Merge pull request #7 from markcannon/gh-pages

Fixed page numbers and editor names

README.md

@@ -16,6 +16,7 @@ Published as Volume 242 by the Proceedings of Machine Learning Research on 11 Ju
 
 Volume Edited by:
   * Alessandro Abate
+  * Mark Cannon
   * Kostas Margellos
   * Antonis Papachristodoulou
 

_config.yml

@@ -15,10 +15,12 @@ id: l4dc2024
 month: 0
 tex_title: Proceedings of the 6th Annual Learning for Dynamics & Control Conference
 cycles: false
-bibtex_editor: Abate, Alessandro and Margellos, Kostas and Papachristodoulou, Antonis
+bibtex_editor: Abate, Alessandro and Cannon, Mark and Margellos, Kostas and Papachristodoulou, Antonis
 editor:
 - given: Alessandro
   family: Abate
+- given: Mark
+  family: Cannon
 - given: Kostas
   family: Margellos
 - given: Antonis
@@ -31,6 +33,7 @@ description: |
 
   Volume Edited by:
     Alessandro Abate
+    Mark Cannon
     Kostas Margellos
     Antonis Papachristodoulou
 

_posts/2024-06-11-agorio24a.md

@@ -15,10 +15,10 @@ publisher: PMLR
 issn: 2640-3498
 id: agorio24a
 month: 0
-tex_title: "{Multi-agent assignment via state augmented reinforcement learning}"
-firstpage: 1
-lastpage: 12
-page: 1-12
+tex_title: "Multi-agent assignment via state augmented reinforcement learning"
+firstpage: 1202
+lastpage: 1213
+page: 1202-1213
 order: 1
 cycles: false
 bibtex_author: Agorio, Leopoldo and Alen, Sean Van and Calvo-Fullana, Miguel and Paternain,

_posts/2024-06-11-aguiar24a.md

@@ -16,10 +16,10 @@ publisher: PMLR
 issn: 2640-3498
 id: aguiar24a
 month: 0
-tex_title: "{Learning flow functions of spiking systems}"
-firstpage: 1
-lastpage: 12
-page: 1-12
+tex_title: "Learning flow functions of spiking systems"
+firstpage: 591
+lastpage: 602
+page: 591-602
 order: 1
 cycles: false
 bibtex_author: Aguiar, Miguel and Das, Amritam and Johansson, Karl H.

_posts/2024-06-11-akgul24a.md

@@ -1,5 +1,5 @@
 ---
-title: Continual Learning of Multi-modal Dynamics with External Memory
+title: Continual learning of multi-modal dynamics with external memory
 abstract: We study the problem of fitting a model to a dynamical environment when
   new modes of behavior emerge sequentially. The learning model is aware when a new
   mode appears, but it cannot access the true modes of individual training sequences.
@@ -20,10 +20,10 @@ publisher: PMLR
 issn: 2640-3498
 id: akgul24a
 month: 0
-tex_title: "{Continual Learning of Multi-modal Dynamics with External Memory}"
-firstpage: 1
-lastpage: 12
-page: 1-12
+tex_title: "Continual learning of multi-modal dynamics with external memory"
+firstpage: 40
+lastpage: 51
+page: 40-51
 order: 1
 cycles: false
 bibtex_author: Akg\"{u}l, Abdullah and Unal, Gozde and Kandemir, Melih

_posts/2024-06-11-alboni24a.md

@@ -1,6 +1,6 @@
 ---
-title: 'CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory
-  Optimization'
+title: 'CACTO-SL: Using Sobolev learning to improve continuous actor-critic with trajectory
+  optimization'
 abstract: Trajectory Optimization (TO) and Reinforcement Learning (RL) are powerful
   and complementary tools to solve optimal control problems. On the one hand, TO can
   efficiently compute locally-optimal solutions, but it tends to get stuck in local
@@ -23,11 +23,11 @@ publisher: PMLR
 issn: 2640-3498
 id: alboni24a
 month: 0
-tex_title: "{CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with
-  Trajectory Optimization}"
-firstpage: 1
-lastpage: 12
-page: 1-12
+tex_title: "{CACTO-SL}: {U}sing {S}obolev learning to improve continuous actor-critic with
+  trajectory optimization"
+firstpage: 1452
+lastpage: 1463
+page: 1452-1463
 order: 1
 cycles: false
 bibtex_author: Alboni, Elisa and Grandesso, Gianluigi and Rosati Papini, Gastone Pietro

_posts/2024-06-11-allen-blanchette24a.md

@@ -20,10 +20,10 @@ publisher: PMLR
 issn: 2640-3498
 id: allen-blanchette24a
 month: 0
-tex_title: "{Hamiltonian GAN}"
-firstpage: 1
-lastpage: 13
-page: 1-13
+tex_title: "{H}amiltonian {GAN}"
+firstpage: 1662
+lastpage: 1674
+page: 1662-1674
 order: 1
 cycles: false
 bibtex_author: Allen-Blanchette, Christine

_posts/2024-06-11-alsalti24a.md

@@ -15,11 +15,11 @@ publisher: PMLR
 issn: 2640-3498
 id: alsalti24a
 month: 0
-tex_title: "{An efficient data-based off-policy Q-learning algorithm for optimal output
-  feedback control of linear systems}"
-firstpage: 1
-lastpage: 12
-page: 1-12
+tex_title: "An efficient data-based off-policy {Q}-learning algorithm for optimal output
+  feedback control of linear systems"
+firstpage: 312
+lastpage: 323
+page: 312-323
 order: 1
 cycles: false
 bibtex_author: Alsalti, Mohammad and Lopez, Victor G. and M\"{u}ller, Matthias A.

_posts/2024-06-11-bai24a.md

@@ -1,5 +1,5 @@
 ---
-title: Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off
+title: Mixing classifiers to alleviate the accuracy-robustness trade-off
 abstract: Deep neural classifiers have recently found tremendous success in data-driven
   control systems. However, existing neural models often suffer from a trade-off between
   accuracy and adversarial robustness, which is a limitation that must be overcome
@@ -21,10 +21,10 @@ publisher: PMLR
 issn: 2640-3498
 id: bai24a
 month: 0
-tex_title: "{Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off}"
-firstpage: 1
-lastpage: 14
-page: 1-14
+tex_title: "Mixing classifiers to alleviate the accuracy-robustness trade-off"
+firstpage: 852
+lastpage: 865
+page: 852-865
 order: 1
 cycles: false
 bibtex_author: Bai, Yatong and Anderson, Brendon G. and Sojoudi, Somayeh

_posts/2024-06-11-bai24b.md

@@ -1,6 +1,6 @@
 ---
-title: Finite-Time Complexity of Incremental Policy Gradient Methods for Solving Multi-Task
-  Reinforcement Learning
+title: Finite-time complexity of incremental policy gradient methods for solving multi-task
+  reinforcement learning
 abstract: We consider a multi-task learning problem, where an agent is presented a
   number of $N$ reinforcement learning tasks. To solve this problem, we are interested
   in studying the gradient approach, which iteratively updates an estimate of the
@@ -22,11 +22,11 @@ publisher: PMLR
 issn: 2640-3498
 id: bai24b
 month: 0
-tex_title: "{Finite-Time Complexity of Incremental Policy Gradient Methods for Solving
-  Multi-Task Reinforcement Learning}"
-firstpage: 1
-lastpage: 12
-page: 1-12
+tex_title: "Finite-time complexity of incremental policy gradient methods for solving
+  multi-task reinforcement learning"
+firstpage: 1046
+lastpage: 1057
+page: 1046-1057
 order: 1
 cycles: false
 bibtex_author: Bai, Yitao and Doan, Thinh

_posts/2024-06-11-bajelani24a.md

@@ -1,5 +1,5 @@
 ---
-title: 'From Raw Data to Safety: Reducing Conservatism by Set Expansion'
+title: 'From raw data to safety: Reducing conservatism by set expansion'
 abstract: In response to safety concerns associated with learning-based algorithms,
   safety filters have been proposed as a modular technique. Generally, these filters
   heavily rely on the system’s model, which is contradictory if they are intended
@@ -20,10 +20,10 @@ publisher: PMLR
 issn: 2640-3498
 id: bajelani24a
 month: 0
-tex_title: "{From Raw Data to Safety: Reducing Conservatism by Set Expansion}"
-firstpage: 1
-lastpage: 13
-page: 1-13
+tex_title: "From raw data to safety: {R}educing conservatism by set expansion"
+firstpage: 1305
+lastpage: 1317
+page: 1305-1317
 order: 1
 cycles: false
 bibtex_author: Bajelani, Mohammad and Heusden, Klaske Van

_posts/2024-06-11-barkley24a.md

@@ -1,5 +1,5 @@
 ---
-title: An Investigation of Time Reversal Symmetry in Reinforcement Learning
+title: An investigation of time reversal symmetry in reinforcement learning
 abstract: One of the fundamental challenges associated with reinforcement learning
   (RL) is that collecting sufficient data can be both time-consuming and expensive.
   In this paper, we formalize a concept of time reversal symmetry in a Markov decision
@@ -27,10 +27,10 @@ publisher: PMLR
 issn: 2640-3498
 id: barkley24a
 month: 0
-tex_title: "{An Investigation of Time Reversal Symmetry in Reinforcement Learning}"
-firstpage: 1
-lastpage: 12
-page: 1-12
+tex_title: "An investigation of time reversal symmetry in reinforcement learning"
+firstpage: 68
+lastpage: 79
+page: 68-79
 order: 1
 cycles: false
 bibtex_author: Barkley, Brett and Zhang, Amy and Fridovich-Keil, David

_posts/2024-06-11-bhan24a.md

@@ -1,6 +1,6 @@
 ---
-title: 'PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial Differential
-  Equations'
+title: 'PDE control gym: A benchmark for data-driven boundary control of partial differential
+  equations'
 abstract: Over the last decade, data-driven methods have surged in popularity, emerging
   as valuable tools for control theory. As such, neural network approximations of
   control feedback laws, system dynamics, and even Lyapunov functions have attracted
@@ -23,11 +23,11 @@ publisher: PMLR
 issn: 2640-3498
 id: bhan24a
 month: 0
-tex_title: "{PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial
-  Differential Equations}"
-firstpage: 1
-lastpage: 13
-page: 1-13
+tex_title: "{PDE} control gym: {A} benchmark for data-driven boundary control of partial
+  differential equations"
+firstpage: 1083
+lastpage: 1095
+page: 1083-1095
 order: 1
 cycles: false
 bibtex_author: Bhan, Luke and Bian, Yuexin and Krstic, Miroslav and Shi, Yuanyuan

_posts/2024-06-11-bhardwaj24a.md

@@ -1,6 +1,6 @@
 ---
-title: Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
-abstract: Recent advances in real-world applications of reinforcement learning (RL)
+title: Real-world fluid directed rigid body control via deep reinforcement learning
+abstract: 'Recent advances in real-world applications of reinforcement learning (RL)
   have relied on the ability to accurately simulate systems at scale. However, domains
   such as fluid dynamical systems exhibit complex dynamic phenomena that are hard
   to simulate at high integration rates, limiting the direct application of modern
@@ -14,17 +14,17 @@ abstract: Recent advances in real-world applications of reinforcement learning (
   from this preliminary study and the availability of systems like the Box o’ Flows
   support the way forward for developing systematic RL algorithms that can be generally
   applied to complex, dynamical systems. Supplementary material and videos of experiments
-  are available at https://sites.google.com/view/box-o-flows/home.
+  are available at https://sites.google.com/view/box-o-flows/home.'
 layout: inproceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR
 issn: 2640-3498
 id: bhardwaj24a
 month: 0
-tex_title: "{Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning}"
-firstpage: 1
-lastpage: 14
-page: 1-14
+tex_title: "Real-world fluid directed rigid body control via deep reinforcement learning"
+firstpage: 414
+lastpage: 427
+page: 414-427
 order: 1
 cycles: false
 bibtex_author: Bhardwaj, Mohak and Lampe, Thomas and Neunert, Michael and Romano,

_posts/2024-06-11-bhargav24a.md

@@ -1,6 +1,6 @@
 ---
-title: Submodular Information Selection for Hypothesis Testing with Misclassification
-  Penalties
+title: Submodular information selection for hypothesis testing with misclassification
+  penalties
 abstract: 'We consider the problem of selecting an optimal subset of information sources
   for a hypothesis testing/classification task where the goal is to identify the true
   state of the world from a finite set of hypotheses, based on finite observation
@@ -25,11 +25,11 @@ publisher: PMLR
 issn: 2640-3498
 id: bhargav24a
 month: 0
-tex_title: "{Submodular Information Selection for Hypothesis Testing with Misclassification
-  Penalties}"
-firstpage: 1
-lastpage: 12
-page: 1-12
+tex_title: "Submodular information selection for hypothesis testing with misclassification
+  penalties"
+firstpage: 566
+lastpage: 577
+page: 566-577
 order: 1
 cycles: false
 bibtex_author: Bhargav, Jayanth and Ghasemi, Mahsa and Sundaram, Shreyas

_posts/2024-06-11-brancato24a.md

@@ -21,10 +21,10 @@ publisher: PMLR
 issn: 2640-3498
 id: brancato24a
 month: 0
-tex_title: "{In vivo learning-based control of microbial populations density in bioreactors}"
-firstpage: 1
-lastpage: 13
-page: 1-13
+tex_title: "In vivo learning-based control of microbial populations density in bioreactors"
+firstpage: 941
+lastpage: 953
+page: 941-953
 order: 1
 cycles: false
 bibtex_author: Brancato, Sara Maria and Salzano, Davide and Lellis, Francesco De and

_posts/2024-06-11-brindise24a.md

@@ -1,6 +1,6 @@
 ---
-title: Pointwise-in-Time Diagnostics for Reinforcement Learning During Training and
-  Runtime
+title: Pointwise-in-time diagnostics for reinforcement learning during training and
+  runtime
 abstract: Explainable AI Planning (XAIP), a subfield of xAI, offers a variety of methods
   to interpret the behavior of autonomous systems. A recent “pointwise-in-time” explanation
   method, called Rule Status Assessment (RSA), characterizes an agent’s behavior at
@@ -18,11 +18,11 @@ publisher: PMLR
 issn: 2640-3498
 id: brindise24a
 month: 0
-tex_title: "{Pointwise-in-Time Diagnostics for Reinforcement Learning During Training
-  and Runtime}"
-firstpage: 1
-lastpage: 13
-page: 1-13
+tex_title: "Pointwise-in-time diagnostics for reinforcement learning during training
+  and runtime"
+firstpage: 694
+lastpage: 706
+page: 694-706
 order: 1
 cycles: false
 bibtex_author: Brindise, Noel and Moreno, Andres Posada and Langbort, Cedric and Trimpe,

_posts/2024-06-11-brumali24a.md

@@ -1,5 +1,5 @@
 ---
-title: A Deep Learning Approach for Distributed Aggregative Optimization with Users’
+title: A deep learning approach for distributed aggregative optimization with users’
   Feedback
 abstract: We propose a novel distributed data-driven scheme for online aggregative
   optimization, i.e., the framework in which agents in a network aim to cooperatively
@@ -21,11 +21,11 @@ publisher: PMLR
 issn: 2640-3498
 id: brumali24a
 month: 0
-tex_title: "{A Deep Learning Approach for Distributed Aggregative Optimization with
-  Users’ Feedback}"
-firstpage: 1
-lastpage: 13
-page: 1-13
+tex_title: "A deep learning approach for distributed aggregative optimization with
+  users’ feedback"
+firstpage: 1552
+lastpage: 1564
+page: 1552-1564
 order: 1
 cycles: false
 bibtex_author: Brumali, Riccardo and Carnevale, Guido and Notarstefano, Giuseppe