Arindam Banerjee
Founder Professor in Engineering
Studies machine learning theory and methods, including deep learning generalization, optimization and probabilistic models for climate and other data.
Arindam Banerjee's lab at UIUC works on learning theory & optimization, probabilistic & causal inference and data management & big data. 20 papers since 2023, 15 at top venues, mostly NeurIPS and ICLR. 11 PhD students and 1 master's student. 34 alumni, most often to Amazon and Google. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 75%
- PhD students
- 11
- MS students
- 1
- alumni listed
- 34
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 1 · collecting
- 9 papers2025 · T1 8
- 3 papers2024 · T1 3
- 4 papers2023 · T1 3
T1 venuesOtherVenues: NeurIPS, ICLR, UAI, AISTATS, ICML
Research focuspaper titles that use each topic's words
- Learning theory & optimization8 papers
- Probabilistic & causal inference1 paper
- Data management & big data1 paper
- climate0 papers
- probabilistic models0 papers
Paper list
From the lab homepage.
20264 papers
- AISTATS (T1)Beyond Johnson-Lindenstrauss: Uniform Bounds for Sketched Bilinear Forms
- ICLR WorkshopGradual Fine-Tuning for Flow Matching Models
- ICLR WorkshopModel Predictive Control with Differentiable World Models for Offline Reinforcement Learning
- International Society for Photogrammetry and Remote Sensing (ISPRS) CongressTorchGeo 1.0: Satellite Image Time Series, and Beyond!
20259 papers
- UAI (T1)Computationally Efficient Methods for Invariant Feature Selection with Sparsity
- ICLR (T1)Conservative Contextual Bandits: Beyond Linear Representations
- AISTATS (T1)Loss Gradient Gaussian Width based Generalization and Optimization Guarantees
- UAI (T1)Multi-Stage Probabilistic Generative Super Resolution with Scarce High-Resolution Data
- IGARSSOn the Generalizability of Foundation Models for Crop Type Mapping
- TMLR (T1)Optimization and Generalization Guarantees for Weight Normalization
- ICML (T1)Optimization for Neural Operators can Benefit from Width
- NeurIPS (T1)Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis
- NeurIPS (T1)Sketched Gaussian Mechanism for Private Federated Learning
20243 papers
- ICLR (T1)Contextual Bandits with Online Neural Regression
- NeurIPS (T1)Robust Neural Contextual Bandit against Adversarial Corruptions
- NeurIPS (T1)Sketching for Distributed Deep Learning: A Sharper Analysis
20234 papers
- NeurIPS'23 Workhop on Deep Learning and Inverse ProblemsAmbientFlow: Invertible generative models from incomplete, noisy measurements
- UAI (T1)Neural tangent kernel at initialization: Linear width suffices
- ICLR (T1)Restricted Strong Convexity of Deep Learning Models with Smooth Activations
- NeurIPS (T1)SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
Where alumni went34 listed, 79% to industry
Industry 15Academia 4
Most often to
- Amazon
- Microsoft
Also to
- TUM
- University of Buffalo
- University of Pennsylvania
Apply to UIUCFall 2027
UIUC Siebel Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL iBT / IELTS
- Fee
- USD 90
Not yet confirmed for this intake.
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- Papers
- The lab homepage
- Members
- Lab homepage
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- Lab homepage
Tiers are venue classifications, not lab rankings.
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