willsky (NIPS, 2007)

Equivalence of LP Relaxation and Max-Product for Weighted Matching in General Graphs - Sujay Sanghavi (CoRR, 2007)

Gossiping With Multiple Messages. - Sujay Sanghavi; Bruce E. Hajek; Laurent Massoulié (IEEE Trans. Inf. Theory, 2007)

Networking sensors using belief propagation. - Sujay Sanghavi; Dmitry Malioutov; Alan S. Willsky (Allerton, 2008)

Compressed sensing with sequential observations. - Dmitry M. Malioutov; Sujay Sanghavi; Alan S. Willsky (ICASSP, 2008)

A New Mechanism for the Free-Rider Problem. - Sujay Sanghavi; Bruce E. Hajek (IEEE Trans. Autom. Control., 2008)

Sparse and low-rank matrix decompositions. - Venkat Chandrasekaran; Sujay Sanghavi; Pablo A. Parrilo; Alan S. Willsky (Allerton, 2009)

Is it enough to drain the heaviest bottlenecks? - Gagan Raj Gupta 001; Sujay Sanghavi; Ness B. Shroff (Allerton, 2009)

Conferencing on trees. - Ali Jalali; David James Love; Sujay Sanghavi; Bertrand M. Hochwald (CISS, 2009)

Some fundamental coding theoretic limits of unequal error protection. - Shashi Borade; Sujay Sanghavi (ISIT, 2009)

Node weighted scheduling. - Gagan Raj Gupta 001; Sujay Sanghavi; Ness B. Shroff (SIGMETRICS/Performance, 2009)

Workload optimality in switches without arrivals. - Gagan Raj Gupta 001; Sujay Sanghavi; Ness B. Shroff (SIGMETRICS Perform. Evaluation Rev., 2009)

Message passing for maximum weight independent set. - Sujay Sanghavi; Devavrat Shah; Alan S. Willsky (IEEE Trans. Inf. Theory, 2009)

Distributed link scheduling with constant overhead. - Loc Bui; Sujay Sanghavi; R. Srikant 001 (IEEE/ACM Trans. Netw., 2009)

A general framework for high-dimensional estimation in the presence of incoherence. - Yuxin Chen; Sujay Sanghavi (Allerton, 2010)

Greedy learning of Markov network structure. - Praneeth Netrapalli; Siddhartha Banerjee; Sujay Sanghavi; Sanjay Shakkottai (Allerton, 2010)

A Dirty Model for Multi-task Learning. - Ali Jalali; Pradeep Ravikumar; Sujay Sanghavi; Chao Ruan (NIPS, 2010)

Sequential Compressed Sensing. - Dmitry M. Malioutov; Sujay Sanghavi; Alan S. Willsky (IEEE J. Sel. Top. Signal Process., 2010)

Learning graphical models for hypothesis testing and classification. - Vincent Y. F. Tan; Sujay Sanghavi; John W. Fisher III; Alan S. Willsky (IEEE Trans. Signal Process., 2010)

Robust Matrix Completion and Corrupted Columns. - Yudong Chen 001; Huan Xu 001; Constantine Caramanis; Sujay Sanghavi (ICML, 2011)

Robust Matrix Completion with Corrupted Columns - Yudong Chen 001; Huan Xu 001; Constantine Caramanis; Sujay Sanghavi (CoRR, 2011)

Learning with Latent Factors in Time Series - Ali Jalali; Sujay Sanghavi (CoRR, 2011)

On Learning Discrete Graphical Models using Group-Sparse Regularization. - Ali Jalali; Pradeep Ravikumar; Vishvas Vasuki; Sujay Sanghavi (AISTATS, 2011)

Rank-Sparsity Incoherence for Matrix Decomposition. - Venkat Chandrasekaran; Sujay Sanghavi; Pablo A. Parrilo; Alan S. Willsky (SIAM J. Optim., 2011)

Belief Propagation and LP Relaxation for Weighted Matching in General Graphs. - Sujay Sanghavi; Dmitry M. Malioutov; Alan S. Willsky (IEEE Trans. Inf. Theory, 2011)

Online load balancing and correlated randomness. - Sharayu Moharir; Sujay Sanghavi (Allerton Conference, 2012)

Greedy learning of graphical models with small girth. - Avik Ray; Sujay Sanghavi; Sanjay Shakkottai (Allerton Conference, 2012)

Learning the Dependence Graph of Time Series with Latent Factors. - Ali Jalali; Sujay Sanghavi (ICML, 2012)

On the effect of channel fading on greedy scheduling. - Aneesh Reddy; Sujay Sanghavi; Sanjay Shakkottai (INFOCOM, 2012)

Learning Markov graphs up to edit distance. - Abhik Kumar Das; Praneeth Netrapalli; Sujay Sanghavi; Sriram Vishwanath (ISIT, 2012)

Clustering Sparse Graphs. - Yudong Chen 001; Sujay Sanghavi; Huan Xu 001 (NIPS, 2012)

Learning the graph of epidemic cascades. - Praneeth Netrapalli; Sujay Sanghavi (SIGMETRICS, 2012)

Greedy dirty models: A new algorithm for multiple sparse regression. - Ali Jalali; Sujay Sanghavi (SSP, 2012)

Finding the Graph of Epidemic Cascades - Praneeth Netrapalli; Sujay Sanghavi (CoRR, 2012)

A New Greedy Algorithm for Multiple Sparse Regression - Ali Jalali; Sujay Sanghavi (CoRR, 2012)

Robust PCA via Outlier Pursuit. - Huan Xu 001; Constantine Caramanis; Sujay Sanghavi (IEEE Trans. Inf. Theory, 2012)

Learning the causal graph of Markov time series. - Avhishek Chatterjee; Ankit Singh Rawat; Sriram Vishwanath; Sujay Sanghavi (Allerton, 2013)

Low-rank matrix completion using alternating minimization. - Prateek Jain 002; Praneeth Netrapalli; Sujay Sanghavi (STOC, 2013)

Low-Rank Matrix Recovery From Errors and Erasures. - Yudong Chen 001; Ali Jalali; Sujay Sanghavi; Constantine Caramanis (IEEE Trans. Inf. Theory, 2013)

A Dirty Model for Multiple Sparse Regression. - Ali Jalali; Pradeep Ravikumar; Sujay Sanghavi (IEEE Trans. Inf. Theory, 2013)

Overlap graph clustering via successive removal. - Avik Ray; Javad Ghaderi; Sujay Sanghavi; Sanjay Shakkottai (Allerton, 2014)

Coherent Matrix Completion. - Yudong Chen 001; Srinadh Bhojanapalli; Sujay Sanghavi; Rachel A. Ward (ICML, 2014)

Alternating Minimization for Mixed Linear Regression. - Xinyang Yi; Constantine Caramanis; Sujay Sanghavi (ICML, 2014)

Learning structure of power-law Markov networks. - Abhik Kumar Das; Praneeth Netrapalli; Sujay Sanghavi; Sriram Vishwanath (ISIT, 2014)

Non-convex Robust PCA. - Praneeth Netrapalli; U. N. Niranjan; Sujay Sanghavi; Animashree Anandkumar; Prateek Jain 002 (NIPS, 2014)

Greedy Subspace Clustering. - Dohyung Park; Constantine Caramanis; Sujay Sanghavi (NIPS, 2014)

Topic modeling from network spread. - Avik Ray; Sujay Sanghavi; Sanjay Shakkottai (SIGMETRICS, 2014)

Clustering partially observed graphs via convex optimization. - Yudong Chen 001; Ali Jalali; Sujay Sanghavi; Huan Xu 001 (J. Mach. Learn. Res., 2014)

Improved Graph Clustering. - Yudong Chen 001; Sujay Sanghavi; Huan Xu 001 (IEEE Trans. Inf. Theory, 2014)

Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons. - Dohyung Park; Joe Neeman; Jin Zhang; Sujay Sanghavi; Inderjit S. Dhillon (ICML, 2015)

Convergence Rates of Active Learning for Maximum Likelihood Estimation. - Kamalika Chaudhuri; Sham M. Kakade; Praneeth Netrapalli; Sujay Sanghavi (NIPS, 2015)

Tighter Low-rank Approximation via Sampling the Leveraged Element. - Srinadh Bhojanapalli; Prateek Jain 002; Sujay Sanghavi (SODA, 2015)

A New Sampling Technique for Tensors. - Srinadh Bhojanapalli; Sujay Sanghavi (CoRR, 2015)

Completing any low-rank matrix, provably. - Yudong Chen 001; Srinadh Bhojanapalli; Sujay Sanghavi; Rachel A. Ward (J. Mach. Learn. Res., 2015)

Serving content with unknown demand: the high-dimensional regime. - Sharayu Moharir; Javad Ghaderi; Sujay Sanghavi; Sanjay Shakkottai (Queueing Syst. Theory Appl., 2015)

Improved Greedy Algorithms for Learning Graphical Models. - Avik Ray; Sujay Sanghavi; Sanjay Shakkottai (IEEE Trans. Inf. Theory, 2015)

Phase Retrieval Using Alternating Minimization. - Praneeth Netrapalli; Prateek Jain 002; Sujay Sanghavi (IEEE Trans. Signal Process., 2015)

Finding low-rank solutions to smooth convex problems via the Burer-Monteiro approach. - Dohyung Park; Anastasios Kyrillidis; Constantine Caramanis; Sujay Sanghavi (Allerton, 2016)

Dropping Convexity for Faster Semi-definite Optimization. - Srinadh Bhojanapalli; Anastasios Kyrillidis; Sujay Sanghavi (COLT, 2016)

Structurally-constrained gradient descent for matrix factorization in haplotype assembly problems. - Changxiao Cai; Sujay Sanghavi; Haris Vikalo (ICASSP, 2016)

Normalized Spectral Map Synchronization. - Yanyao Shen; Qixing Huang; Nati Srebro; Sujay Sanghavi (NIPS, 2016)

Single Pass PCA of Matrix Products. - Shanshan Wu; Srinadh Bhojanapalli; Sujay Sanghavi; Alexandros G. Dimakis (NIPS, 2016)

Provable non-convex projected gradient descent for a class of constrained matrix optimization problems. - Dohyung Park; Anastasios Kyrillidis; Srinadh Bhojanapalli; Constantine Caramanis; Sujay Sanghavi (CoRR, 2016)

Finding Low-rank Solutions to Matrix Problems, Efficiently and Provably. - Dohyung Park; Anastasios Kyrillidis; Constantine Caramanis; Sujay Sanghavi (CoRR, 2016)

Trading-off variance and complexity in stochastic gradient descent. - Vatsal Shah; Megasthenis Asteris; Anastasios Kyrillidis; Sujay Sanghavi (CoRR, 2016)

Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization. - Xinyang Yi; Constantine Caramanis; Sujay Sanghavi (CoRR, 2016)

Online Collaborative Filtering on Graphs. - Siddhartha Banerjee; Sujay Sanghavi; Sanjay Shakkottai (Oper. Res., 2016)

Structured Low-Rank Matrix Factorization for Haplotype Assembly. - Changxiao Cai; Sujay Sanghavi; Haris Vikalo (IEEE J. Sel. Top. Signal Process., 2016)

Matrix Completion With Column Manipulation: Near-Optimal Sample-Robustness-Rank Tradeoffs. - Yudong Chen 001; Huan Xu 001; Constantine Caramanis; Sujay Sanghavi (IEEE Trans. Inf. Theory, 2016)

Online Load Balancing Under Graph Constraints. - Sharayu Moharir; Sujay Sanghavi; Sanjay Shakkottai (IEEE/ACM Trans. Netw., 2016)

Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach. - Dohyung Park; Anastasios Kyrillidis; Constantine Caramanis; Sujay Sanghavi (AISTATS, 2017)

Sparse Quadratic Logistic Regression in Sub-quadratic Time. - Karthikeyan Shanmugam; Murat Kocaoglu; Alexandros G. Dimakis; Sujay Sanghavi (CoRR, 2017)

Provable quantum state tomography via non-convex methods. - Anastasios Kyrillidis; Amir Kalev; Dohyung Park; Srinadh Bhojanapalli; Constantine Caramanis; Sujay Sanghavi (CoRR, 2017)

The Search Problem in Mixture Models. - Avik Ray; Joe Neeman; Sujay Sanghavi; Sanjay Shakkottai (J. Mach. Learn. Res., 2017)

The Sparse Recovery Autoencoder. - Shanshan Wu; Alexandros G. Dimakis; Sujay Sanghavi; Felix X. Yu; Daniel Niels Holtmann-Rice; Dmitry Storcheus; Afshin Rostamizadeh; Sanjiv Kumar (CoRR, 2018)

Iteratively Learning from the Best. - Yanyao Shen; Sujay Sanghavi (CoRR, 2018)

Finding Low-Rank Solutions via Nonconvex Matrix Factorization, Efficiently and Provably. - Dohyung Park; Anastasios Kyrillidis; Constantine Caramanis; Sujay Sanghavi (SIAM J. Imaging Sci., 2018)

Searching for a Single Community in a Graph. - Avik Ray; Sujay Sanghavi; Sanjay Shakkottai (ACM Trans. Model. Perform. Evaluation Comput. Syst., 2018)

Learning with Bad Training Data via Iterative Trimmed Loss Minimization. - Yanyao Shen; Sujay Sanghavi (ICML, 2019)

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling. - Shanshan Wu; Alex Dimakis; Sujay Sanghavi; Felix X. Yu; Daniel Niels Holtmann-Rice; Dmitry Storcheus; Afshin Rostamizadeh; Sanjiv Kumar (ICML, 2019)

Blocking Bandits. - Soumya Basu 001; Rajat Sen; Sujay Sanghavi; Sanjay Shakkottai (NeurIPS, 2019)

Iterative Least Trimmed Squares for Mixed Linear Regression. - Yanyao Shen; Sujay Sanghavi (NeurIPS, 2019)

Learning Distributions Generated by One-Layer ReLU Networks. - Shanshan Wu; Alexandros G. Dimakis; Sujay Sanghavi (NeurIPS, 2019)

Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models. - Shanshan Wu; Sujay Sanghavi; Alexandros G. Dimakis (NeurIPS, 2019)

Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space. - Shuo Yang; Yanyao Shen; Sujay Sanghavi (NeurIPS, 2019)

PruneTrain: fast neural network training by dynamic sparse model reconfiguration. - Sangkug Lym; Esha Choukse; Siavash Zangeneh; Wei Wen; Sujay Sanghavi; Mattan Erez (SC, 2019)

Choosing the Sample with Lowest Loss makes SGD Robust. - Vatsal Shah; Xiaoxia Wu; Sujay Sanghavi (AISTATS, 2020)

Extreme Multi-label Classification from Aggregated Labels. - Yanyao Shen; Hsiang-Fu Yu; Sujay Sanghavi; Inderjit S. Dhillon (ICML, 2020)

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization. - Abolfazl Hashemi; Anish Acharya; Rudrajit Das; Haris Vikalo; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2020)

On Generalization of Adaptive Methods for Over-parameterized Linear Regression. - Vatsal Shah; Soumya Basu 001; Anastasios Kyrillidis; Sujay Sanghavi (CoRR, 2020)

Improved Convergence Rates for Non-Convex Federated Learning with Compression. - Rudrajit Das; Abolfazl Hashemi; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2020)

Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification. - Tavor Z. Baharav; Daniel L. Jiang; Kedarnath Kolluri; Sujay Sanghavi; Inderjit S. Dhillon (CIKM, 2021)

Nearly Horizon-Free Offline Reinforcement Learning. - Tongzheng Ren; Jialian Li; Bo Dai 001; Simon S. Du; Sujay Sanghavi (NeurIPS, 2021)

Combinatorial Bandits without Total Order for Arms. - Shuo Yang; Tongzheng Ren; Inderjit S. Dhillon; Sujay Sanghavi (CoRR, 2021)

DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning. - Rudrajit Das; Abolfazl Hashemi; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2021)

Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. - Anish Acharya; Abolfazl Hashemi; Prateek Jain 002; Sujay Sanghavi; Inderjit S. Dhillon; Ufuk Topcu (AISTATS, 2022)

Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. - Tongzheng Ren; Fuheng Cui; Alexia Atsidakou; Sujay Sanghavi; Nhat Ho (AISTATS, 2022)

Asymptotically-Optimal Gaussian Bandits with Side Observations. - Alexia Atsidakou; Orestis Papadigenopoulos; Constantine Caramanis; Sujay Sanghavi; Sanjay Shakkottai (ICML, 2022)

Linear Bandit Algorithms with Sublinear Time Complexity. - Shuo Yang; Tongzheng Ren; Sanjay Shakkottai; Eric Price 001; Inderjit S. Dhillon; Sujay Sanghavi (ICML, 2022)

Minimax Regret for Cascading Bandits. - Daniel Vial; Sujay Sanghavi; Sanjay Shakkottai; R. Srikant 001 (NeurIPS, 2022)

Toward Understanding Privileged Features Distillation in Learning-to-Rank. - Shuo Yang; Sujay Sanghavi; Holakou Rahmanian; Jan Bakus; S. V. N. Vishwanathan (NeurIPS, 2022)

Faster non-convex federated learning via global and local momentum. - Rudrajit Das; Anish Acharya; Abolfazl Hashemi; Sujay Sanghavi; Inderjit S. Dhillon; Ufuk Topcu (UAI, 2022)

Improving Computational Complexity in Statistical Models with Second-Order Information. - Tongzheng Ren; Jiacheng Zhuo; Sujay Sanghavi; Nhat Ho (CoRR, 2022)

An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models. - Nhat Ho; Tongzheng Ren; Sujay Sanghavi; Purnamrita Sarkar; Rachel A. Ward (CoRR, 2022)

Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures. - Tongzheng Ren; Fuheng Cui; Sujay Sanghavi; Nhat Ho (CoRR, 2022)

Positive Unlabeled Contrastive Learning. - Anish Acharya; Sujay Sanghavi; Li Jing 001; Bhargav Bhushanam; Dhruv Choudhary; Michael G. Rabbat; Inderjit S. Dhillon (CoRR, 2022)

On the Value of Behavioral Representations for Dense Retrieval. - Nan Jiang 012; Dhivya Eswaran; Choon Hui Teo; Yexiang Xue; Yesh Dattatreya; Sujay Sanghavi; Vishy Vishwanathan (CoRR, 2022)

Bayesian Fixed-Budget Best-Arm Identification. - Alexia Atsidakou; Sumeet Katariya; Sujay Sanghavi; Branislav Kveton (CoRR, 2022)

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. - Abolfazl Hashemi; Anish Acharya; Rudrajit Das; Haris Vikalo; Sujay Sanghavi; Inderjit S. Dhillon (IEEE Trans. Parallel Distributed Syst., 2022)

Sample Efficiency of Data Augmentation Consistency Regularization. - Shuo Yang; Yijun Dong; Rachel A. Ward; Inderjit S. Dhillon; Sujay Sanghavi; Qi Lei (AISTATS, 2023)

Latent Variable Representation for Reinforcement Learning. - Tongzheng Ren; Chenjun Xiao; Tianjun Zhang; Na Li 002; Zhaoran Wang 001; Sujay Sanghavi; Dale Schuurmans; Bo Dai 001 (ICLR, 2023)

Beyond Uniform Lipschitz Condition in Differentially Private Optimization. - Rudrajit Das; Satyen Kale; Zheng Xu 002; Tong Zhang 001; Sujay Sanghavi (ICML, 2023)

Understanding Self-Distillation in the Presence of Label Noise. - Rudrajit Das; Sujay Sanghavi (ICML, 2023)

Logarithmic Bayes Regret Bounds. - Alexia Atsidakou; Branislav Kveton; Sumeet Katariya; Constantine Caramanis; Sujay Sanghavi (NeurIPS, 2023)

Understanding the Effectiveness of Early Weight Averaging for Training Large Language Models. - Sunny Sanyal; Jean Kaddour; Abhishek Kumar; Sujay Sanghavi (CoRR, 2023)

Improving Computational Complexity in Statistical Models with Local Curvature Information. - Pedram Akbarian; Tongzheng Ren; Jiacheng Zhuo; Sujay Sanghavi; Nhat Ho (ICML, 2024)

Understanding the Training Speedup from Sampling with Approximate Losses. - Rudrajit Das; Xi Chen; Bertram Ieong; Parikshit Bansal; Sujay Sanghavi (ICML, 2024)

Time Weaver: A Conditional Time Series Generation Model. - Sai Shankar Narasimhan; Shubhankar Agarwal; Oguzhan Akcin; Sujay Sanghavi; Sandeep P. Chinchali (ICML, 2024)

In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness. - Liam Collins; Advait Parulekar; Aryan Mokhtari; Sujay Sanghavi; Sanjay Shakkottai (NeurIPS, 2024)

Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. - Ruichen Jiang; Ali Kavis; Qiujiang Jin; Sujay Sanghavi; Aryan Mokhtari (NeurIPS, 2024)

DataComp-LM: In search of the next generation of training sets for language models. - Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Yitzhak Gadre; Hritik Bansal; Etash Kumar Guha; Sedrick Scott Keh; Kushal Arora; Saurabh Garg; Rui Xin; et al. (NeurIPS, 2024)

SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors. - Vijay Lingam; Atula Neerkaje; Aditya Vavre; Aneesh Shetty; Gautham Krishna Gudur; Joydeep Ghosh; Eunsol Choi; Alex Dimakis; Aleksandar Bojchevski; Sujay Sanghavi (NeurIPS, 2024)

Contrastive Approach to Prior Free Positive Unlabeled Learning. - Anish Acharya; Sujay Sanghavi (CoRR, 2024)

Towards Quantifying the Preconditioning Effect of Adam. - Rudrajit Das; Naman Agarwal; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2024)

Pre-training Small Base LMs with Fewer Tokens. - Sunny Sanyal; Sujay Sanghavi; Alexandros G. Dimakis (CoRR, 2024)

SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors. - Vijay Lingam; Atula Tejaswi; Aditya Vavre; Aneesh Shetty; Gautham Krishna Gudur; Joydeep Ghosh; Alex Dimakis; Eunsol Choi; Aleksandar Bojchevski; Sujay Sanghavi (CoRR, 2024)

Retraining with Predicted Hard Labels Provably Increases Model Accuracy. - Rudrajit Das; Inderjit S. Dhillon; Alessandro Epasto; Adel Javanmard; Jieming Mao; Vahab Mirrokni; Sujay Sanghavi; Peilin Zhong (CoRR, 2024)

Geometric Median (GM) Matching for Robust Data Pruning. - Anish Acharya; Inderjit S. Dhillon; Sujay Sanghavi (CoRR, 2024)

RARe: Retrieval Augmented Retrieval with In-Context Examples. - Atula Tejaswi; Yoonsang Lee 004; Sujay Sanghavi; Eunsol Choi (CoRR, 2024)

Understanding Contrastive Learning via Gaussian Mixture Models. - Parikshit Bansal; Ali Kavis; Sujay Sanghavi (CoRR, 2024)

Learning Mixtures of Experts with EM. - Quentin Fruytier; Aryan Mokhtari; Sujay Sanghavi (CoRR, 2024)

Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity. - Charlie Hou; Kiran Koshy Thekumparampil; Michael Shavlovsky; Giulia Fanti; Yesh Dattatreya; Sujay Sanghavi (Trans. Mach. Learn. Res., 2024)

Enhancing Language Model Agents using Diversity of Thoughts. - Vijay Lingam; Behrooz Omidvar-Tehrani; Sujay Sanghavi; Gaurav Gupta; Sayan Ghosh; Linbo Liu; Jun Huan; Anoop Deoras (ICLR, 2025)

Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models. - Negin Raoof; Litu Rout; Giannis Daras; Sujay Sanghavi; Constantine Caramanis; Sanjay Shakkottai; Alex Dimakis (ICLR, 2025)

InfoPO: On Mutual Information Maximization for Large Language Model Alignment. - Teng Xiao; Zhen Ge; Sujay Sanghavi; Tian Wang; Julian Katz-Samuels; Marc Versage; Qingjun Cui; Trishul Chilimbi (NAACL (Long Papers), 2025)

Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting. - Sunny Sanyal; Hayden Prairie; Rudrajit Das; Ali Kavis; Sujay Sanghavi (CoRR, 2025)

Geometric Median Matching for Robust k-Subset Selection from Noisy Data. - Anish Acharya; Sujay Sanghavi; Alexandros G. Dimakis; Inderjit S. Dhillon (CoRR, 2025)

Context-Free Synthetic Data Mitigates Forgetting. - Parikshit Bansal; Sujay Sanghavi (CoRR, 2025)