Abhishek Shetty
Catherine M. and James E. Allchin Early-Career Assistant Professor
Studies learning theory at the intersection of machine learning, theoretical computer science and statistics, including smoothed online learning and data quality.
Abhishek Shetty's lab at Georgia Tech works on learning theory & optimization and data management & big data. 23 papers since 2023, 18 at top venues, mostly COLT and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 78%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 7 · collecting
- 3 papers2025 · T1 3
- 5 papers2024 · T1 4
- 6 papers2023 · T1 4
T1 venuesOtherVenues: COLT, NeurIPS, ICLR, ICML, ITCS
Research focuspaper titles that use each topic's words
- Learning theory & optimization6 papers
- Data management & big data3 papers
- theoretical computer0 papers
- studies learning0 papers
- theoretical0 papers
Paper list
From the lab homepage.
20269 papers
- COLT (T1)An Empirical Bayes Perspective on Heteroskedastic Mean Estimation
- COLT (T1)Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness
- ITCSDimension-Free Correlated Sampling for the Hypersimplex
- COLT (T1)Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
- ALTLearning with Monotone Adversarial Corruptions
- COLT (T1)Partition Function Estimation under Bounded f-Divergence
- ICLR (T1)Sequences of Logits Reveal the Low Rank Structure of Language Models
- ICML (T1)Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
- ICLR (T1)Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking
20253 papers
- COLT (T1)Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks
- ICML (T1)Low-Rank Thinning
- COLT (T1)The Space Complexity of Learning-Unlearning Algorithms
20245 papers
- University of California, BerkeleyLearning in a Changing World: Covariate Shift, Subset Selection and Optimal PAC Bounds
- COLT (T1)Omnipredictors for regression and the approximate rank of convex functions
- COLT (T1)On the performance of empirical risk minimization with smoothed data
- NeurIPS (T1)Oracle-efficient differentially private learning with public data
- NeurIPS (T1)Tolerant algorithms for learning with arbitrary covariate shift
20236 papers
- NeurIPS (T1)Adversarial resilience in sequential prediction via abstention
- FOCS and Journal of the ACMOptimal PAC bounds without uniform convergence
- NeurIPS (T1)Progressive ensemble distillation: Building ensembles for efficient inference
- ITCSSmooth Nash Equilibria: Algorithms and Complexity
- NeurIPS (T1)Smoothed analysis of sequential probability assignment
- COLT (T1)The One-Inclusion Graph Algorithm is not Always Optimal
Where alumni went
Alumni are not listed on the lab homepage.
Apply to Georgia TechFall 2027
Georgia Tech Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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