Publications

Institute Publications

Peer-Reviewed Journals

Ding, Z., Tran, D., Ponder, K., Ding, Z., Froebe, R., Ntanavara, L., Fahey, P. G., Cobos, E., Baroni, L., Diamantaki, M., Wang, E. Y., Chang, A., Papadopoulos, S., Fu, J., Muhammad, T., Papadopoulos, C., Cadena, S. A., Evangelou, A., Willeke, K., … Tolias, A. S. (2026). Functional bipartite invariance in mouse primary visual cortex receptive fields. Nature Neuroscience, 29(4), 851–863. https://doi.org/10.1038/s41593-026-02213-3

Jeon, Y.-N., Cho, H. Y. H., Green, A. C., & Issa, E. B. (2025). Comparison of human and marmoset basic-level face categorization based on shape. Scientific Reports, 16(1), 1785. https://doi.org/10.1038/s41598-025-31437-9

Mao, J., Rothkopf, C. A., & Stocker, A. A. (2025). Adaptation optimizes sensory encoding for future stimuli. PLOS Computational Biology, 21(1), e1012746. https://doi.org/10.1371/journal.pcbi.1012746

Noor, D. F., Chowdhury, M. R., & Sikder, S. (2026). IllumiSIFT: A Cascade Framework for DoG Pyramid Learning in Darkness. Sensors, 26(7), 2147. https://doi.org/10.3390/s26072147

Siam, M. A., Noor, D. F., Ndoye, M., & Khan, J. F. (2025). Advancing SAR Target Recognition Through Hierarchical Self-Supervised Learning with Multi-Task Pretext Training. Sensors, 26(1), 122. https://doi.org/10.3390/s26010122

Triplett, M. A., Bäumler, E., Prodan, A., Stonis, R., Peterka, D. S., Häusser, M., & Paninski, L. (2026). Computational optimization of two-photon holographic stimulation sites in vivo. Journal of Neural Engineering, 23(2), 026015. https://doi.org/10.1088/1741-2552/ae4925

Ye, Z., Shelton, A. M., Shaker, J. R., Boussard, J., Colonell, J., Birman, D., Manavi, S., Chen, S., Windolf, C., Hurwitz, C., Yu, H., Namima, T., Pedraja, F., Weiss, S., Raducanu, B. C., Ness, T. V., Jia, X., Mastroberardino, G., Rossi, L. F., … Steinmetz, N. A. (2025). Ultra-high-density Neuropixels probes improve detection and identification in neuronal recordings. Neuron, 113(23), 3966-3982.e12. https://doi.org/10.1016/j.neuron.2025.08.030

Zhang, Yizi, et al. “Exploiting Correlations across Trials and Behavioral Sessions to Improve Neural Decoding.” 15 Sept. 2024. Neuroscience, https://doi.org/10.1101/2024.09.14.613047

Zolfaghari, H., Ebrahimi, N., Pitkow, X., & Davoodi, M. (2025). Actuator Selection and Control of an Array of Electromagnetic Soft Actuators. Electronics, 14(18), 3682. https://doi.org/10.3390/electronics14183682

Zolfaghari, H., Ebrahimi, N., Ji, Y., Pitkow, X., & Davoodi, M. (2025). Integrated Analytical Modeling and Numerical Simulation Framework for Design Optimization of Electromagnetic Soft Actuators. Actuators, 14(3), 128. https://doi.org/10.3390/act14030128

Peer-Reviewed Conference Publications

Altabaa, A., & Lafferty, J. (2024, September). Learning Hierarchical Relational Representations through Relational Convolutions. TMLR 2024. https://openreview.net/forum?id=vNZlnznmV2
Altabaa, A., Webb, T., Cohen, J., & Lafferty, J. (2023). Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers. ICLR 2024. https://doi.org/10.48550/ARXIV.2304.00195
Altabaa, Awni, and John Lafferty. “Disentangling and Integrating Relational and Sensory Information in Transformer Architectures.” 2024, ICML 2025. https://openreview.net/forum?id=lbrqeIipJr
Altabaa, A., Montasser, O., & Lafferty, J. (2025). CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision. NeurIPS 2025 Spotlight. https://openreview.net/forum?id=OkVQJZWGfn
Ananthram, A., Stengel-Eskin, E., Bradford, L. A., Demarest, J., Purvis, A., Krut, K., Stein, R., Pantalony, R. E., Bansal, M., & McKeown, K. (2026). PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions. ICLR 2026 Poster. https://openreview.net/forum?id=UBhY1c4r2W&referrer=%5Bthe%20profile%20of%20Keith%20Krut%5D(%2Fprofile%3Fid%3D~Keith_Krut1)
Ananthram, A., Stengel-Eskin, E., Vondrick, C., Bansal, M., & McKeown, K. (2025). See It from My Perspective: How Language Affects Cultural Bias in Image Understanding [ICLR Poster]. https://iclr.cc/virtual/2025/poster/29299
Azabou, M., Pan, K. X., Arora, V., Knight, I. J., Dyer, E. L., & Richards, B. A. (2025). Multi-session, multi-task neural decoding from distinct cell-types and brain regions. ICRL 2025. https://openreview.net/forum?id=IuU0wcO0mo
Bae, S., Azabou, M., Richards, B., & Cha, J. (2026). Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining. ICLR 2026 Poster. https://openreview.net/forum?id=z9kAjjRejs
Beiran, M., Litwin-Kumar, A., Abbott, L., & Turaga, S. (2026). Predicting neural responses to perturbations using connectome-constrained networks. COSYNE 2026. Poster. https://pmc.ncbi.nlm.nih.gov/articles/PMC12648571/
Bencomo, G., Gupta, M., Marinescu, I., McCoy, R., & Griffiths, T. (2025). Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks. Proceedings of the Annual Meeting of the Cognitive Science Society. 47. https://escholarship.org/uc/item/7kh8q92h
Bojic, I., Ong, Q. C., Ma, S. H. X., Ai, L., Liu, Z., Gong, Z., Hirschberg, J., Ho, A. H. Y., & Khong, A. W. H. (2025). SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes. Findings of the Association for Computational Linguistics: EMNLP 2025, 16288–16305. https://doi.org/10.18653/v1/2025.findings-emnlp.885
Cai, D., Modi, C., Margossian, C., Gower, R., Blei, D., & Saul, L. (2024). EigenVI: Score-based variational inference with orthogonal function expansions. Advances in Neural Information Processing Systems 37, 132691–132721. https://doi.org/10.52202/079017-4218
Cai, D., Modi, C., Pillaud-Vivien, L., Margossian, C. C., Gower, R. M., Blei, D. M., & Saul, L. K. (2024). Batch and match: Black-box variational inference with a score-based divergence. ICML’24: Proceedings of the 41st International Conference on Machine Learning, 205, 5258–5297. https://dl.acm.org/doi/10.5555/3692070.3692275
Cai, Y., Wu, C., Ma, B., Chen, B., Xue, Y., Hirschberg, J., & Gong, Z. (2026). SURE: Synergistic Uncertainty-Aware Reasoning for Multimodal Emotion Recognition in Conversations. ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 11832–11836. https://doi.org/10.1109/ICASSP55912.2026.11464547
Chen, R., Liang, W., Gong, Z., Ai, L., & Hirschberg, J. (2026). Detecting Mental Manipulation in Speech via Synthetic Multi-Speaker Dialogue. In G. Riccardi, S. M. Mousavi, M. I. Torres, K. Yoshino, Z. Callejas, S. A. Chowdhury, Y.-N. Chen, F. Bechet, J. Gustafson, G. Damnati, A. Papangelis, L. F. D’Haro, J. Mendonça, R. Bernardi, D. Hakkani-Tur, G. ”Pino” Di Fabbrizio, T. Kawahara, F. Alam, G. Tur, & M. Johnston (Eds.), Proceedings of the 16th International Workshop on Spoken Dialogue System Technology (pp. 428–440). Association for Computational Linguistics. https://aclanthology.org/2026.iwsds-1.41/
Chiquier, M., Mall, U., & Vondrick, C. (2025). Evolving Interpretable Visual Classifiers with Large Language Models. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024 (Vol. 15122, pp. 183–201). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73039-9_11
Deng, Z., Zollo, T., Eyre, B., Inamdar, A., Madras, D., & Zemel, R. (2025). QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. International Conference on Machine Learning. https://icml.cc/virtual/2025/poster/45665
Dern, N., Cunningham, J. P., & Pleiss, G. (2024). Theoretical Limitations of Ensembles in the Age of Overparameterization. ICRL 2024. https://openreview.net/forum?id=Cf0N07E1vu
Eyre, B., Creager, E., Madras, D., Papyan, V., & Zemel, R. (2024). Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. Proceedings of the 41st International Conference on Machine Learning, 12701–12722. https://proceedings.mlr.press/v235/eyre24a.html
Eyre, B., & Madras, D. (2025). Regression for the Mean: Auto-Evaluation and Inference with Few Labels through Post-hoc Regression. ICML 2025. https://openreview.net/forum?id=kRKsUOJdp5
Goddard, C., Smith, L. M., Ngampruetikorn, V., & Schwab, D. J. (2025). When can in-context learning generalize out of task distribution? ICML 2025. https://arxiv.org/abs/2506.05574
Gong, Z., Shi, P., Donbekci, K., Ai, L., Chen, R., Sasu, D., Wu, Z., & Hirschberg, J. (2025). Learning More with Less: Self-Supervised Approaches forLow-Resource Speech Emotion Recognition. Interspeech 2025, 151–155. https://doi.org/10.21437/Interspeech.2025-1006
Hall, Z., Subbiah, M., Zollo, T. P., McKeown, K., & Zemel, R. (2025). Guiding LLM Decision-Making with Fairness Reward Models. NeurIPS 2025. https://arxiv.org/abs/2507.11344
He, L., Zhong, T., Antonello, R., Mischler, G., Goldblum, M., & Mesgarani, N. (2025). Far from the Shallow: Brain-Predictive Reasoning Embedding through Residual Disentanglement. NeurIPS 2025. https://openreview.net/forum?id=tPqBnGwTwa&referrer=%5Bthe%20profile%20of%20Linyang%20He%5D%28%2Fprofile%3Fid%3D~Linyang_He2%29
Inamdar, A., Tang, Z., Anderson, A., & Zemel, R. (2026). Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning. ICLR 2026. https://openreview.net/forum?id=EFxYTuNmAq
Keller, R., Kirsch, A., Pei, F., Pitkow, X., Kozachkov, L., & Nayebi, A. (2025). Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamics. NeurIPS 2025. https://openreview.net/forum?id=g2vViuEVDS
Jesson, A., Beltran-Velez, N., & Blei, D. (2024). Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective. ICLR 2025. https://openreview.net/forum?id=bcynT7s2du
Jesson, A., Beltran-Velez, N., Chu, Q., Karlekar, S., Kossen, J., Gal, Y., Cunningham, J. P., & Blei, D. (2024). Estimating the Hallucination Rate of Generative AI. Advances in Neural Information Processing Systems, 37, 31154–31201. https://doi.org/10.52202/079017-0982
Liang, J., Liu, R., Ozguroglu, E., Sudhakar, S., Dave, A., Tokmakov, P., Song, S., & Vondrick, C. (2025). Dreamitate: Real-World Visuomotor Policy Learning via Video Generation. Proceedings of The 8th Conference on Robot Learning, 3943–3960. https://proceedings.mlr.press/v270/liang25b.html
Limpijankit, M., Chen, Y., Subbiah, M., Deas, N., & McKeown, K. (2025). Counterfactual Simulatability of LLM Explanations for Generation Tasks. In L. Flek, S. Narayan, L. H. Phương, & J. Pei (Eds.), Proceedings of the 18th International Natural Language Generation Conference (pp. 659–683). Association for Computational Linguistics. https://aclanthology.org/2025.inlg-main.38/
Lin, X., Li, M., Zemel, R., Ji, H., & Chang, S.-F. (2024). Training-free Deep Concept Injection Enables Language Models for Video Question Answering. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 22399–22416. https://doi.org/10.18653/v1/2024.emnlp-main.1249
Maus, N., Kim, K., Pleiss, G., Eriksson, D., Cunningham, J., & Gardner, J. (2024). Approximation-Aware Bayesian Optimization. Advances in Neural Information Processing Systems 37, 21114–21140. https://doi.org/10.52202/079017-0665
Menon, S., Zemel, R., & Vondrick, C. (2024). Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 20016–20031. https://doi.org/10.18653/v1/2024.emnlp-main.1117
Morrill, T., Puli, A., Megjhani, M., Park, S., & Zemel, R. (2026). Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads. Proceedings of the Fifth Machine Learning for Health Symposium, 697–720. https://proceedings.mlr.press/v297/morrill26a.html
Nazaret, A., & Blei, D. (2024). Extremely Greedy Equivalence Search. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, 2716–2745. https://proceedings.mlr.press/v244/nazaret24a.html
Nguyen, A., Schwab, D., & Ngampruetikorn, V. (2026). Generalization vs Specialization under Concept Shift. Advances in Neural Information Processing Systems, 38, 59446–59466. https://proceedings.neurips.cc/paper_files/paper/2025/hash/55a887089077bcf644d97df25b050317-Abstract-Conference.html
Ozguroglu, E., Liu, R., Surís, D., Chen, D., Dave, A., Tokmakov, P., & Vondrick, C. (2024). pix2gestalt: Amodal Segmentation by Synthesizing Wholes. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3931–3940. https://doi.org/10.1109/CVPR52733.2024.00377
Rajaneesh, N., Zollo, T., & Zemel, R. (2025). Test-Time Warmup for Multimodal Large Language Models. ICML 2025. https://openreview.net/forum?id=iCYbIaGKSR
Ramesh, R., Bisulco, A., DiTullio, R. W., Wei, L., Balasubramanian, V., Daniilidis, K., & Chaudhari, P. (2024). Many Perception Tasks are Highly Redundant Functions of their Input Data. COSYNE 2025. https://www.cosyne.org/past-conferences
Rooke, S., Wang, Z., Tullio, R. D., & Balasubramanian, V. (2024). Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes. Advances in Neural Information Processing Systems 37, 96892–96923. https://doi.org/10.52202/079017-3071
Sasu, D., Wu, Z., Gong, Z., Chen, R., Shi, P., Ai, L., Hirschberg, J., & Schluter, N. (2025). Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues. Findings of the Association for Computational Linguistics: ACL 2025, 9820–9831. https://doi.org/10.18653/v1/2025.findings-acl.510
Siam, M., Noor, D. F., & Ndoye, M. (2026). Layer-Wise Feature Analysis for Self-Supervised Sar Target Recognition: Identifying Optimal Representations Across Data Regimes. SoutheastCon 2026, 1–7. https://doi.org/10.1109/SoutheastCon63549.2026.11475932
Shan, H., & Litwin-Kumar, A. (2026). Graph embeddings for identifying symmetries in connectomes. COYSNE 2026 Poster. https://doi.org/10.64898/2025.12.06.692615
Shan, H., Minni, S., & Duncker, L. (2025). Separating the what and how of compositional computation to enable reuse and continual learning. NeurIPS 2025. https://openreview.net/forum?id=Wg9gAqjAHb
Shi, C., Beltran-Velez, N., Nazaret, A., Zheng, C., Garriga-Alonso, A., Jesson, A., Makar, M., & Blei, D. M. (2024). Hypothesis Testing the Circuit Hypothesis in LLMs. arXiv. https://doi.org/10.48550/ARXIV.2410.13032
Siam, M. A., & Noor, D. F. (2025). Self-Supervised Learning for SAR Target Recognition with Multi-Task Pretext Training. SoutheastCon 2025, 1207–1213. https://doi.org/10.1109/SoutheastCon56624.2025.10971440
Slavutsky, Y., & Blei, D. M. (2025). Quantifying Uncertainty in the Presence of Distribution Shifts. NeurIPS 2025. https://openreview.net/forum?id=04p7u1gIsv
Subbiah, M., Mian, H., Deas, N., Mayukha, A., McAdams, D. P., & McKeown, K. (2026). Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives (arXiv:2604.20131). arXiv. https://doi.org/10.48550/arXiv.2604.20131
Subbiah, M., Mishra, A., Kim, G., Tang, L., Durrett, G., & McKeown, K. (2025). Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 185–203. https://doi.org/10.18653/v1/2025.emnlp-main.10
Tatzel, L., Wenger, J., Schneider, F., & Hennig, P. (2025). Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty. TMLR 2025. https://arxiv.org/abs/2310.20285
Toosi, T. (2025, September 30). Interpretability at the Network Level: Prior-Guided Drift Diffusion for Neural Circuit Analysis. Mechanistic Interpretability Workshop at NeurIPS 2025. https://openreview.net/forum?id=vL28JhJDEM
Toosi, T., & Miller, K. D. (2025). Natural Scene Coding Consistency in Genetically-Defined Cell Populations. COSYNE 2026 Poster. https://doi.org/10.1101/2025.10.29.685363
Toosi, T., & Miller, K. D. (2025c, September 23). Unifying Gestalt Principles Through Inference-Time Prior Integration. First Workshop on CogInterp: Interpreting Cognition in Deep Learning Models. https://openreview.net/forum?id=WksqQxYZXs
Toosi, T., & Miller, K. (2026). Feedback-Mediated Prior Integration Unifies Gestalt Perceptual Organization. Vision Sciences Society 2026 Talk Session. https://www.visionsciences.org/talk-sessions/?id=725
Tyulina, N., Emmanouil, T. A., & Levitan, S. I. (2024). Understanding Linguistic and Visual Factors that Affect Human Trust Perception of Virtual Agents. ACM Conversational User Interfaces 2024, 1–6. https://doi.org/10.1145/3640794.3665581
Tyulina, N., Yu, Y., Emmanouil, T. A., & Levitan, S. I. (2025). Beyond Face Value: Visual and Auditory Signals in Human and Machine Trust Judgments. Proceedings of the 7th ACM Conference on Conversational User Interfaces, 1–5. https://doi.org/10.1145/3719160.3737630
Wang, J., Zollo, T., Zemel, R., & Namkoong, H. (2025, July 9). Adaptive Elicitation of Latent Information Using Natural Language. ICML 2025. ICML 2025. https://openreview.net/forum?id=I7N6vtUChM
Wang, Y., Yu, H., Blau, A., Zhang, Y., Laboratory, T. I. B., Paninski, L., Hurwitz, C., & Whiteway, M. (2026). Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining. ICLR 2026. https://openreview.net/forum?id=AeqPIRKUni
Wang, Z., Di Tullio, R. W., Rooke, S., & Balasubramanian, V. (2024, August 12). Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences. NeurIPS 2024. NeurIPS 2024. https://doi.org/10.1101/2024.08.11.607484
Wang, Z., Morris, G., Derdikman, D., Chaudhari, P., & Balasubramanian, V. (2025, July 3). REMI: Reconstructing Episodic Memory During Internally Driven Path Planning. NeurIPS 2025. https://doi.org/10.1101/2025.07.02.662824
Wenger, J., Coker, B., Marusic, J., & Cunningham, J. P. (2025). Variational Deep Learning via Implicit Regularization. NeurIPS 2025. https://openreview.net/forum?id=ZlzZaFgjgo
Wenger, J., Wu, K., Hennig, P., Gardner, J. R., Pleiss, G., & Cunningham, J. P. (2024). Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. NeurIPS 2024. https://openreview.net/forum?id=tDvFa5OJyS
Willeke, K. F., Turishcheva, P., Gilbert, A., Chakrabarty, G., Bedel, H. A., Fahey, P. G., Qiu, Y., Weis, M. A., Vystrčilová, M., Muhammad, T., Ntanavara, L., Froebe, R. E., Ponder, K., Tan, Z. H., Orhan, E., Cobos, E., Sanborn, S., Franke, K., Sinz, F. H., Tolias, A. S. (2025, October 8). OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens. The Fourteenth International Conference on Learning Representations (ICLR 2026). Poster. https://openreview.net/forum?id=mEw4lhAn0F
Wu, C., Cai, Y., Liu, Y., Zhu, P., Xue, Y., Gong, Z., Hirschberg, J., & Ma, B. (2025). Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects. Findings of the Association for Computational Linguistics: EMNLP 2025, 6257–6274. https://doi.org/10.18653/v1/2025.findings-emnlp.332
Wu, K., Wenger, J., Jones, H. T., Pleiss, G., & Gardner, J. (2024). Large-Scale Gaussian Processes via Alternating Projection. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 2620–2628. https://proceedings.mlr.press/v238/wu24d.html
Xia, J., Zhang, Y., Wang, S., Allen, G., Paninski, L., Hurwitz, C., & Miller, K. (2026). Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets. Advances in Neural Information Processing Systems, 38, 79040–79072. https://proceedings.neurips.cc/paper_files/paper/2025/hash/7195b4ffa1803de9eb34447032f94234-Abstract-Conference.html
Xu, K., Zhang, L., & Shi, J. (2025a). Detecting Origin Attribution for Text-to-Image Diffusion Models. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025. https://openreview.net/forum?id=pkp0CdLHLz
Xu, K., Zhang, L., & Shi, J. (2025b). Detecting Origin Attribution for Text-to-Image Diffusion Models. 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 8775–8785. https://doi.org/10.1109/WACV61041.2025.00850
Xu, K., Zhang, L., & Shi, J. (2025c). Good Seed Makes a Good Crop: Discovering Secret Seeds in Text-to-Image Diffusion Models. 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3024–3034. https://doi.org/10.1109/WACV61041.2025.00299
Yang, H., Gee, J., & Shi, J. (2024). Brain Decodes Deep Nets. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 23030–23040. https://doi.org/10.1109/CVPR52733.2024.0217
Yang, H., Xu, K., Lu, A., Grossberg, M. D., Bai, Y., & Shi, J. (2026). Vibe Spaces for Creatively Connecting and Expressing Visual Concepts. 21912–21921. https://openaccess.thecvf.com/content/CVPR2026/html/Yang_Vibe_Spaces_for_Creatively_Connecting_and_Expressing_Visual_Concepts_CVPR_2026_paper.html
Yu, H., Lyu, H., Xu, E. Y., Windolf, C., Lee, E. K., Yang, F., Shelton, A. M., Olsen, S., Minavi, S., Winter, O., The International Brain Laboratory, Dyer, E. L., Chandrasekaran, C., Steinmetz, N. A., Paninski, L., & Hurwitz, C. (2024, November 5). In vivo cell-type and brain region classification via multimodal contrastive learning. ICLR 2025. https://doi.org/10.1101/2024.11.05.622159
Zhang, Y., Charlin, L., Zemel, R., & Ren, M. (2025). Integrating Present and Past in Unsupervised Continual Learning. Proceedings of The 3rd Conference on Lifelong Learning Agents, 388–409. https://proceedings.mlr.press/v274/zhang25a.html
Zhang, Y., McKeown, K., & Muresan, S. (2025). Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 25647–25660. https://doi.org/10.18653/v1/2025.emnlp-main.1301
Zhang, Y., Wang, Y., Azabou, M., Andre, A., Wang, Z., Lyu, H., Laboratory, T. B. I., Dyer, E., Paninski, L., & Hurwitz, C. (2025). Neural Encoding and Decoding at Scale. ICLM 2025 Spotlight. https://icml.cc/virtual/2025/poster/43679
Zhang, Y., Wang, Y., Jimenez-Beneto, D., Wang, Z., Azabou, M., Richards, B., Winter, O., Laboratory, I. B., Dyer, E., Paninski, L., & Hurwitz, C. (2024, July 23). Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution. NeurIPS 2024. https://openreview.net/forum?id=nRRJsDahEg
Zollo, T. P., Deng, Z., Snell, J. C., Pitassi, T., & Zemel, R. (2024). Improving Predictor Reliability with Selective Recalibration. TMLR 2024. https://openreview.net/forum?id=Aoj9H6jl6F
Zollo, T. P., Siah, A. W. T., Ye, N., Li, A., & Namkoong, H. (2025). PersonalLLM: Tailoring LLMs to Individual Preferences. ICLR 2025. https://openreview.net/forum?id=2R7498e2Tx
Zollo, T. P., & Zemel, R. (2026). Confidence Calibration in Vision-Language-Action Models. ICML 2026. https://openreview.net/forum?id=P1Ah9s26td
Zollo, T., Rajaneesh, N., Zemel, R., Gillis, T., & Black, E. (2025). Towards Effective Discrimination Testing for Generative AI. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 1028–1047. https://doi.org/10.1145/3715275.3732067