Can reinforcement learning model learning across development? Online lifelong learning through adaptive intrinsic motivation by Sandbrink, K, Christian, B, Nasvytis, L, Schroeder de Witt, C, Butlin, P

QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning by Rashid, T, Samvelyan, M, Schroeder de Witt, C, Farquhar, G, Foerster, J, Whiteson, S

Monotonic value function factorisation for deep multi-agent reinforcement learning by Rashid, T, Samvelyan, M, Schröder de Witt, C, Farquhar, G, Foerster, JN, Whiteson, S

Multi-agent common knowledge reinforcement learning by de Witt, C, Foerster, J, Farquhar, G, Torr, PHS, Boehmer, W, Whiteson, S

Secret collusion among AI agents: multi-agent deception via steganography by Motwani, SR, Baranchuk, M, Strohmeier, M, Bolina, V, Torr, PHS, Hammond, L, Schroeder de Witt, C

Exploring a New Population of Compact Objects: X-ray and IR Observations of the Galactic Centre by Bandyopadhyay, R, Gosling, A, Eikenberry, SE, Muno, M, Blundell, K, Podsiadlowski, P, Mikles, V, DeWitt, C

Constraining the global composition of D/H and 18O/16O in Martian water using SOFIA/EXES by Alday, J, Aoki, S, DeWitt, C, Montmessin, F, Holmes, JA, Patel, MR, Mason, JP, Encrenaz, T, Richter, MJ, Daerden, F, Terada, N, Irwin, PGJ, Nakagawa, H

Constraining the global composition of D/H and 18O/16O in Martian water from SOFIA/EXES by Alday, J, Aoki, S, DeWitt, C, Montmessin, F, Holmes, J, Patel, M, Mason, J, Encrenaz, T, Richter, M, Irwin, P, Daerden, F, Terada, N, Nakagawa, H

Amortized rejection sampling in universal probabilistic programming by Naderiparizi, S, Scibior, A, Munk, A, Ghadiri, M, Güneş Baydin, A, Gram-Hansen, B, Schroeder de Witt, C, Zinkov, R, Torr, PHS, Rainforth, T, Teh, YW, Wood, F

Simulation-based inference for global health decisions by Schroeder de Witt, C, Gram-Hansen, B, Nardelli, N, Gambardella, A, Zinkov, R, Dokania, P, Siddharth, N, Espinosa-Gonzalez, AB, Darzi, A, Torr, PHS, Güneş Baydin, A

Position: near to mid-term risks and opportunities of open-source generative AI by Eiras, F, Petrov, A, Vidgen, B, Schroeder de Witt, C, Pizzati, F, Elkins, K, Mukhopadhyay, S, Bibi, A, Csaba, B, Steibel, F, Barez, F, Smith, G, Guadagni, G, Chun, J, Cabot, J, Imperial, JM, Nolazco-Flores, JA, Landay, L, Jackson, M, Röttger, P, Torr, PHS, Darrell, T, Lee, YS, Foerster, J

Risks and opportunities of open-source generative AI by Eiras, F, Petrov, A, Vidgen, B, Schroeder de Witt, C, Pizzati, F, Elkins, K, Mukhopadhyay, S, Bibi, A, Purewal, A, Csaba, B, Steibel, F, Keshtkar, F, Barez, F, Smith, G, Guadagni, G, Chun, J, Cabot, J, Imperial, JM, Nolazco-Flores, JA, Landay, L, Jackson, M, Torr, PHS, Darrell, T, Lee, YS, Foerster, J