ALT 2024 will be held over 4 days. Each accepted paper will be presented as a 12-minute talk during area-based sessions. Additionally, this year we offer to all authors the option to bring and present a poster during a poster session after lunch. Posters corresponding to papers presented during the day should be put up either before the beginning of the first session or during the first coffee break. This way, there will be 9-13 posters per day and discussions around each presented work can continue informally.
| Sunday, 25 February | Monday, 26 February | |
|---|---|---|
| 9:00 – 10:00 | Opening remarks; Online Learning 1 | Neural Networks |
| 10:00 – 10:45 | Coffee break | Coffee break |
| 10:45 – 11:15 | Unsupervised and Semi-supervised Learning | Privacy 1 |
| 11:35 – 12:30 | Invited talk: Stefanie Jegelka Benefits of learning with symmetries: eigenvectors, graph representations and sample complexity | Invited talk: Gregory Valiant Memory and Energy: Two Bottlenecks for Learning |
| 12:30 – 13:30 | Lunch break | Lunch break |
| 13:30 – 14:15 | Posters | Posters |
| 14:15 – 15:15 | Optimization | Games and Bandits |
| 15:15 – 16:00 | Coffee break | 15:00 – 15:45: Coffee break |
| 16:00 – 16:45 | Reinforcement Learning | 15:45 – 16:45: Generalization Bounds |
| 16:45 – 17:15: Impromptu talks |
The second part of the conference will have slightly shorter lunch breaks to allow for a business meeting slot as well as some free hiking time.
| Tuesday, 27 February | Wednesday, 28 February | |
|---|---|---|
| 9:00 – 10:00 | Supervised Learning | 9:15-10:00: Query Learning |
| 10:00 – 10:45 | Coffee break | Coffee break |
| 10:45 – 11:30 | Online Learning 2 | Bandit Problems |
| 11:35 – 12:30 | Invited talk: Fan Chung Graham Clustering in graphs with high clustering coefficients | Invited talk: Gergely Neu Online-to-PAC Conversions: Generalization Bounds via Regret Analysis |
| 12:30 – 13:15 | Lunch break | Lunch break |
| 13:15 – 14:00 | Posters | Posters |
| 14:00 – 14:30 | Business meeting | 14:00 – 15:00: Learnability |
| 14:30 – 15:15 | Privacy 2 | |
| 15:15 – | Free afternoon, hike (sunset at 17:44) | 15:00: Closing remarks |
| Reception (18:00) |
Sunday, 25 February
Online Learning 1
Improving Adaptive Online Learning Using Refined Discretization
Online Infinite-Dimensional Regression: Learning Linear Operators
The Dimension of Self-Directed Learning
Unsupervised and Semi-supervised Learning
Concentration of empirical barycenters in metric spaces
Distances for Markov Chains, and Their Differentiation
Optimization
Dueling Optimization with a Monotone Adversary
RedEx: Beyond Fixed Representation Methods via Convex Optimization
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies
Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
Reinforcement Learning
The complexity of non-stationary reinforcement learning
Near-continuous time Reinforcement Learning for continuous state-action spaces
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
Monday, 26 February
Neural Networks
Universal Representation of Permutation-Invariant Functions on Vectors and Tensors
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
Computation with Sequences of Assemblies in a Model of the Brain
Provable Accelerated Convergence of Nesterov’s Momentum for Deep ReLU Neural Networks
Privacy 1
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
Not All Learnable Distributions are Privately Learnable
Games and Bandits
The Attractor of the Replicator Dynamic in Zero-Sum Games
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
Adversarial Contextual Bandits Go Kernelized
Generalization Bounds
Tight bounds for maximum $\ell_1$-margin classifiers
On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation
Efficient Agnostic Learning with Average Smoothness
Tight Bounds for Local Glivenko-Cantelli
Tuesday, 27 February
Supervised Learning
Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms
On the Computational Benefit of Multimodal Learning
Partially Interpretable Models with Guarantees on Coverage and Accuracy
Online Learning 2
Adversarial Online Collaborative Filtering
Corruption-Robust Lipschitz Contextual Search
Multiclass Online Learnability under Bandit Feedback
Privacy 2
Private PAC Learning May be Harder than Online Learning
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
Wednesday, 28 February
Query Learning
Learning Spanning Forests Optimally in Weighted Undirected Graphs with CUT queries
Agnostic Membership Query Learning with Nontrivial Savings: New Results and Techniques
Learning Hypertrees From Shortest Path Queries
Bandits Problems
Importance-Weighted Offline Learning Done Right
Optimal Regret Bounds for Collaborative Learning in Bandits
Online Recommendations for Agents with Discounted Adaptive Preferences
Learnability
Multiclass Learnability Does Not Imply Sample Compression
The Impossibility of Parallelizing Boosting
Learning bounded-degree polytrees with known skeleton