The Institute for Emerging CORE Methods in Data Science

Breaking the Sorting Barrier for Directed Single Source Shortest Paths
29:07
Breaking the Sorting Barrier for Directed Single Source Shortest Paths
On the Hardness Hierarchy for On√log n Complexity in Word RAM
32:54
On the Hardness Hierarchy for On√log n Complexity in Word RAM
Fine Grained Complexity as a Guide to Faster Algorithms Lessons from All Pairs Max Flo
23:47
Fine Grained Complexity as a Guide to Faster Algorithms Lessons from All Pairs Max Flo
Effect of Sparsity on k Dominating Set and First Order Graph Properties
30:57
Effect of Sparsity on k Dominating Set and First Order Graph Properties
Deterministic Fully Sparse Matrix Multiplication Using Coding Theory
24:08
Deterministic Fully Sparse Matrix Multiplication Using Coding Theory
Regularity in Hyper Clique Detection & Optimizing Boolean CSPs
26:29
Regularity in Hyper Clique Detection & Optimizing Boolean CSPs
Congestion Approximators from the Bottom Up
39:05
Congestion Approximators from the Bottom Up
Completeness Theorems for k SUM and Geometric Friends
27:39
Completeness Theorems for k SUM and Geometric Friends
Computational Complexity of Self-Attention
27:35
Computational Complexity of Self-Attention
Online Context Free Recognition in OMv Time
25:26
Online Context Free Recognition in OMv Time
Fine Grained Complexity of Ambiguity Problems on Automata
32:01
Fine Grained Complexity of Ambiguity Problems on Automata
How to Plant Orthogonal Vectors
38:14
How to Plant Orthogonal Vectors
Revisiting PAC Learning - Kasper Green Larsen
50:36
Revisiting PAC Learning - Kasper Green Larsen
Exponentially faster gradient methods in the presence of ravines
50:23
Exponentially faster gradient methods in the presence of ravines
Forster-Warmuth Counterfactual Regression: A Unified Learning Approach
1:07:54
Forster-Warmuth Counterfactual Regression: A Unified Learning Approach
Doubly Robust and Efficient Calibration of Prediction Sets for Censored Time-to-Event Outcomes
56:11
Doubly Robust and Efficient Calibration of Prediction Sets for Censored Time-to-Event Outcomes
Toward Interpretable Deep Learning
1:00:07
Toward Interpretable Deep Learning
On White-Box Learning and Public-Key Encryption
2:02:01
On White-Box Learning and Public-Key Encryption
Is nasty noise actually harder than malicious noise?
1:00:53
Is nasty noise actually harder than malicious noise?
Toward Witness Encryption via NP-hardness of PAC Learning
35:15
Toward Witness Encryption via NP-hardness of PAC Learning
Learning vs the Deletion Channel
34:11
Learning vs the Deletion Channel
Synergy between Circuit Obfuscation and Circuit Minimization
1:03:45
Synergy between Circuit Obfuscation and Circuit Minimization
Reverse Mathematics of Complexity Lower Bounds
34:41
Reverse Mathematics of Complexity Lower Bounds
Is it (NP) hard to distinguish order from chaos?
56:13
Is it (NP) hard to distinguish order from chaos?
Towards Tractable Fragments for Skolem Synthesis
38:27
Towards Tractable Fragments for Skolem Synthesis
Smoothed Boosting Applied to the Theory of Cryptography
3:04:43
Smoothed Boosting Applied to the Theory of Cryptography
Tutorial: Frontiers of Efficient PAC Learning
1:24:55
Tutorial: Frontiers of Efficient PAC Learning
Model Stealing for Low Rank Language Models
47:01
Model Stealing for Low Rank Language Models
An Arithmetic Circuit Lens on Deep Learning Architectures
45:33
An Arithmetic Circuit Lens on Deep Learning Architectures
Implicit Geometry of Next-token Prediction
36:12
Implicit Geometry of Next-token Prediction